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--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/triton/compiler/utils.py
@@ -0,0 +1,304 @@
+# Copyright (c) 2023 NVIDIA Corporation & Affiliates. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files
+# (the "Software"), to deal in the Software without restriction,
+# including without limitation the rights to use, copy, modify, merge,
+# publish, distribute, sublicense, and/or sell copies of the Software,
+# and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from __future__ import annotations
+
+from ..runtime import driver
+
+
+def generate_cu_signature(constants, signature, ids):
+    # CUtensorMap*s are always the last arguments
+    num_regular_signatures = max(signature.keys()) + 1 if len(signature) > 0 else 0
+    if ids["ids_of_tensormaps"] is not None:
+        for i, _ in enumerate(ids["ids_of_tensormaps"]):
+            signature[num_regular_signatures + i] = '*CUtensorMap'
+    return signature, num_regular_signatures
+
+
+def dummy_tensormaps_info(n=2):
+    ret = []
+    for i in range(n):
+        ret.append(InfoFromBackendForTensorMap(dummy=True))
+    return ret
+
+
+def parse_tma_info(infos, ids_of_folded_args):
+    ret = []
+    for info in infos:
+        e = InfoFromBackendForTensorMap(infos=info)
+        e.ids_of_folded_args = ids_of_folded_args
+        ret.append(e)
+    return ret
+
+
+def get_tma_mapping(tensormaps_info):
+    ret = {}
+    if tensormaps_info is not None:
+        for i, e in enumerate(tensormaps_info):
+            ret.update(e.get_address_tma_mapping())
+    else:
+        ret = None
+    return ret
+
+
+def get_ids_of_tensormaps(tensormaps_info):
+    ret = None
+    # order is not relevant
+    if tensormaps_info is not None:
+        ret = [e.get_id_of_tensormap() for e in tensormaps_info]
+    return ret
+
+
+# decouple information for tensormap from backend
+# please ignore the naming style, xx_yy is compiler.py style, xxYy is to comply with cuda tensormap style
+# mixing style is for readability
+class InfoFromBackendForTensorMap:
+    N = 2
+    n = 0
+    ntma = 0
+
+    def __init__(self, infos=None, dummy=False):
+        self.dummy = dummy
+        self.ids_of_folded_args = ()
+        if not dummy and not isinstance(infos, dict):
+            self._extract_info_from_backend(infos)
+        elif not dummy and isinstance(infos, dict):
+            self._extract_info_from_dict(infos)
+        elif dummy:
+            self._dummy()
+
+    def _dummy(self):
+        assert InfoFromBackendForTensorMap.n < InfoFromBackendForTensorMap.N
+        if InfoFromBackendForTensorMap.n == 0:
+            self.tensorDataType = driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT16"]
+            self.tensorRank = 4
+            self.globalAddressArgIdx = 0
+            self.globalStridesArgIdx = [7, 6, -1, -1]
+            self.globalDimsArgIdx = [5, 3, -1, -1]
+            self.boxDims = [16, 64, 1, 1]
+            self.elementStrides = [1, 1, 1, 1]
+            self.interleave = driver.utils.CUtensorMapInterleave["CU_TENSOR_MAP_INTERLEAVE_NONE"]
+            self.swizzle = driver.utils.CUtensorMapSwizzle["CU_TENSOR_MAP_SWIZZLE_32B"]
+            self.l2Promotion = driver.utils.CUtensorMapL2promotion["CU_TENSOR_MAP_L2_PROMOTION_L2_128B"]
+            self.TMADescArgIdx = 11
+            self.oobFill = driver.utils.CUtensorMapFloatOOBfill["CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE"]
+            InfoFromBackendForTensorMap.n += 1
+            return
+        if InfoFromBackendForTensorMap.n == 1:
+            self.tensorDataType = driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT16"]
+            self.tensorRank = 4
+            self.globalAddressArgIdx = 1
+            self.globalStridesArgIdx = [7, 6, -1, -1]
+            self.globalDimsArgIdx = [5, 3, -1, -1]
+            self.boxDims = [16, 64, 1, 1]
+            self.elementStrides = [1, 1, 1, 1]
+            self.interleave = driver.utils.CUtensorMapInterleave["CU_TENSOR_MAP_INTERLEAVE_NONE"]
+            self.swizzle = driver.utils.CUtensorMapSwizzle["CU_TENSOR_MAP_SWIZZLE_32B"]
+            self.l2Promotion = driver.utils.CUtensorMapL2promotion["CU_TENSOR_MAP_L2_PROMOTION_L2_128B"]
+            self.TMADescArgIdx = 12
+            self.oobFill = driver.utils.CUtensorMapFloatOOBfill["CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE"]
+            InfoFromBackendForTensorMap.n += 1
+            return
+
+    def _extract_info_from_backend(self, infos):
+        self.tensorDataType = infos.tensorDataType
+        self.tensorRank = infos.tensorRank
+        self.globalAddressArgIdx = infos.globalAddressArgIdx
+        self.globalStridesArgIdx = infos.globalStridesArgIdx
+        self.globalDimsArgIdx = infos.globalDimsArgIdx
+        self.boxDims = infos.boxDims
+        self.elementStrides = infos.elementStrides
+        self.interleave = infos.interleave
+        self.swizzle = infos.swizzle
+        self.l2Promotion = infos.l2Promotion
+        self.oobFill = infos.oobFill
+        self.TMADescArgIdx = infos.TMADescArgIdx
+
+    # dict could be from cached metadata json
+    def _extract_info_from_dict(self, infos: dict):
+        self.tensorDataType = infos['tensorDataType']
+        self.tensorRank = infos['tensorRank']
+        self.globalAddressArgIdx = infos['globalAddressArgIdx']
+        self.globalStridesArgIdx = infos['globalStridesArgIdx']
+        self.globalDimsArgIdx = infos['globalDimsArgIdx']
+        self.boxDims = infos['boxDims']
+        self.elementStrides = infos['elementStrides']
+        self.interleave = infos['interleave']
+        self.swizzle = infos['swizzle']
+        self.l2Promotion = infos['l2Promotion']
+        self.oobFill = infos['oobFill']
+        self.TMADescArgIdx = infos['TMADescArgIdx']
+
+    def get_address_tma_mapping(self):
+        return {self.globalAddressArgIdx: self.TMADescArgIdx + len(self.ids_of_folded_args)}
+
+    def get_id_of_tensormap(self):
+        return self.TMADescArgIdx + len(self.ids_of_folded_args)
+
+    def getTMADescArgIdx(self):
+        return self.TMADescArgIdx
+
+    # dtype:cuda.CUtensorMapDataType | int
+    def bytes_from_type(self, dtype):
+        return {
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT8"]: 1,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT16"]: 2,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT32"]: 4,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_INT32"]: 4,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT64"]: 8,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_INT64"]: 8,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT16"]: 2,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT32"]: 4,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT64"]: 8,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_BFLOAT16"]: 2,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ"]: 4,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_TFLOAT32"]: 4,
+            driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ"]: 4
+        }[dtype]
+
+    def getTensorMapDataType(self):
+        return self.tensorDataType
+
+    def getInterleave(self):
+        return self.interleave
+
+    def getSwizzle(self):
+        return self.swizzle
+
+    def getL2Promotion(self):
+        return self.l2Promotion
+
+    def getOobFill(self):
+        return self.oobFill
+
+    def getTensorRank(self):
+        return self.tensorRank
+
+    def getBoxDims(self):
+        return self.boxDims
+
+    def getElementStrides(self):
+        return self.elementStrides
+
+    def getGlobalAddress(self, args):
+        idx = self.getOriginArgIdx(self.globalAddressArgIdx, args)
+        return args[idx]
+
+    # args, captured kernel args in runtime
+    def getGlobalDims(self, args):
+        shape = []
+        for e in self.globalDimsArgIdx:
+            t = 1
+            # < 0 means folded arg or constant (-1 - value)
+            # -1 means extended dim which is 1, -2 means folded arg with constant 1 (-1 - value)
+            if e == -1:
+                t = 1
+            elif e < 0 and e != -1:
+                t = -e - 1
+            else:
+                idx = self.getOriginArgIdx(e, args)
+                t = args[idx]
+            shape.append(t)
+        return shape
+
+    def getGlobalStrides(self, args):
+        t_globalDims = [int(e) for e in self.getGlobalDims(args)]
+        t_globalStridesArgIdx = self.globalStridesArgIdx.copy()
+        strides_in_elements = []
+        # todo: get all stride from backend even in extended mode
+        for i in range(self.tensorRank):
+            t = 1
+            if t_globalStridesArgIdx[i] == -1:
+                for ii in range(i):
+                    t *= t_globalDims[ii]
+            # -2 means the sride in arguments is folded constant 1, we don't use 1 because it can not be distinguished from index 1
+            elif t_globalStridesArgIdx[i] < 0:
+                t = -1 - t_globalStridesArgIdx[i]
+            else:
+                new_idx = self.getOriginArgIdx(t_globalStridesArgIdx[i], args)
+                t = args[new_idx]
+
+            strides_in_elements.append(t)
+
+        strides_in_elements = strides_in_elements[1:]
+        strides_in_bytes = [e * self.bytes_from_type(self.tensorDataType) for e in strides_in_elements]
+        return strides_in_bytes
+
+    def getOriginArgIdx(self, idx, args):
+        if self.ids_of_folded_args:
+            ids_before_folding_arg = [i for i in range(len(args)) if i not in self.ids_of_folded_args]
+            return ids_before_folding_arg[idx]
+        else:
+            return idx
+
+    def tensormap(self, args):
+        return driver.utils.cuTensorMapEncodeTiled(
+            self.getTensorMapDataType(),
+            self.getTensorRank(),
+            self.getGlobalAddress(args),
+            self.getGlobalDims(args),
+            self.getGlobalStrides(args),
+            self.getBoxDims(),
+            self.getElementStrides(),
+            self.getInterleave(),
+            self.getSwizzle(),
+            self.getL2Promotion(),
+            self.getOobFill(),
+        )
+
+    # make hashable to use as partial key in cache
+    def __hash__(self):
+        return hash((self.ids_of_folded_args, self.globalAddressArgIdx, tuple(self.globalDimsArgIdx),
+                     tuple(self.globalStridesArgIdx), self.tensorDataType, self.tensorRank, tuple(self.boxDims),
+                     tuple(self.elementStrides), self.interleave, self.swizzle, self.l2Promotion, self.oobFill))
+
+    def __eq__(self, other):
+        if not isinstance(other, self.__class__):
+            return False
+        return (self.ids_of_folded_args, self.globalAddressArgIdx, self.globalDimsArgIdx, self.globalStridesArgIdx,
+                self.tensorDataType, self.tensorRank, self.boxDims, self.elementStrides, self.interleave, self.swizzle,
+                self.l2Promotion,
+                self.oobFill) == (other.ids_of_folded_args, other.globalAddressArgIdx, other.globalDimsArgIdx,
+                                  other.globalStridesArgIdx, other.tensorDataType, other.tensorRank, other.boxDims,
+                                  other.elementStrides, other.interleave, other.swizzle, other.l2Promotion,
+                                  other.oobFill)
+
+
+class TensorMapManager:
+
+    def __init__(self):
+        self.tensormaps_device = {}
+
+    def __getitem__(self, key: tuple):
+        if key in self.tensormaps_device:
+            return int(self.tensormaps_device[key])
+        else:
+            (e, args) = key
+            t_tensormap = e.tensormap(args)
+            TENSORMAP_SIZE_IN_BYTES = 128
+            t_tensormap_device = driver.utils.cuMemAlloc(TENSORMAP_SIZE_IN_BYTES)
+            driver.utils.cuMemcpyHtoD(t_tensormap_device, t_tensormap, TENSORMAP_SIZE_IN_BYTES)
+            self.tensormaps_device[key] = t_tensormap_device
+            return int(self.tensormaps_device[key])
+
+    def __del__(self):
+        for _, v in self.tensormaps_device.items():
+            driver.utils.cuMemFree(v)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/triton/ops/__pycache__/flash_attention.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/triton/ops/__pycache__/flash_attention.cpython-312.pyc
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/triton/ops/matmul_perf_model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/triton/ops/matmul_perf_model.py
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+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/triton/ops/matmul_perf_model.py
@@ -0,0 +1,162 @@
+import heapq
+
+import torch
+
+from .. import cdiv
+from .._C.libtriton.triton import runtime
+from ..runtime import driver
+from ..testing import (get_dram_gbps, get_max_simd_tflops, get_max_tensorcore_tflops, nvsmi)
+
+
+def get_tensorcore_tflops(backend, device, num_ctas, num_warps, dtype):
+    ''' return compute throughput in TOPS '''
+    total_warps = num_ctas * min(num_warps, 4)
+    num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4  # on recent GPUs
+    cur_sm_clock = nvsmi(['clocks.max.sm'])[0]
+    tflops = min(num_subcores, total_warps) / num_subcores * get_max_tensorcore_tflops(
+        dtype, cur_sm_clock, backend, device)
+    return tflops
+
+
+def get_simd_tflops(backend, device, num_ctas, num_warps, dtype):
+    ''' return compute throughput in TOPS '''
+    total_warps = num_ctas * min(num_warps, 4)
+    num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4  # on recent GPUs
+    cur_sm_clock = nvsmi(['clocks.max.sm'])[0]
+    tflops = min(num_subcores, total_warps) / num_subcores * get_max_simd_tflops(dtype, cur_sm_clock, backend, device)
+    return tflops
+
+
+def get_tflops(backend, device, num_ctas, num_warps, dtype):
+    capability = torch.cuda.get_device_capability(device)
+    if capability[0] < 8 and dtype == torch.float32:
+        return get_simd_tflops(backend, device, num_ctas, num_warps, dtype)
+    return get_tensorcore_tflops(backend, device, num_ctas, num_warps, dtype)
+
+
+def estimate_matmul_time(
+        # backend, device,
+        num_warps, num_stages,  #
+        A, B, C,  #
+        M, N, K,  #
+        BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K,  #
+        debug=False, **kwargs  #
+):
+    ''' return estimated running time in ms
+          = max(compute, loading) + store '''
+    backend = runtime.backend.CUDA
+    device = torch.cuda.current_device()
+    dtype = A.dtype
+    dtsize = A.element_size()
+
+    num_cta_m = cdiv(M, BLOCK_M)
+    num_cta_n = cdiv(N, BLOCK_N)
+    num_cta_k = SPLIT_K
+    num_ctas = num_cta_m * num_cta_n * num_cta_k
+
+    # If the input is smaller than the block size
+    M, N = max(M, BLOCK_M), max(N, BLOCK_N)
+
+    # time to compute
+    total_ops = 2 * M * N * K / (1024 * 1024 * 1024)  # GOPS
+    tput = get_tflops(backend, device, num_ctas, num_warps, dtype)
+    compute_ms = total_ops / tput
+
+    # time to load data
+    num_sm = driver.utils.get_device_properties(device)["multiprocessor_count"]
+    active_cta_ratio = min(1, num_ctas / num_sm)
+    active_cta_ratio_bw1 = min(1, num_ctas / 32)  # 32 active ctas are enough to saturate
+    active_cta_ratio_bw2 = max(min(1, (num_ctas - 32) / (108 - 32)), 0)  # 32-108, remaining 5%
+    dram_bw = get_dram_gbps(backend, device) * (active_cta_ratio_bw1 * 0.95 + active_cta_ratio_bw2 * 0.05)  # in GB/s
+    l2_bw = dram_bw * 4  # rough estimation (should be 4.7 for A100?)
+    # assume 80% of (following) loads are in L2 cache
+    load_a_dram = M * K * dtsize * (1 + 0.2 * (num_cta_n - 1))
+    load_a_l2 = M * K * dtsize * 0.8 * (num_cta_n - 1)
+    load_b_dram = N * K * dtsize * (1 + 0.2 * (num_cta_m - 1))
+    load_b_l2 = N * K * dtsize * 0.8 * (num_cta_m - 1)
+    # total
+    total_dram = (load_a_dram + load_b_dram) / (1024 * 1024)  # MB
+    total_l2 = (load_a_l2 + load_b_l2) / (1024 * 1024)
+    # loading time in ms
+    load_ms = total_dram / dram_bw + total_l2 / l2_bw
+
+    # estimate storing time
+    store_bw = dram_bw * 0.6  # :o
+    store_c_dram = M * N * dtsize * SPLIT_K / (1024 * 1024)  # MB
+    if SPLIT_K == 1:
+        store_ms = store_c_dram / store_bw
+    else:
+        reduce_bw = store_bw
+        store_ms = store_c_dram / reduce_bw
+        # c.zero_()
+        zero_ms = M * N * 2 / (1024 * 1024) / store_bw
+        store_ms += zero_ms
+
+    total_time_ms = max(compute_ms, load_ms) + store_ms
+    if debug:
+        print(f'Total time: {total_time_ms}ms, compute time: {compute_ms}ms, '
+              f'loading time: {load_ms}ms, store time: {store_ms}ms, '
+              f'Activate CTAs: {active_cta_ratio*100}%')
+    return total_time_ms
+
+
+def early_config_prune(configs, named_args):
+    device = torch.cuda.current_device()
+    capability = torch.cuda.get_device_capability()
+    # BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages
+    dtsize = named_args['A'].element_size()
+    dtype = named_args['A'].dtype
+
+    # 1. make sure we have enough smem
+    pruned_configs = []
+    for config in configs:
+        kw = config.kwargs
+        BLOCK_M, BLOCK_N, BLOCK_K, num_stages = \
+            kw['BLOCK_M'], kw['BLOCK_N'], kw['BLOCK_K'], config.num_stages
+
+        max_shared_memory = driver.utils.get_device_properties(device)["max_shared_mem"]
+        required_shared_memory = (BLOCK_M + BLOCK_N) * BLOCK_K * num_stages * dtsize
+        if required_shared_memory <= max_shared_memory:
+            pruned_configs.append(config)
+    configs = pruned_configs
+
+    # Some dtypes do not allow atomic_add
+    if dtype not in [torch.float16, torch.float32]:
+        configs = [config for config in configs if config.kwargs['SPLIT_K'] == 1]
+
+    # group configs by (BLOCK_M,_N,_K, SPLIT_K, num_warps)
+    configs_map = {}
+    for config in configs:
+        kw = config.kwargs
+        BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages = \
+            kw['BLOCK_M'], kw['BLOCK_N'], kw['BLOCK_K'], kw['SPLIT_K'], config.num_warps, config.num_stages
+
+        key = (BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps)
+        if key in configs_map:
+            configs_map[key].append((config, num_stages))
+        else:
+            configs_map[key] = [(config, num_stages)]
+
+    pruned_configs = []
+    for k, v in configs_map.items():
+        BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps = k
+        if capability[0] >= 8:
+            # compute cycles (only works for ampere GPUs)
+            mmas = BLOCK_M * BLOCK_N * BLOCK_K / (16 * 8 * 16)
+            mma_cycles = mmas / min(4, num_warps) * 8
+
+            ldgsts_latency = 300  # Does this matter?
+            optimal_num_stages = ldgsts_latency / mma_cycles
+
+            # nearest stages, prefer large #stages
+            nearest = heapq.nsmallest(
+                2, v, key=lambda x: 10 + abs(x[1] - optimal_num_stages)
+                if (x[1] - optimal_num_stages) < 0 else x[1] - optimal_num_stages)
+
+            for n in nearest:
+                pruned_configs.append(n[0])
+        else:  # Volta & Turing only supports num_stages <= 2
+            random_config = v[0][0]
+            random_config.num_stages = 2
+            pruned_configs.append(random_config)
+    return pruned_configs
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/typing_inspection-0.4.1.dist-info/licenses/LICENSE b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/typing_inspection-0.4.1.dist-info/licenses/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..e825ad51621c5e34d370ba64f13f6854d89456a6
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/typing_inspection-0.4.1.dist-info/licenses/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) Pydantic Services Inc. 2025 to present
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata-2025.2.dist-info/licenses/licenses/LICENSE_APACHE b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata-2025.2.dist-info/licenses/licenses/LICENSE_APACHE
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index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata-2025.2.dist-info/licenses/licenses/LICENSE_APACHE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/iso3166.tab b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/iso3166.tab
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+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/iso3166.tab
@@ -0,0 +1,279 @@
+# ISO 3166 alpha-2 country codes
+#
+# This file is in the public domain, so clarified as of
+# 2009-05-17 by Arthur David Olson.
+#
+# From Paul Eggert (2023-09-06):
+# This file contains a table of two-letter country codes.  Columns are
+# separated by a single tab.  Lines beginning with '#' are comments.
+# All text uses UTF-8 encoding.  The columns of the table are as follows:
+#
+# 1.  ISO 3166-1 alpha-2 country code, current as of
+#     ISO/TC 46 N1108 (2023-04-05).  See: ISO/TC 46 Documents
+#     https://www.iso.org/committee/48750.html?view=documents
+# 2.  The usual English name for the coded region.  This sometimes
+#     departs from ISO-listed names, sometimes so that sorted subsets
+#     of names are useful (e.g., "Samoa (American)" and "Samoa
+#     (western)" rather than "American Samoa" and "Samoa"),
+#     sometimes to avoid confusion among non-experts (e.g.,
+#     "Czech Republic" and "Turkey" rather than "Czechia" and "Türkiye"),
+#     and sometimes to omit needless detail or churn (e.g., "Netherlands"
+#     rather than "Netherlands (the)" or "Netherlands (Kingdom of the)").
+#
+# The table is sorted by country code.
+#
+# This table is intended as an aid for users, to help them select time
+# zone data appropriate for their practical needs.  It is not intended
+# to take or endorse any position on legal or territorial claims.
+#
+#country-
+#code	name of country, territory, area, or subdivision
+AD	Andorra
+AE	United Arab Emirates
+AF	Afghanistan
+AG	Antigua & Barbuda
+AI	Anguilla
+AL	Albania
+AM	Armenia
+AO	Angola
+AQ	Antarctica
+AR	Argentina
+AS	Samoa (American)
+AT	Austria
+AU	Australia
+AW	Aruba
+AX	Åland Islands
+AZ	Azerbaijan
+BA	Bosnia & Herzegovina
+BB	Barbados
+BD	Bangladesh
+BE	Belgium
+BF	Burkina Faso
+BG	Bulgaria
+BH	Bahrain
+BI	Burundi
+BJ	Benin
+BL	St Barthelemy
+BM	Bermuda
+BN	Brunei
+BO	Bolivia
+BQ	Caribbean NL
+BR	Brazil
+BS	Bahamas
+BT	Bhutan
+BV	Bouvet Island
+BW	Botswana
+BY	Belarus
+BZ	Belize
+CA	Canada
+CC	Cocos (Keeling) Islands
+CD	Congo (Dem. Rep.)
+CF	Central African Rep.
+CG	Congo (Rep.)
+CH	Switzerland
+CI	Côte d'Ivoire
+CK	Cook Islands
+CL	Chile
+CM	Cameroon
+CN	China
+CO	Colombia
+CR	Costa Rica
+CU	Cuba
+CV	Cape Verde
+CW	Curaçao
+CX	Christmas Island
+CY	Cyprus
+CZ	Czech Republic
+DE	Germany
+DJ	Djibouti
+DK	Denmark
+DM	Dominica
+DO	Dominican Republic
+DZ	Algeria
+EC	Ecuador
+EE	Estonia
+EG	Egypt
+EH	Western Sahara
+ER	Eritrea
+ES	Spain
+ET	Ethiopia
+FI	Finland
+FJ	Fiji
+FK	Falkland Islands
+FM	Micronesia
+FO	Faroe Islands
+FR	France
+GA	Gabon
+GB	Britain (UK)
+GD	Grenada
+GE	Georgia
+GF	French Guiana
+GG	Guernsey
+GH	Ghana
+GI	Gibraltar
+GL	Greenland
+GM	Gambia
+GN	Guinea
+GP	Guadeloupe
+GQ	Equatorial Guinea
+GR	Greece
+GS	South Georgia & the South Sandwich Islands
+GT	Guatemala
+GU	Guam
+GW	Guinea-Bissau
+GY	Guyana
+HK	Hong Kong
+HM	Heard Island & McDonald Islands
+HN	Honduras
+HR	Croatia
+HT	Haiti
+HU	Hungary
+ID	Indonesia
+IE	Ireland
+IL	Israel
+IM	Isle of Man
+IN	India
+IO	British Indian Ocean Territory
+IQ	Iraq
+IR	Iran
+IS	Iceland
+IT	Italy
+JE	Jersey
+JM	Jamaica
+JO	Jordan
+JP	Japan
+KE	Kenya
+KG	Kyrgyzstan
+KH	Cambodia
+KI	Kiribati
+KM	Comoros
+KN	St Kitts & Nevis
+KP	Korea (North)
+KR	Korea (South)
+KW	Kuwait
+KY	Cayman Islands
+KZ	Kazakhstan
+LA	Laos
+LB	Lebanon
+LC	St Lucia
+LI	Liechtenstein
+LK	Sri Lanka
+LR	Liberia
+LS	Lesotho
+LT	Lithuania
+LU	Luxembourg
+LV	Latvia
+LY	Libya
+MA	Morocco
+MC	Monaco
+MD	Moldova
+ME	Montenegro
+MF	St Martin (French)
+MG	Madagascar
+MH	Marshall Islands
+MK	North Macedonia
+ML	Mali
+MM	Myanmar (Burma)
+MN	Mongolia
+MO	Macau
+MP	Northern Mariana Islands
+MQ	Martinique
+MR	Mauritania
+MS	Montserrat
+MT	Malta
+MU	Mauritius
+MV	Maldives
+MW	Malawi
+MX	Mexico
+MY	Malaysia
+MZ	Mozambique
+NA	Namibia
+NC	New Caledonia
+NE	Niger
+NF	Norfolk Island
+NG	Nigeria
+NI	Nicaragua
+NL	Netherlands
+NO	Norway
+NP	Nepal
+NR	Nauru
+NU	Niue
+NZ	New Zealand
+OM	Oman
+PA	Panama
+PE	Peru
+PF	French Polynesia
+PG	Papua New Guinea
+PH	Philippines
+PK	Pakistan
+PL	Poland
+PM	St Pierre & Miquelon
+PN	Pitcairn
+PR	Puerto Rico
+PS	Palestine
+PT	Portugal
+PW	Palau
+PY	Paraguay
+QA	Qatar
+RE	Réunion
+RO	Romania
+RS	Serbia
+RU	Russia
+RW	Rwanda
+SA	Saudi Arabia
+SB	Solomon Islands
+SC	Seychelles
+SD	Sudan
+SE	Sweden
+SG	Singapore
+SH	St Helena
+SI	Slovenia
+SJ	Svalbard & Jan Mayen
+SK	Slovakia
+SL	Sierra Leone
+SM	San Marino
+SN	Senegal
+SO	Somalia
+SR	Suriname
+SS	South Sudan
+ST	Sao Tome & Principe
+SV	El Salvador
+SX	St Maarten (Dutch)
+SY	Syria
+SZ	Eswatini (Swaziland)
+TC	Turks & Caicos Is
+TD	Chad
+TF	French S. Terr.
+TG	Togo
+TH	Thailand
+TJ	Tajikistan
+TK	Tokelau
+TL	East Timor
+TM	Turkmenistan
+TN	Tunisia
+TO	Tonga
+TR	Turkey
+TT	Trinidad & Tobago
+TV	Tuvalu
+TW	Taiwan
+TZ	Tanzania
+UA	Ukraine
+UG	Uganda
+UM	US minor outlying islands
+US	United States
+UY	Uruguay
+UZ	Uzbekistan
+VA	Vatican City
+VC	St Vincent
+VE	Venezuela
+VG	Virgin Islands (UK)
+VI	Virgin Islands (US)
+VN	Vietnam
+VU	Vanuatu
+WF	Wallis & Futuna
+WS	Samoa (western)
+YE	Yemen
+YT	Mayotte
+ZA	South Africa
+ZM	Zambia
+ZW	Zimbabwe
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/zone.tab b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/zone.tab
new file mode 100644
index 0000000000000000000000000000000000000000..2626b0550341a0605087f33cac6952d5fbb24e67
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/zone.tab
@@ -0,0 +1,448 @@
+# tzdb timezone descriptions (deprecated version)
+#
+# This file is in the public domain, so clarified as of
+# 2009-05-17 by Arthur David Olson.
+#
+# From Paul Eggert (2021-09-20):
+# This file is intended as a backward-compatibility aid for older programs.
+# New programs should use zone1970.tab.  This file is like zone1970.tab (see
+# zone1970.tab's comments), but with the following additional restrictions:
+#
+# 1.  This file contains only ASCII characters.
+# 2.  The first data column contains exactly one country code.
+#
+# Because of (2), each row stands for an area that is the intersection
+# of a region identified by a country code and of a timezone where civil
+# clocks have agreed since 1970; this is a narrower definition than
+# that of zone1970.tab.
+#
+# Unlike zone1970.tab, a row's third column can be a Link from
+# 'backward' instead of a Zone.
+#
+# This table is intended as an aid for users, to help them select timezones
+# appropriate for their practical needs.  It is not intended to take or
+# endorse any position on legal or territorial claims.
+#
+#country-
+#code	coordinates	TZ			comments
+AD	+4230+00131	Europe/Andorra
+AE	+2518+05518	Asia/Dubai
+AF	+3431+06912	Asia/Kabul
+AG	+1703-06148	America/Antigua
+AI	+1812-06304	America/Anguilla
+AL	+4120+01950	Europe/Tirane
+AM	+4011+04430	Asia/Yerevan
+AO	-0848+01314	Africa/Luanda
+AQ	-7750+16636	Antarctica/McMurdo	New Zealand time - McMurdo, South Pole
+AQ	-6617+11031	Antarctica/Casey	Casey
+AQ	-6835+07758	Antarctica/Davis	Davis
+AQ	-6640+14001	Antarctica/DumontDUrville	Dumont-d'Urville
+AQ	-6736+06253	Antarctica/Mawson	Mawson
+AQ	-6448-06406	Antarctica/Palmer	Palmer
+AQ	-6734-06808	Antarctica/Rothera	Rothera
+AQ	-690022+0393524	Antarctica/Syowa	Syowa
+AQ	-720041+0023206	Antarctica/Troll	Troll
+AQ	-7824+10654	Antarctica/Vostok	Vostok
+AR	-3436-05827	America/Argentina/Buenos_Aires	Buenos Aires (BA, CF)
+AR	-3124-06411	America/Argentina/Cordoba	Argentina (most areas: CB, CC, CN, ER, FM, MN, SE, SF)
+AR	-2447-06525	America/Argentina/Salta	Salta (SA, LP, NQ, RN)
+AR	-2411-06518	America/Argentina/Jujuy	Jujuy (JY)
+AR	-2649-06513	America/Argentina/Tucuman	Tucuman (TM)
+AR	-2828-06547	America/Argentina/Catamarca	Catamarca (CT), Chubut (CH)
+AR	-2926-06651	America/Argentina/La_Rioja	La Rioja (LR)
+AR	-3132-06831	America/Argentina/San_Juan	San Juan (SJ)
+AR	-3253-06849	America/Argentina/Mendoza	Mendoza (MZ)
+AR	-3319-06621	America/Argentina/San_Luis	San Luis (SL)
+AR	-5138-06913	America/Argentina/Rio_Gallegos	Santa Cruz (SC)
+AR	-5448-06818	America/Argentina/Ushuaia	Tierra del Fuego (TF)
+AS	-1416-17042	Pacific/Pago_Pago
+AT	+4813+01620	Europe/Vienna
+AU	-3133+15905	Australia/Lord_Howe	Lord Howe Island
+AU	-5430+15857	Antarctica/Macquarie	Macquarie Island
+AU	-4253+14719	Australia/Hobart	Tasmania
+AU	-3749+14458	Australia/Melbourne	Victoria
+AU	-3352+15113	Australia/Sydney	New South Wales (most areas)
+AU	-3157+14127	Australia/Broken_Hill	New South Wales (Yancowinna)
+AU	-2728+15302	Australia/Brisbane	Queensland (most areas)
+AU	-2016+14900	Australia/Lindeman	Queensland (Whitsunday Islands)
+AU	-3455+13835	Australia/Adelaide	South Australia
+AU	-1228+13050	Australia/Darwin	Northern Territory
+AU	-3157+11551	Australia/Perth	Western Australia (most areas)
+AU	-3143+12852	Australia/Eucla	Western Australia (Eucla)
+AW	+1230-06958	America/Aruba
+AX	+6006+01957	Europe/Mariehamn
+AZ	+4023+04951	Asia/Baku
+BA	+4352+01825	Europe/Sarajevo
+BB	+1306-05937	America/Barbados
+BD	+2343+09025	Asia/Dhaka
+BE	+5050+00420	Europe/Brussels
+BF	+1222-00131	Africa/Ouagadougou
+BG	+4241+02319	Europe/Sofia
+BH	+2623+05035	Asia/Bahrain
+BI	-0323+02922	Africa/Bujumbura
+BJ	+0629+00237	Africa/Porto-Novo
+BL	+1753-06251	America/St_Barthelemy
+BM	+3217-06446	Atlantic/Bermuda
+BN	+0456+11455	Asia/Brunei
+BO	-1630-06809	America/La_Paz
+BQ	+120903-0681636	America/Kralendijk
+BR	-0351-03225	America/Noronha	Atlantic islands
+BR	-0127-04829	America/Belem	Para (east), Amapa
+BR	-0343-03830	America/Fortaleza	Brazil (northeast: MA, PI, CE, RN, PB)
+BR	-0803-03454	America/Recife	Pernambuco
+BR	-0712-04812	America/Araguaina	Tocantins
+BR	-0940-03543	America/Maceio	Alagoas, Sergipe
+BR	-1259-03831	America/Bahia	Bahia
+BR	-2332-04637	America/Sao_Paulo	Brazil (southeast: GO, DF, MG, ES, RJ, SP, PR, SC, RS)
+BR	-2027-05437	America/Campo_Grande	Mato Grosso do Sul
+BR	-1535-05605	America/Cuiaba	Mato Grosso
+BR	-0226-05452	America/Santarem	Para (west)
+BR	-0846-06354	America/Porto_Velho	Rondonia
+BR	+0249-06040	America/Boa_Vista	Roraima
+BR	-0308-06001	America/Manaus	Amazonas (east)
+BR	-0640-06952	America/Eirunepe	Amazonas (west)
+BR	-0958-06748	America/Rio_Branco	Acre
+BS	+2505-07721	America/Nassau
+BT	+2728+08939	Asia/Thimphu
+BW	-2439+02555	Africa/Gaborone
+BY	+5354+02734	Europe/Minsk
+BZ	+1730-08812	America/Belize
+CA	+4734-05243	America/St_Johns	Newfoundland, Labrador (SE)
+CA	+4439-06336	America/Halifax	Atlantic - NS (most areas), PE
+CA	+4612-05957	America/Glace_Bay	Atlantic - NS (Cape Breton)
+CA	+4606-06447	America/Moncton	Atlantic - New Brunswick
+CA	+5320-06025	America/Goose_Bay	Atlantic - Labrador (most areas)
+CA	+5125-05707	America/Blanc-Sablon	AST - QC (Lower North Shore)
+CA	+4339-07923	America/Toronto	Eastern - ON & QC (most areas)
+CA	+6344-06828	America/Iqaluit	Eastern - NU (most areas)
+CA	+484531-0913718	America/Atikokan	EST - ON (Atikokan), NU (Coral H)
+CA	+4953-09709	America/Winnipeg	Central - ON (west), Manitoba
+CA	+744144-0944945	America/Resolute	Central - NU (Resolute)
+CA	+624900-0920459	America/Rankin_Inlet	Central - NU (central)
+CA	+5024-10439	America/Regina	CST - SK (most areas)
+CA	+5017-10750	America/Swift_Current	CST - SK (midwest)
+CA	+5333-11328	America/Edmonton	Mountain - AB, BC(E), NT(E), SK(W)
+CA	+690650-1050310	America/Cambridge_Bay	Mountain - NU (west)
+CA	+682059-1334300	America/Inuvik	Mountain - NT (west)
+CA	+4906-11631	America/Creston	MST - BC (Creston)
+CA	+5546-12014	America/Dawson_Creek	MST - BC (Dawson Cr, Ft St John)
+CA	+5848-12242	America/Fort_Nelson	MST - BC (Ft Nelson)
+CA	+6043-13503	America/Whitehorse	MST - Yukon (east)
+CA	+6404-13925	America/Dawson	MST - Yukon (west)
+CA	+4916-12307	America/Vancouver	Pacific - BC (most areas)
+CC	-1210+09655	Indian/Cocos
+CD	-0418+01518	Africa/Kinshasa	Dem. Rep. of Congo (west)
+CD	-1140+02728	Africa/Lubumbashi	Dem. Rep. of Congo (east)
+CF	+0422+01835	Africa/Bangui
+CG	-0416+01517	Africa/Brazzaville
+CH	+4723+00832	Europe/Zurich
+CI	+0519-00402	Africa/Abidjan
+CK	-2114-15946	Pacific/Rarotonga
+CL	-3327-07040	America/Santiago	most of Chile
+CL	-4534-07204	America/Coyhaique	Aysen Region
+CL	-5309-07055	America/Punta_Arenas	Magallanes Region
+CL	-2709-10926	Pacific/Easter	Easter Island
+CM	+0403+00942	Africa/Douala
+CN	+3114+12128	Asia/Shanghai	Beijing Time
+CN	+4348+08735	Asia/Urumqi	Xinjiang Time
+CO	+0436-07405	America/Bogota
+CR	+0956-08405	America/Costa_Rica
+CU	+2308-08222	America/Havana
+CV	+1455-02331	Atlantic/Cape_Verde
+CW	+1211-06900	America/Curacao
+CX	-1025+10543	Indian/Christmas
+CY	+3510+03322	Asia/Nicosia	most of Cyprus
+CY	+3507+03357	Asia/Famagusta	Northern Cyprus
+CZ	+5005+01426	Europe/Prague
+DE	+5230+01322	Europe/Berlin	most of Germany
+DE	+4742+00841	Europe/Busingen	Busingen
+DJ	+1136+04309	Africa/Djibouti
+DK	+5540+01235	Europe/Copenhagen
+DM	+1518-06124	America/Dominica
+DO	+1828-06954	America/Santo_Domingo
+DZ	+3647+00303	Africa/Algiers
+EC	-0210-07950	America/Guayaquil	Ecuador (mainland)
+EC	-0054-08936	Pacific/Galapagos	Galapagos Islands
+EE	+5925+02445	Europe/Tallinn
+EG	+3003+03115	Africa/Cairo
+EH	+2709-01312	Africa/El_Aaiun
+ER	+1520+03853	Africa/Asmara
+ES	+4024-00341	Europe/Madrid	Spain (mainland)
+ES	+3553-00519	Africa/Ceuta	Ceuta, Melilla
+ES	+2806-01524	Atlantic/Canary	Canary Islands
+ET	+0902+03842	Africa/Addis_Ababa
+FI	+6010+02458	Europe/Helsinki
+FJ	-1808+17825	Pacific/Fiji
+FK	-5142-05751	Atlantic/Stanley
+FM	+0725+15147	Pacific/Chuuk	Chuuk/Truk, Yap
+FM	+0658+15813	Pacific/Pohnpei	Pohnpei/Ponape
+FM	+0519+16259	Pacific/Kosrae	Kosrae
+FO	+6201-00646	Atlantic/Faroe
+FR	+4852+00220	Europe/Paris
+GA	+0023+00927	Africa/Libreville
+GB	+513030-0000731	Europe/London
+GD	+1203-06145	America/Grenada
+GE	+4143+04449	Asia/Tbilisi
+GF	+0456-05220	America/Cayenne
+GG	+492717-0023210	Europe/Guernsey
+GH	+0533-00013	Africa/Accra
+GI	+3608-00521	Europe/Gibraltar
+GL	+6411-05144	America/Nuuk	most of Greenland
+GL	+7646-01840	America/Danmarkshavn	National Park (east coast)
+GL	+7029-02158	America/Scoresbysund	Scoresbysund/Ittoqqortoormiit
+GL	+7634-06847	America/Thule	Thule/Pituffik
+GM	+1328-01639	Africa/Banjul
+GN	+0931-01343	Africa/Conakry
+GP	+1614-06132	America/Guadeloupe
+GQ	+0345+00847	Africa/Malabo
+GR	+3758+02343	Europe/Athens
+GS	-5416-03632	Atlantic/South_Georgia
+GT	+1438-09031	America/Guatemala
+GU	+1328+14445	Pacific/Guam
+GW	+1151-01535	Africa/Bissau
+GY	+0648-05810	America/Guyana
+HK	+2217+11409	Asia/Hong_Kong
+HN	+1406-08713	America/Tegucigalpa
+HR	+4548+01558	Europe/Zagreb
+HT	+1832-07220	America/Port-au-Prince
+HU	+4730+01905	Europe/Budapest
+ID	-0610+10648	Asia/Jakarta	Java, Sumatra
+ID	-0002+10920	Asia/Pontianak	Borneo (west, central)
+ID	-0507+11924	Asia/Makassar	Borneo (east, south), Sulawesi/Celebes, Bali, Nusa Tengarra, Timor (west)
+ID	-0232+14042	Asia/Jayapura	New Guinea (West Papua / Irian Jaya), Malukus/Moluccas
+IE	+5320-00615	Europe/Dublin
+IL	+314650+0351326	Asia/Jerusalem
+IM	+5409-00428	Europe/Isle_of_Man
+IN	+2232+08822	Asia/Kolkata
+IO	-0720+07225	Indian/Chagos
+IQ	+3321+04425	Asia/Baghdad
+IR	+3540+05126	Asia/Tehran
+IS	+6409-02151	Atlantic/Reykjavik
+IT	+4154+01229	Europe/Rome
+JE	+491101-0020624	Europe/Jersey
+JM	+175805-0764736	America/Jamaica
+JO	+3157+03556	Asia/Amman
+JP	+353916+1394441	Asia/Tokyo
+KE	-0117+03649	Africa/Nairobi
+KG	+4254+07436	Asia/Bishkek
+KH	+1133+10455	Asia/Phnom_Penh
+KI	+0125+17300	Pacific/Tarawa	Gilbert Islands
+KI	-0247-17143	Pacific/Kanton	Phoenix Islands
+KI	+0152-15720	Pacific/Kiritimati	Line Islands
+KM	-1141+04316	Indian/Comoro
+KN	+1718-06243	America/St_Kitts
+KP	+3901+12545	Asia/Pyongyang
+KR	+3733+12658	Asia/Seoul
+KW	+2920+04759	Asia/Kuwait
+KY	+1918-08123	America/Cayman
+KZ	+4315+07657	Asia/Almaty	most of Kazakhstan
+KZ	+4448+06528	Asia/Qyzylorda	Qyzylorda/Kyzylorda/Kzyl-Orda
+KZ	+5312+06337	Asia/Qostanay	Qostanay/Kostanay/Kustanay
+KZ	+5017+05710	Asia/Aqtobe	Aqtobe/Aktobe
+KZ	+4431+05016	Asia/Aqtau	Mangghystau/Mankistau
+KZ	+4707+05156	Asia/Atyrau	Atyrau/Atirau/Gur'yev
+KZ	+5113+05121	Asia/Oral	West Kazakhstan
+LA	+1758+10236	Asia/Vientiane
+LB	+3353+03530	Asia/Beirut
+LC	+1401-06100	America/St_Lucia
+LI	+4709+00931	Europe/Vaduz
+LK	+0656+07951	Asia/Colombo
+LR	+0618-01047	Africa/Monrovia
+LS	-2928+02730	Africa/Maseru
+LT	+5441+02519	Europe/Vilnius
+LU	+4936+00609	Europe/Luxembourg
+LV	+5657+02406	Europe/Riga
+LY	+3254+01311	Africa/Tripoli
+MA	+3339-00735	Africa/Casablanca
+MC	+4342+00723	Europe/Monaco
+MD	+4700+02850	Europe/Chisinau
+ME	+4226+01916	Europe/Podgorica
+MF	+1804-06305	America/Marigot
+MG	-1855+04731	Indian/Antananarivo
+MH	+0709+17112	Pacific/Majuro	most of Marshall Islands
+MH	+0905+16720	Pacific/Kwajalein	Kwajalein
+MK	+4159+02126	Europe/Skopje
+ML	+1239-00800	Africa/Bamako
+MM	+1647+09610	Asia/Yangon
+MN	+4755+10653	Asia/Ulaanbaatar	most of Mongolia
+MN	+4801+09139	Asia/Hovd	Bayan-Olgii, Hovd, Uvs
+MO	+221150+1133230	Asia/Macau
+MP	+1512+14545	Pacific/Saipan
+MQ	+1436-06105	America/Martinique
+MR	+1806-01557	Africa/Nouakchott
+MS	+1643-06213	America/Montserrat
+MT	+3554+01431	Europe/Malta
+MU	-2010+05730	Indian/Mauritius
+MV	+0410+07330	Indian/Maldives
+MW	-1547+03500	Africa/Blantyre
+MX	+1924-09909	America/Mexico_City	Central Mexico
+MX	+2105-08646	America/Cancun	Quintana Roo
+MX	+2058-08937	America/Merida	Campeche, Yucatan
+MX	+2540-10019	America/Monterrey	Durango; Coahuila, Nuevo Leon, Tamaulipas (most areas)
+MX	+2550-09730	America/Matamoros	Coahuila, Nuevo Leon, Tamaulipas (US border)
+MX	+2838-10605	America/Chihuahua	Chihuahua (most areas)
+MX	+3144-10629	America/Ciudad_Juarez	Chihuahua (US border - west)
+MX	+2934-10425	America/Ojinaga	Chihuahua (US border - east)
+MX	+2313-10625	America/Mazatlan	Baja California Sur, Nayarit (most areas), Sinaloa
+MX	+2048-10515	America/Bahia_Banderas	Bahia de Banderas
+MX	+2904-11058	America/Hermosillo	Sonora
+MX	+3232-11701	America/Tijuana	Baja California
+MY	+0310+10142	Asia/Kuala_Lumpur	Malaysia (peninsula)
+MY	+0133+11020	Asia/Kuching	Sabah, Sarawak
+MZ	-2558+03235	Africa/Maputo
+NA	-2234+01706	Africa/Windhoek
+NC	-2216+16627	Pacific/Noumea
+NE	+1331+00207	Africa/Niamey
+NF	-2903+16758	Pacific/Norfolk
+NG	+0627+00324	Africa/Lagos
+NI	+1209-08617	America/Managua
+NL	+5222+00454	Europe/Amsterdam
+NO	+5955+01045	Europe/Oslo
+NP	+2743+08519	Asia/Kathmandu
+NR	-0031+16655	Pacific/Nauru
+NU	-1901-16955	Pacific/Niue
+NZ	-3652+17446	Pacific/Auckland	most of New Zealand
+NZ	-4357-17633	Pacific/Chatham	Chatham Islands
+OM	+2336+05835	Asia/Muscat
+PA	+0858-07932	America/Panama
+PE	-1203-07703	America/Lima
+PF	-1732-14934	Pacific/Tahiti	Society Islands
+PF	-0900-13930	Pacific/Marquesas	Marquesas Islands
+PF	-2308-13457	Pacific/Gambier	Gambier Islands
+PG	-0930+14710	Pacific/Port_Moresby	most of Papua New Guinea
+PG	-0613+15534	Pacific/Bougainville	Bougainville
+PH	+143512+1205804	Asia/Manila
+PK	+2452+06703	Asia/Karachi
+PL	+5215+02100	Europe/Warsaw
+PM	+4703-05620	America/Miquelon
+PN	-2504-13005	Pacific/Pitcairn
+PR	+182806-0660622	America/Puerto_Rico
+PS	+3130+03428	Asia/Gaza	Gaza Strip
+PS	+313200+0350542	Asia/Hebron	West Bank
+PT	+3843-00908	Europe/Lisbon	Portugal (mainland)
+PT	+3238-01654	Atlantic/Madeira	Madeira Islands
+PT	+3744-02540	Atlantic/Azores	Azores
+PW	+0720+13429	Pacific/Palau
+PY	-2516-05740	America/Asuncion
+QA	+2517+05132	Asia/Qatar
+RE	-2052+05528	Indian/Reunion
+RO	+4426+02606	Europe/Bucharest
+RS	+4450+02030	Europe/Belgrade
+RU	+5443+02030	Europe/Kaliningrad	MSK-01 - Kaliningrad
+RU	+554521+0373704	Europe/Moscow	MSK+00 - Moscow area
+# The obsolescent zone.tab format cannot represent Europe/Simferopol well.
+# Put it in RU section and list as UA.  See "territorial claims" above.
+# Programs should use zone1970.tab instead; see above.
+UA	+4457+03406	Europe/Simferopol	Crimea
+RU	+5836+04939	Europe/Kirov	MSK+00 - Kirov
+RU	+4844+04425	Europe/Volgograd	MSK+00 - Volgograd
+RU	+4621+04803	Europe/Astrakhan	MSK+01 - Astrakhan
+RU	+5134+04602	Europe/Saratov	MSK+01 - Saratov
+RU	+5420+04824	Europe/Ulyanovsk	MSK+01 - Ulyanovsk
+RU	+5312+05009	Europe/Samara	MSK+01 - Samara, Udmurtia
+RU	+5651+06036	Asia/Yekaterinburg	MSK+02 - Urals
+RU	+5500+07324	Asia/Omsk	MSK+03 - Omsk
+RU	+5502+08255	Asia/Novosibirsk	MSK+04 - Novosibirsk
+RU	+5322+08345	Asia/Barnaul	MSK+04 - Altai
+RU	+5630+08458	Asia/Tomsk	MSK+04 - Tomsk
+RU	+5345+08707	Asia/Novokuznetsk	MSK+04 - Kemerovo
+RU	+5601+09250	Asia/Krasnoyarsk	MSK+04 - Krasnoyarsk area
+RU	+5216+10420	Asia/Irkutsk	MSK+05 - Irkutsk, Buryatia
+RU	+5203+11328	Asia/Chita	MSK+06 - Zabaykalsky
+RU	+6200+12940	Asia/Yakutsk	MSK+06 - Lena River
+RU	+623923+1353314	Asia/Khandyga	MSK+06 - Tomponsky, Ust-Maysky
+RU	+4310+13156	Asia/Vladivostok	MSK+07 - Amur River
+RU	+643337+1431336	Asia/Ust-Nera	MSK+07 - Oymyakonsky
+RU	+5934+15048	Asia/Magadan	MSK+08 - Magadan
+RU	+4658+14242	Asia/Sakhalin	MSK+08 - Sakhalin Island
+RU	+6728+15343	Asia/Srednekolymsk	MSK+08 - Sakha (E), N Kuril Is
+RU	+5301+15839	Asia/Kamchatka	MSK+09 - Kamchatka
+RU	+6445+17729	Asia/Anadyr	MSK+09 - Bering Sea
+RW	-0157+03004	Africa/Kigali
+SA	+2438+04643	Asia/Riyadh
+SB	-0932+16012	Pacific/Guadalcanal
+SC	-0440+05528	Indian/Mahe
+SD	+1536+03232	Africa/Khartoum
+SE	+5920+01803	Europe/Stockholm
+SG	+0117+10351	Asia/Singapore
+SH	-1555-00542	Atlantic/St_Helena
+SI	+4603+01431	Europe/Ljubljana
+SJ	+7800+01600	Arctic/Longyearbyen
+SK	+4809+01707	Europe/Bratislava
+SL	+0830-01315	Africa/Freetown
+SM	+4355+01228	Europe/San_Marino
+SN	+1440-01726	Africa/Dakar
+SO	+0204+04522	Africa/Mogadishu
+SR	+0550-05510	America/Paramaribo
+SS	+0451+03137	Africa/Juba
+ST	+0020+00644	Africa/Sao_Tome
+SV	+1342-08912	America/El_Salvador
+SX	+180305-0630250	America/Lower_Princes
+SY	+3330+03618	Asia/Damascus
+SZ	-2618+03106	Africa/Mbabane
+TC	+2128-07108	America/Grand_Turk
+TD	+1207+01503	Africa/Ndjamena
+TF	-492110+0701303	Indian/Kerguelen
+TG	+0608+00113	Africa/Lome
+TH	+1345+10031	Asia/Bangkok
+TJ	+3835+06848	Asia/Dushanbe
+TK	-0922-17114	Pacific/Fakaofo
+TL	-0833+12535	Asia/Dili
+TM	+3757+05823	Asia/Ashgabat
+TN	+3648+01011	Africa/Tunis
+TO	-210800-1751200	Pacific/Tongatapu
+TR	+4101+02858	Europe/Istanbul
+TT	+1039-06131	America/Port_of_Spain
+TV	-0831+17913	Pacific/Funafuti
+TW	+2503+12130	Asia/Taipei
+TZ	-0648+03917	Africa/Dar_es_Salaam
+UA	+5026+03031	Europe/Kyiv	most of Ukraine
+UG	+0019+03225	Africa/Kampala
+UM	+2813-17722	Pacific/Midway	Midway Islands
+UM	+1917+16637	Pacific/Wake	Wake Island
+US	+404251-0740023	America/New_York	Eastern (most areas)
+US	+421953-0830245	America/Detroit	Eastern - MI (most areas)
+US	+381515-0854534	America/Kentucky/Louisville	Eastern - KY (Louisville area)
+US	+364947-0845057	America/Kentucky/Monticello	Eastern - KY (Wayne)
+US	+394606-0860929	America/Indiana/Indianapolis	Eastern - IN (most areas)
+US	+384038-0873143	America/Indiana/Vincennes	Eastern - IN (Da, Du, K, Mn)
+US	+410305-0863611	America/Indiana/Winamac	Eastern - IN (Pulaski)
+US	+382232-0862041	America/Indiana/Marengo	Eastern - IN (Crawford)
+US	+382931-0871643	America/Indiana/Petersburg	Eastern - IN (Pike)
+US	+384452-0850402	America/Indiana/Vevay	Eastern - IN (Switzerland)
+US	+415100-0873900	America/Chicago	Central (most areas)
+US	+375711-0864541	America/Indiana/Tell_City	Central - IN (Perry)
+US	+411745-0863730	America/Indiana/Knox	Central - IN (Starke)
+US	+450628-0873651	America/Menominee	Central - MI (Wisconsin border)
+US	+470659-1011757	America/North_Dakota/Center	Central - ND (Oliver)
+US	+465042-1012439	America/North_Dakota/New_Salem	Central - ND (Morton rural)
+US	+471551-1014640	America/North_Dakota/Beulah	Central - ND (Mercer)
+US	+394421-1045903	America/Denver	Mountain (most areas)
+US	+433649-1161209	America/Boise	Mountain - ID (south), OR (east)
+US	+332654-1120424	America/Phoenix	MST - AZ (except Navajo)
+US	+340308-1181434	America/Los_Angeles	Pacific
+US	+611305-1495401	America/Anchorage	Alaska (most areas)
+US	+581807-1342511	America/Juneau	Alaska - Juneau area
+US	+571035-1351807	America/Sitka	Alaska - Sitka area
+US	+550737-1313435	America/Metlakatla	Alaska - Annette Island
+US	+593249-1394338	America/Yakutat	Alaska - Yakutat
+US	+643004-1652423	America/Nome	Alaska (west)
+US	+515248-1763929	America/Adak	Alaska - western Aleutians
+US	+211825-1575130	Pacific/Honolulu	Hawaii
+UY	-345433-0561245	America/Montevideo
+UZ	+3940+06648	Asia/Samarkand	Uzbekistan (west)
+UZ	+4120+06918	Asia/Tashkent	Uzbekistan (east)
+VA	+415408+0122711	Europe/Vatican
+VC	+1309-06114	America/St_Vincent
+VE	+1030-06656	America/Caracas
+VG	+1827-06437	America/Tortola
+VI	+1821-06456	America/St_Thomas
+VN	+1045+10640	Asia/Ho_Chi_Minh
+VU	-1740+16825	Pacific/Efate
+WF	-1318-17610	Pacific/Wallis
+WS	-1350-17144	Pacific/Apia
+YE	+1245+04512	Asia/Aden
+YT	-1247+04514	Indian/Mayotte
+ZA	-2615+02800	Africa/Johannesburg
+ZM	-1525+02817	Africa/Lusaka
+ZW	-1750+03103	Africa/Harare
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/zone1970.tab b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/zone1970.tab
new file mode 100644
index 0000000000000000000000000000000000000000..36535bdf5cfbdedfc70b4d7899de7351b8eb5070
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/zone1970.tab
@@ -0,0 +1,375 @@
+# tzdb timezone descriptions
+#
+# This file is in the public domain.
+#
+# From Paul Eggert (2018-06-27):
+# This file contains a table where each row stands for a timezone where
+# civil timestamps have agreed since 1970.  Columns are separated by
+# a single tab.  Lines beginning with '#' are comments.  All text uses
+# UTF-8 encoding.  The columns of the table are as follows:
+#
+# 1.  The countries that overlap the timezone, as a comma-separated list
+#     of ISO 3166 2-character country codes.  See the file 'iso3166.tab'.
+# 2.  Latitude and longitude of the timezone's principal location
+#     in ISO 6709 sign-degrees-minutes-seconds format,
+#     either ±DDMM±DDDMM or ±DDMMSS±DDDMMSS,
+#     first latitude (+ is north), then longitude (+ is east).
+# 3.  Timezone name used in value of TZ environment variable.
+#     Please see the theory.html file for how these names are chosen.
+#     If multiple timezones overlap a country, each has a row in the
+#     table, with each column 1 containing the country code.
+# 4.  Comments; present if and only if countries have multiple timezones,
+#     and useful only for those countries.  For example, the comments
+#     for the row with countries CH,DE,LI and name Europe/Zurich
+#     are useful only for DE, since CH and LI have no other timezones.
+#
+# If a timezone covers multiple countries, the most-populous city is used,
+# and that country is listed first in column 1; any other countries
+# are listed alphabetically by country code.  The table is sorted
+# first by country code, then (if possible) by an order within the
+# country that (1) makes some geographical sense, and (2) puts the
+# most populous timezones first, where that does not contradict (1).
+#
+# This table is intended as an aid for users, to help them select timezones
+# appropriate for their practical needs.  It is not intended to take or
+# endorse any position on legal or territorial claims.
+#
+#country-
+#codes	coordinates	TZ	comments
+AD	+4230+00131	Europe/Andorra
+AE,OM,RE,SC,TF	+2518+05518	Asia/Dubai	Crozet
+AF	+3431+06912	Asia/Kabul
+AL	+4120+01950	Europe/Tirane
+AM	+4011+04430	Asia/Yerevan
+AQ	-6617+11031	Antarctica/Casey	Casey
+AQ	-6835+07758	Antarctica/Davis	Davis
+AQ	-6736+06253	Antarctica/Mawson	Mawson
+AQ	-6448-06406	Antarctica/Palmer	Palmer
+AQ	-6734-06808	Antarctica/Rothera	Rothera
+AQ	-720041+0023206	Antarctica/Troll	Troll
+AQ	-7824+10654	Antarctica/Vostok	Vostok
+AR	-3436-05827	America/Argentina/Buenos_Aires	Buenos Aires (BA, CF)
+AR	-3124-06411	America/Argentina/Cordoba	most areas: CB, CC, CN, ER, FM, MN, SE, SF
+AR	-2447-06525	America/Argentina/Salta	Salta (SA, LP, NQ, RN)
+AR	-2411-06518	America/Argentina/Jujuy	Jujuy (JY)
+AR	-2649-06513	America/Argentina/Tucuman	Tucumán (TM)
+AR	-2828-06547	America/Argentina/Catamarca	Catamarca (CT), Chubut (CH)
+AR	-2926-06651	America/Argentina/La_Rioja	La Rioja (LR)
+AR	-3132-06831	America/Argentina/San_Juan	San Juan (SJ)
+AR	-3253-06849	America/Argentina/Mendoza	Mendoza (MZ)
+AR	-3319-06621	America/Argentina/San_Luis	San Luis (SL)
+AR	-5138-06913	America/Argentina/Rio_Gallegos	Santa Cruz (SC)
+AR	-5448-06818	America/Argentina/Ushuaia	Tierra del Fuego (TF)
+AS,UM	-1416-17042	Pacific/Pago_Pago	Midway
+AT	+4813+01620	Europe/Vienna
+AU	-3133+15905	Australia/Lord_Howe	Lord Howe Island
+AU	-5430+15857	Antarctica/Macquarie	Macquarie Island
+AU	-4253+14719	Australia/Hobart	Tasmania
+AU	-3749+14458	Australia/Melbourne	Victoria
+AU	-3352+15113	Australia/Sydney	New South Wales (most areas)
+AU	-3157+14127	Australia/Broken_Hill	New South Wales (Yancowinna)
+AU	-2728+15302	Australia/Brisbane	Queensland (most areas)
+AU	-2016+14900	Australia/Lindeman	Queensland (Whitsunday Islands)
+AU	-3455+13835	Australia/Adelaide	South Australia
+AU	-1228+13050	Australia/Darwin	Northern Territory
+AU	-3157+11551	Australia/Perth	Western Australia (most areas)
+AU	-3143+12852	Australia/Eucla	Western Australia (Eucla)
+AZ	+4023+04951	Asia/Baku
+BB	+1306-05937	America/Barbados
+BD	+2343+09025	Asia/Dhaka
+BE,LU,NL	+5050+00420	Europe/Brussels
+BG	+4241+02319	Europe/Sofia
+BM	+3217-06446	Atlantic/Bermuda
+BO	-1630-06809	America/La_Paz
+BR	-0351-03225	America/Noronha	Atlantic islands
+BR	-0127-04829	America/Belem	Pará (east), Amapá
+BR	-0343-03830	America/Fortaleza	Brazil (northeast: MA, PI, CE, RN, PB)
+BR	-0803-03454	America/Recife	Pernambuco
+BR	-0712-04812	America/Araguaina	Tocantins
+BR	-0940-03543	America/Maceio	Alagoas, Sergipe
+BR	-1259-03831	America/Bahia	Bahia
+BR	-2332-04637	America/Sao_Paulo	Brazil (southeast: GO, DF, MG, ES, RJ, SP, PR, SC, RS)
+BR	-2027-05437	America/Campo_Grande	Mato Grosso do Sul
+BR	-1535-05605	America/Cuiaba	Mato Grosso
+BR	-0226-05452	America/Santarem	Pará (west)
+BR	-0846-06354	America/Porto_Velho	Rondônia
+BR	+0249-06040	America/Boa_Vista	Roraima
+BR	-0308-06001	America/Manaus	Amazonas (east)
+BR	-0640-06952	America/Eirunepe	Amazonas (west)
+BR	-0958-06748	America/Rio_Branco	Acre
+BT	+2728+08939	Asia/Thimphu
+BY	+5354+02734	Europe/Minsk
+BZ	+1730-08812	America/Belize
+CA	+4734-05243	America/St_Johns	Newfoundland, Labrador (SE)
+CA	+4439-06336	America/Halifax	Atlantic - NS (most areas), PE
+CA	+4612-05957	America/Glace_Bay	Atlantic - NS (Cape Breton)
+CA	+4606-06447	America/Moncton	Atlantic - New Brunswick
+CA	+5320-06025	America/Goose_Bay	Atlantic - Labrador (most areas)
+CA,BS	+4339-07923	America/Toronto	Eastern - ON & QC (most areas)
+CA	+6344-06828	America/Iqaluit	Eastern - NU (most areas)
+CA	+4953-09709	America/Winnipeg	Central - ON (west), Manitoba
+CA	+744144-0944945	America/Resolute	Central - NU (Resolute)
+CA	+624900-0920459	America/Rankin_Inlet	Central - NU (central)
+CA	+5024-10439	America/Regina	CST - SK (most areas)
+CA	+5017-10750	America/Swift_Current	CST - SK (midwest)
+CA	+5333-11328	America/Edmonton	Mountain - AB, BC(E), NT(E), SK(W)
+CA	+690650-1050310	America/Cambridge_Bay	Mountain - NU (west)
+CA	+682059-1334300	America/Inuvik	Mountain - NT (west)
+CA	+5546-12014	America/Dawson_Creek	MST - BC (Dawson Cr, Ft St John)
+CA	+5848-12242	America/Fort_Nelson	MST - BC (Ft Nelson)
+CA	+6043-13503	America/Whitehorse	MST - Yukon (east)
+CA	+6404-13925	America/Dawson	MST - Yukon (west)
+CA	+4916-12307	America/Vancouver	Pacific - BC (most areas)
+CH,DE,LI	+4723+00832	Europe/Zurich	Büsingen
+CI,BF,GH,GM,GN,IS,ML,MR,SH,SL,SN,TG	+0519-00402	Africa/Abidjan
+CK	-2114-15946	Pacific/Rarotonga
+CL	-3327-07040	America/Santiago	most of Chile
+CL	-4534-07204	America/Coyhaique	Aysén Region
+CL	-5309-07055	America/Punta_Arenas	Magallanes Region
+CL	-2709-10926	Pacific/Easter	Easter Island
+CN	+3114+12128	Asia/Shanghai	Beijing Time
+CN	+4348+08735	Asia/Urumqi	Xinjiang Time
+CO	+0436-07405	America/Bogota
+CR	+0956-08405	America/Costa_Rica
+CU	+2308-08222	America/Havana
+CV	+1455-02331	Atlantic/Cape_Verde
+CY	+3510+03322	Asia/Nicosia	most of Cyprus
+CY	+3507+03357	Asia/Famagusta	Northern Cyprus
+CZ,SK	+5005+01426	Europe/Prague
+DE,DK,NO,SE,SJ	+5230+01322	Europe/Berlin	most of Germany
+DO	+1828-06954	America/Santo_Domingo
+DZ	+3647+00303	Africa/Algiers
+EC	-0210-07950	America/Guayaquil	Ecuador (mainland)
+EC	-0054-08936	Pacific/Galapagos	Galápagos Islands
+EE	+5925+02445	Europe/Tallinn
+EG	+3003+03115	Africa/Cairo
+EH	+2709-01312	Africa/El_Aaiun
+ES	+4024-00341	Europe/Madrid	Spain (mainland)
+ES	+3553-00519	Africa/Ceuta	Ceuta, Melilla
+ES	+2806-01524	Atlantic/Canary	Canary Islands
+FI,AX	+6010+02458	Europe/Helsinki
+FJ	-1808+17825	Pacific/Fiji
+FK	-5142-05751	Atlantic/Stanley
+FM	+0519+16259	Pacific/Kosrae	Kosrae
+FO	+6201-00646	Atlantic/Faroe
+FR,MC	+4852+00220	Europe/Paris
+GB,GG,IM,JE	+513030-0000731	Europe/London
+GE	+4143+04449	Asia/Tbilisi
+GF	+0456-05220	America/Cayenne
+GI	+3608-00521	Europe/Gibraltar
+GL	+6411-05144	America/Nuuk	most of Greenland
+GL	+7646-01840	America/Danmarkshavn	National Park (east coast)
+GL	+7029-02158	America/Scoresbysund	Scoresbysund/Ittoqqortoormiit
+GL	+7634-06847	America/Thule	Thule/Pituffik
+GR	+3758+02343	Europe/Athens
+GS	-5416-03632	Atlantic/South_Georgia
+GT	+1438-09031	America/Guatemala
+GU,MP	+1328+14445	Pacific/Guam
+GW	+1151-01535	Africa/Bissau
+GY	+0648-05810	America/Guyana
+HK	+2217+11409	Asia/Hong_Kong
+HN	+1406-08713	America/Tegucigalpa
+HT	+1832-07220	America/Port-au-Prince
+HU	+4730+01905	Europe/Budapest
+ID	-0610+10648	Asia/Jakarta	Java, Sumatra
+ID	-0002+10920	Asia/Pontianak	Borneo (west, central)
+ID	-0507+11924	Asia/Makassar	Borneo (east, south), Sulawesi/Celebes, Bali, Nusa Tengarra, Timor (west)
+ID	-0232+14042	Asia/Jayapura	New Guinea (West Papua / Irian Jaya), Malukus/Moluccas
+IE	+5320-00615	Europe/Dublin
+IL	+314650+0351326	Asia/Jerusalem
+IN	+2232+08822	Asia/Kolkata
+IO	-0720+07225	Indian/Chagos
+IQ	+3321+04425	Asia/Baghdad
+IR	+3540+05126	Asia/Tehran
+IT,SM,VA	+4154+01229	Europe/Rome
+JM	+175805-0764736	America/Jamaica
+JO	+3157+03556	Asia/Amman
+JP,AU	+353916+1394441	Asia/Tokyo	Eyre Bird Observatory
+KE,DJ,ER,ET,KM,MG,SO,TZ,UG,YT	-0117+03649	Africa/Nairobi
+KG	+4254+07436	Asia/Bishkek
+KI,MH,TV,UM,WF	+0125+17300	Pacific/Tarawa	Gilberts, Marshalls, Wake
+KI	-0247-17143	Pacific/Kanton	Phoenix Islands
+KI	+0152-15720	Pacific/Kiritimati	Line Islands
+KP	+3901+12545	Asia/Pyongyang
+KR	+3733+12658	Asia/Seoul
+KZ	+4315+07657	Asia/Almaty	most of Kazakhstan
+KZ	+4448+06528	Asia/Qyzylorda	Qyzylorda/Kyzylorda/Kzyl-Orda
+KZ	+5312+06337	Asia/Qostanay	Qostanay/Kostanay/Kustanay
+KZ	+5017+05710	Asia/Aqtobe	Aqtöbe/Aktobe
+KZ	+4431+05016	Asia/Aqtau	Mangghystaū/Mankistau
+KZ	+4707+05156	Asia/Atyrau	Atyraū/Atirau/Gur'yev
+KZ	+5113+05121	Asia/Oral	West Kazakhstan
+LB	+3353+03530	Asia/Beirut
+LK	+0656+07951	Asia/Colombo
+LR	+0618-01047	Africa/Monrovia
+LT	+5441+02519	Europe/Vilnius
+LV	+5657+02406	Europe/Riga
+LY	+3254+01311	Africa/Tripoli
+MA	+3339-00735	Africa/Casablanca
+MD	+4700+02850	Europe/Chisinau
+MH	+0905+16720	Pacific/Kwajalein	Kwajalein
+MM,CC	+1647+09610	Asia/Yangon
+MN	+4755+10653	Asia/Ulaanbaatar	most of Mongolia
+MN	+4801+09139	Asia/Hovd	Bayan-Ölgii, Hovd, Uvs
+MO	+221150+1133230	Asia/Macau
+MQ	+1436-06105	America/Martinique
+MT	+3554+01431	Europe/Malta
+MU	-2010+05730	Indian/Mauritius
+MV,TF	+0410+07330	Indian/Maldives	Kerguelen, St Paul I, Amsterdam I
+MX	+1924-09909	America/Mexico_City	Central Mexico
+MX	+2105-08646	America/Cancun	Quintana Roo
+MX	+2058-08937	America/Merida	Campeche, Yucatán
+MX	+2540-10019	America/Monterrey	Durango; Coahuila, Nuevo León, Tamaulipas (most areas)
+MX	+2550-09730	America/Matamoros	Coahuila, Nuevo León, Tamaulipas (US border)
+MX	+2838-10605	America/Chihuahua	Chihuahua (most areas)
+MX	+3144-10629	America/Ciudad_Juarez	Chihuahua (US border - west)
+MX	+2934-10425	America/Ojinaga	Chihuahua (US border - east)
+MX	+2313-10625	America/Mazatlan	Baja California Sur, Nayarit (most areas), Sinaloa
+MX	+2048-10515	America/Bahia_Banderas	Bahía de Banderas
+MX	+2904-11058	America/Hermosillo	Sonora
+MX	+3232-11701	America/Tijuana	Baja California
+MY,BN	+0133+11020	Asia/Kuching	Sabah, Sarawak
+MZ,BI,BW,CD,MW,RW,ZM,ZW	-2558+03235	Africa/Maputo	Central Africa Time
+NA	-2234+01706	Africa/Windhoek
+NC	-2216+16627	Pacific/Noumea
+NF	-2903+16758	Pacific/Norfolk
+NG,AO,BJ,CD,CF,CG,CM,GA,GQ,NE	+0627+00324	Africa/Lagos	West Africa Time
+NI	+1209-08617	America/Managua
+NP	+2743+08519	Asia/Kathmandu
+NR	-0031+16655	Pacific/Nauru
+NU	-1901-16955	Pacific/Niue
+NZ,AQ	-3652+17446	Pacific/Auckland	New Zealand time
+NZ	-4357-17633	Pacific/Chatham	Chatham Islands
+PA,CA,KY	+0858-07932	America/Panama	EST - ON (Atikokan), NU (Coral H)
+PE	-1203-07703	America/Lima
+PF	-1732-14934	Pacific/Tahiti	Society Islands
+PF	-0900-13930	Pacific/Marquesas	Marquesas Islands
+PF	-2308-13457	Pacific/Gambier	Gambier Islands
+PG,AQ,FM	-0930+14710	Pacific/Port_Moresby	Papua New Guinea (most areas), Chuuk, Yap, Dumont d'Urville
+PG	-0613+15534	Pacific/Bougainville	Bougainville
+PH	+143512+1205804	Asia/Manila
+PK	+2452+06703	Asia/Karachi
+PL	+5215+02100	Europe/Warsaw
+PM	+4703-05620	America/Miquelon
+PN	-2504-13005	Pacific/Pitcairn
+PR,AG,CA,AI,AW,BL,BQ,CW,DM,GD,GP,KN,LC,MF,MS,SX,TT,VC,VG,VI	+182806-0660622	America/Puerto_Rico	AST - QC (Lower North Shore)
+PS	+3130+03428	Asia/Gaza	Gaza Strip
+PS	+313200+0350542	Asia/Hebron	West Bank
+PT	+3843-00908	Europe/Lisbon	Portugal (mainland)
+PT	+3238-01654	Atlantic/Madeira	Madeira Islands
+PT	+3744-02540	Atlantic/Azores	Azores
+PW	+0720+13429	Pacific/Palau
+PY	-2516-05740	America/Asuncion
+QA,BH	+2517+05132	Asia/Qatar
+RO	+4426+02606	Europe/Bucharest
+RS,BA,HR,ME,MK,SI	+4450+02030	Europe/Belgrade
+RU	+5443+02030	Europe/Kaliningrad	MSK-01 - Kaliningrad
+RU	+554521+0373704	Europe/Moscow	MSK+00 - Moscow area
+# Mention RU and UA alphabetically.  See "territorial claims" above.
+RU,UA	+4457+03406	Europe/Simferopol	Crimea
+RU	+5836+04939	Europe/Kirov	MSK+00 - Kirov
+RU	+4844+04425	Europe/Volgograd	MSK+00 - Volgograd
+RU	+4621+04803	Europe/Astrakhan	MSK+01 - Astrakhan
+RU	+5134+04602	Europe/Saratov	MSK+01 - Saratov
+RU	+5420+04824	Europe/Ulyanovsk	MSK+01 - Ulyanovsk
+RU	+5312+05009	Europe/Samara	MSK+01 - Samara, Udmurtia
+RU	+5651+06036	Asia/Yekaterinburg	MSK+02 - Urals
+RU	+5500+07324	Asia/Omsk	MSK+03 - Omsk
+RU	+5502+08255	Asia/Novosibirsk	MSK+04 - Novosibirsk
+RU	+5322+08345	Asia/Barnaul	MSK+04 - Altai
+RU	+5630+08458	Asia/Tomsk	MSK+04 - Tomsk
+RU	+5345+08707	Asia/Novokuznetsk	MSK+04 - Kemerovo
+RU	+5601+09250	Asia/Krasnoyarsk	MSK+04 - Krasnoyarsk area
+RU	+5216+10420	Asia/Irkutsk	MSK+05 - Irkutsk, Buryatia
+RU	+5203+11328	Asia/Chita	MSK+06 - Zabaykalsky
+RU	+6200+12940	Asia/Yakutsk	MSK+06 - Lena River
+RU	+623923+1353314	Asia/Khandyga	MSK+06 - Tomponsky, Ust-Maysky
+RU	+4310+13156	Asia/Vladivostok	MSK+07 - Amur River
+RU	+643337+1431336	Asia/Ust-Nera	MSK+07 - Oymyakonsky
+RU	+5934+15048	Asia/Magadan	MSK+08 - Magadan
+RU	+4658+14242	Asia/Sakhalin	MSK+08 - Sakhalin Island
+RU	+6728+15343	Asia/Srednekolymsk	MSK+08 - Sakha (E), N Kuril Is
+RU	+5301+15839	Asia/Kamchatka	MSK+09 - Kamchatka
+RU	+6445+17729	Asia/Anadyr	MSK+09 - Bering Sea
+SA,AQ,KW,YE	+2438+04643	Asia/Riyadh	Syowa
+SB,FM	-0932+16012	Pacific/Guadalcanal	Pohnpei
+SD	+1536+03232	Africa/Khartoum
+SG,AQ,MY	+0117+10351	Asia/Singapore	peninsular Malaysia, Concordia
+SR	+0550-05510	America/Paramaribo
+SS	+0451+03137	Africa/Juba
+ST	+0020+00644	Africa/Sao_Tome
+SV	+1342-08912	America/El_Salvador
+SY	+3330+03618	Asia/Damascus
+TC	+2128-07108	America/Grand_Turk
+TD	+1207+01503	Africa/Ndjamena
+TH,CX,KH,LA,VN	+1345+10031	Asia/Bangkok	north Vietnam
+TJ	+3835+06848	Asia/Dushanbe
+TK	-0922-17114	Pacific/Fakaofo
+TL	-0833+12535	Asia/Dili
+TM	+3757+05823	Asia/Ashgabat
+TN	+3648+01011	Africa/Tunis
+TO	-210800-1751200	Pacific/Tongatapu
+TR	+4101+02858	Europe/Istanbul
+TW	+2503+12130	Asia/Taipei
+UA	+5026+03031	Europe/Kyiv	most of Ukraine
+US	+404251-0740023	America/New_York	Eastern (most areas)
+US	+421953-0830245	America/Detroit	Eastern - MI (most areas)
+US	+381515-0854534	America/Kentucky/Louisville	Eastern - KY (Louisville area)
+US	+364947-0845057	America/Kentucky/Monticello	Eastern - KY (Wayne)
+US	+394606-0860929	America/Indiana/Indianapolis	Eastern - IN (most areas)
+US	+384038-0873143	America/Indiana/Vincennes	Eastern - IN (Da, Du, K, Mn)
+US	+410305-0863611	America/Indiana/Winamac	Eastern - IN (Pulaski)
+US	+382232-0862041	America/Indiana/Marengo	Eastern - IN (Crawford)
+US	+382931-0871643	America/Indiana/Petersburg	Eastern - IN (Pike)
+US	+384452-0850402	America/Indiana/Vevay	Eastern - IN (Switzerland)
+US	+415100-0873900	America/Chicago	Central (most areas)
+US	+375711-0864541	America/Indiana/Tell_City	Central - IN (Perry)
+US	+411745-0863730	America/Indiana/Knox	Central - IN (Starke)
+US	+450628-0873651	America/Menominee	Central - MI (Wisconsin border)
+US	+470659-1011757	America/North_Dakota/Center	Central - ND (Oliver)
+US	+465042-1012439	America/North_Dakota/New_Salem	Central - ND (Morton rural)
+US	+471551-1014640	America/North_Dakota/Beulah	Central - ND (Mercer)
+US	+394421-1045903	America/Denver	Mountain (most areas)
+US	+433649-1161209	America/Boise	Mountain - ID (south), OR (east)
+US,CA	+332654-1120424	America/Phoenix	MST - AZ (most areas), Creston BC
+US	+340308-1181434	America/Los_Angeles	Pacific
+US	+611305-1495401	America/Anchorage	Alaska (most areas)
+US	+581807-1342511	America/Juneau	Alaska - Juneau area
+US	+571035-1351807	America/Sitka	Alaska - Sitka area
+US	+550737-1313435	America/Metlakatla	Alaska - Annette Island
+US	+593249-1394338	America/Yakutat	Alaska - Yakutat
+US	+643004-1652423	America/Nome	Alaska (west)
+US	+515248-1763929	America/Adak	Alaska - western Aleutians
+US	+211825-1575130	Pacific/Honolulu	Hawaii
+UY	-345433-0561245	America/Montevideo
+UZ	+3940+06648	Asia/Samarkand	Uzbekistan (west)
+UZ	+4120+06918	Asia/Tashkent	Uzbekistan (east)
+VE	+1030-06656	America/Caracas
+VN	+1045+10640	Asia/Ho_Chi_Minh	south Vietnam
+VU	-1740+16825	Pacific/Efate
+WS	-1350-17144	Pacific/Apia
+ZA,LS,SZ	-2615+02800	Africa/Johannesburg
+#
+# The next section contains experimental tab-separated comments for
+# use by user agents like tzselect that identify continents and oceans.
+#
+# For example, the comment "#@AQ<tab>Antarctica/" means the country code
+# AQ is in the continent Antarctica regardless of the Zone name,
+# so Pacific/Auckland should be listed under Antarctica as well as
+# under the Pacific because its line's country codes include AQ.
+#
+# If more than one country code is affected each is listed separated
+# by commas, e.g., #@IS,SH<tab>Atlantic/".  If a country code is in
+# more than one continent or ocean, each is listed separated by
+# commas, e.g., the second column of "#@CY,TR<tab>Asia/,Europe/".
+#
+# These experimental comments are present only for country codes where
+# the continent or ocean is not already obvious from the Zone name.
+# For example, there is no such comment for RU since it already
+# corresponds to Zone names starting with both "Europe/" and "Asia/".
+#
+#@AQ	Antarctica/
+#@IS,SH	Atlantic/
+#@CY,TR	Asia/,Europe/
+#@SJ	Arctic/
+#@CC,CX,KM,MG,YT	Indian/
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/zonenow.tab b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/zonenow.tab
new file mode 100644
index 0000000000000000000000000000000000000000..093f0a0cb7495b5d50751bc0cb5b22df4a67c763
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/tzdata/zoneinfo/zonenow.tab
@@ -0,0 +1,296 @@
+# tzdb timezone descriptions, for users who do not care about old timestamps
+#
+# This file is in the public domain.
+#
+# From Paul Eggert (2023-12-18):
+# This file contains a table where each row stands for a timezone
+# where civil timestamps are predicted to agree from now on.
+# This file is like zone1970.tab (see zone1970.tab's comments),
+# but with the following changes:
+#
+# 1.  Each timezone corresponds to a set of clocks that are planned
+#     to agree from now on.  This is a larger set of clocks than in
+#     zone1970.tab, where each timezone's clocks must agree from 1970 on.
+# 2.  The first column is irrelevant and ignored.
+# 3.  The table is sorted in a different way:
+#     first by standard time UTC offset;
+#     then, if DST is used, by daylight saving UTC offset;
+#     then by time zone abbreviation.
+# 4.  Every timezone has a nonempty comments column, with wording
+#     distinguishing the timezone only from other timezones with the
+#     same UTC offset at some point during the year.
+#
+# The format of this table is experimental, and may change in future versions.
+#
+# This table is intended as an aid for users, to help them select timezones
+# appropriate for their practical needs.  It is not intended to take or
+# endorse any position on legal or territorial claims.
+#
+#XX	coordinates	TZ	comments
+#
+# -11 - SST
+XX	-1416-17042	Pacific/Pago_Pago	Midway; Samoa ("SST")
+#
+# -11
+XX	-1901-16955	Pacific/Niue	Niue
+#
+# -10 - HST
+XX	+211825-1575130	Pacific/Honolulu	Hawaii ("HST")
+#
+# -10
+XX	-1732-14934	Pacific/Tahiti	Tahiti; Cook Islands
+#
+# -10/-09 - HST / HDT (North America DST)
+XX	+515248-1763929	America/Adak	western Aleutians in Alaska ("HST/HDT")
+#
+# -09:30
+XX	-0900-13930	Pacific/Marquesas	Marquesas
+#
+# -09
+XX	-2308-13457	Pacific/Gambier	Gambier
+#
+# -09/-08 - AKST/AKDT (North America DST)
+XX	+611305-1495401	America/Anchorage	most of Alaska ("AKST/AKDT")
+#
+# -08
+XX	-2504-13005	Pacific/Pitcairn	Pitcairn
+#
+# -08/-07 - PST/PDT (North America DST)
+XX	+340308-1181434	America/Los_Angeles	Pacific ("PST/PDT") - US & Canada; Mexico near US border
+#
+# -07 - MST
+XX	+332654-1120424	America/Phoenix	Mountain Standard ("MST") - Arizona; western Mexico; Yukon
+#
+# -07/-06 - MST/MDT (North America DST)
+XX	+394421-1045903	America/Denver	Mountain ("MST/MDT") - US & Canada; Mexico near US border
+#
+# -06
+XX	-0054-08936	Pacific/Galapagos	Galápagos
+#
+# -06 - CST
+XX	+1924-09909	America/Mexico_City	Central Standard ("CST") - Saskatchewan; central Mexico; Central America
+#
+# -06/-05 (Chile DST)
+XX	-2709-10926	Pacific/Easter	Easter Island
+#
+# -06/-05 - CST/CDT (North America DST)
+XX	+415100-0873900	America/Chicago	Central ("CST/CDT") - US & Canada; Mexico near US border
+#
+# -05
+XX	-1203-07703	America/Lima	eastern South America
+#
+# -05 - EST
+XX	+175805-0764736	America/Jamaica	Eastern Standard ("EST") - Caymans; Jamaica; eastern Mexico; Panama
+#
+# -05/-04 - CST/CDT (Cuba DST)
+XX	+2308-08222	America/Havana	Cuba
+#
+# -05/-04 - EST/EDT (North America DST)
+XX	+404251-0740023	America/New_York	Eastern ("EST/EDT") - US & Canada
+#
+# -04
+XX	+1030-06656	America/Caracas	western South America
+#
+# -04 - AST
+XX	+1828-06954	America/Santo_Domingo	Atlantic Standard ("AST") - eastern Caribbean
+#
+# -04/-03 (Chile DST)
+XX	-3327-07040	America/Santiago	most of Chile
+#
+# -04/-03 - AST/ADT (North America DST)
+XX	+4439-06336	America/Halifax	Atlantic ("AST/ADT") - Canada; Bermuda
+#
+# -03:30/-02:30 - NST/NDT (North America DST)
+XX	+4734-05243	America/St_Johns	Newfoundland ("NST/NDT")
+#
+# -03
+XX	-2332-04637	America/Sao_Paulo	eastern and southern South America
+#
+# -03/-02 (North America DST)
+XX	+4703-05620	America/Miquelon	St Pierre & Miquelon
+#
+# -02
+XX	-0351-03225	America/Noronha	Fernando de Noronha; South Georgia
+#
+# -02/-01 (EU DST)
+XX	+6411-05144	America/Nuuk	most of Greenland
+#
+# -01
+XX	+1455-02331	Atlantic/Cape_Verde	Cape Verde
+#
+# -01/+00 (EU DST)
+XX	+3744-02540	Atlantic/Azores	Azores
+#
+# +00 - GMT
+XX	+0519-00402	Africa/Abidjan	far western Africa; Iceland ("GMT")
+#
+# +00/+01 - GMT/BST (EU DST)
+XX	+513030-0000731	Europe/London	United Kingdom ("GMT/BST")
+#
+# +00/+01 - WET/WEST (EU DST)
+XX	+3843-00908	Europe/Lisbon	western Europe ("WET/WEST")
+#
+# +00/+02 - Troll DST
+XX	-720041+0023206	Antarctica/Troll	Troll Station in Antarctica
+#
+# +01 - CET
+XX	+3647+00303	Africa/Algiers	Algeria, Tunisia ("CET")
+#
+# +01 - WAT
+XX	+0627+00324	Africa/Lagos	western Africa ("WAT")
+#
+# +01/+00 - IST/GMT (EU DST in reverse)
+XX	+5320-00615	Europe/Dublin	Ireland ("IST/GMT")
+#
+# +01/+00 - (Morocco DST)
+XX	+3339-00735	Africa/Casablanca	Morocco
+#
+# +01/+02 - CET/CEST (EU DST)
+XX	+4852+00220	Europe/Paris	central Europe ("CET/CEST")
+#
+# +02 - CAT
+XX	-2558+03235	Africa/Maputo	central Africa ("CAT")
+#
+# +02 - EET
+XX	+3254+01311	Africa/Tripoli	Libya; Kaliningrad ("EET")
+#
+# +02 - SAST
+XX	-2615+02800	Africa/Johannesburg	southern Africa ("SAST")
+#
+# +02/+03 - EET/EEST (EU DST)
+XX	+3758+02343	Europe/Athens	eastern Europe ("EET/EEST")
+#
+# +02/+03 - EET/EEST (Egypt DST)
+XX	+3003+03115	Africa/Cairo	Egypt
+#
+# +02/+03 - EET/EEST (Lebanon DST)
+XX	+3353+03530	Asia/Beirut	Lebanon
+#
+# +02/+03 - EET/EEST (Moldova DST)
+XX	+4700+02850	Europe/Chisinau	Moldova
+#
+# +02/+03 - EET/EEST (Palestine DST)
+XX	+3130+03428	Asia/Gaza	Palestine
+#
+# +02/+03 - IST/IDT (Israel DST)
+XX	+314650+0351326	Asia/Jerusalem	Israel
+#
+# +03
+XX	+4101+02858	Europe/Istanbul	Near East; Belarus
+#
+# +03 - EAT
+XX	-0117+03649	Africa/Nairobi	eastern Africa ("EAT")
+#
+# +03 - MSK
+XX	+554521+0373704	Europe/Moscow	Moscow ("MSK")
+#
+# +03:30
+XX	+3540+05126	Asia/Tehran	Iran
+#
+# +04
+XX	+2518+05518	Asia/Dubai	Russia; Caucasus; Persian Gulf; Seychelles; Réunion
+#
+# +04:30
+XX	+3431+06912	Asia/Kabul	Afghanistan
+#
+# +05
+XX	+4120+06918	Asia/Tashkent	Russia; Kazakhstan; Tajikistan; Turkmenistan; Uzbekistan; Maldives
+#
+# +05 - PKT
+XX	+2452+06703	Asia/Karachi	Pakistan ("PKT")
+#
+# +05:30
+XX	+0656+07951	Asia/Colombo	Sri Lanka
+#
+# +05:30 - IST
+XX	+2232+08822	Asia/Kolkata	India ("IST")
+#
+# +05:45
+XX	+2743+08519	Asia/Kathmandu	Nepal
+#
+# +06
+XX	+2343+09025	Asia/Dhaka	Russia; Kyrgyzstan; Bhutan; Bangladesh; Chagos
+#
+# +06:30
+XX	+1647+09610	Asia/Yangon	Myanmar; Cocos
+#
+# +07
+XX	+1345+10031	Asia/Bangkok	Russia; Indochina; Christmas Island
+#
+# +07 - WIB
+XX	-0610+10648	Asia/Jakarta	Indonesia ("WIB")
+#
+# +08
+XX	+0117+10351	Asia/Singapore	Russia; Brunei; Malaysia; Singapore; Concordia
+#
+# +08 - AWST
+XX	-3157+11551	Australia/Perth	Western Australia ("AWST")
+#
+# +08 - CST
+XX	+3114+12128	Asia/Shanghai	China ("CST")
+#
+# +08 - HKT
+XX	+2217+11409	Asia/Hong_Kong	Hong Kong ("HKT")
+#
+# +08 - PHT
+XX	+143512+1205804	Asia/Manila	Philippines ("PHT")
+#
+# +08 - WITA
+XX	-0507+11924	Asia/Makassar	Indonesia ("WITA")
+#
+# +08:45
+XX	-3143+12852	Australia/Eucla	Eucla
+#
+# +09
+XX	+5203+11328	Asia/Chita	Russia; Palau; East Timor
+#
+# +09 - JST
+XX	+353916+1394441	Asia/Tokyo	Japan ("JST"); Eyre Bird Observatory
+#
+# +09 - KST
+XX	+3733+12658	Asia/Seoul	Korea ("KST")
+#
+# +09 - WIT
+XX	-0232+14042	Asia/Jayapura	Indonesia ("WIT")
+#
+# +09:30 - ACST
+XX	-1228+13050	Australia/Darwin	Northern Territory ("ACST")
+#
+# +09:30/+10:30 - ACST/ACDT (Australia DST)
+XX	-3455+13835	Australia/Adelaide	South Australia ("ACST/ACDT")
+#
+# +10
+XX	+4310+13156	Asia/Vladivostok	Russia; Yap; Chuuk; Papua New Guinea; Dumont d'Urville
+#
+# +10 - AEST
+XX	-2728+15302	Australia/Brisbane	Queensland ("AEST")
+#
+# +10 - ChST
+XX	+1328+14445	Pacific/Guam	Mariana Islands ("ChST")
+#
+# +10/+11 - AEST/AEDT (Australia DST)
+XX	-3352+15113	Australia/Sydney	southeast Australia ("AEST/AEDT")
+#
+# +10:30/+11
+XX	-3133+15905	Australia/Lord_Howe	Lord Howe Island
+#
+# +11
+XX	-0613+15534	Pacific/Bougainville	Russia; Kosrae; Bougainville; Solomons
+#
+# +11/+12 (Australia DST)
+XX	-2903+16758	Pacific/Norfolk	Norfolk Island
+#
+# +12
+XX	+5301+15839	Asia/Kamchatka	Russia; Tuvalu; Fiji; etc.
+#
+# +12/+13 (New Zealand DST)
+XX	-3652+17446	Pacific/Auckland	New Zealand ("NZST/NZDT")
+#
+# +12:45/+13:45 (Chatham DST)
+XX	-4357-17633	Pacific/Chatham	Chatham Islands
+#
+# +13
+XX	-210800-1751200	Pacific/Tongatapu	Kanton; Tokelau; Samoa (western); Tonga
+#
+# +14
+XX	+0152-15720	Pacific/Kiritimati	Kiritimati
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn-0.35.0.dist-info/licenses/LICENSE.md b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn-0.35.0.dist-info/licenses/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..a6bba14552c7673a7db57a5750ddb06508264273
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn-0.35.0.dist-info/licenses/LICENSE.md
@@ -0,0 +1,27 @@
+Copyright © 2017-present, [Encode OSS Ltd](https://www.encode.io/).
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/lifespan/off.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/lifespan/off.py
new file mode 100644
index 0000000000000000000000000000000000000000..74554b1e2a149c37131168fbe283f3e2476a8f75
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/lifespan/off.py
@@ -0,0 +1,17 @@
+from __future__ import annotations
+
+from typing import Any
+
+from uvicorn import Config
+
+
+class LifespanOff:
+    def __init__(self, config: Config) -> None:
+        self.should_exit = False
+        self.state: dict[str, Any] = {}
+
+    async def startup(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/loops/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/loops/__init__.py
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/loops/asyncio.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/loops/asyncio.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bead4a061cc0989ffcd999ffeb0a7279ec25b4f
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/loops/asyncio.py
@@ -0,0 +1,10 @@
+import asyncio
+import logging
+import sys
+
+logger = logging.getLogger("uvicorn.error")
+
+
+def asyncio_setup(use_subprocess: bool = False) -> None:
+    if sys.platform == "win32" and use_subprocess:
+        asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())  # pragma: full coverage
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/loops/auto.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/loops/auto.py
new file mode 100644
index 0000000000000000000000000000000000000000..2285457bf181d31d36b74ba6c368eac604178645
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/loops/auto.py
@@ -0,0 +1,11 @@
+def auto_loop_setup(use_subprocess: bool = False) -> None:
+    try:
+        import uvloop  # noqa
+    except ImportError:  # pragma: no cover
+        from uvicorn.loops.asyncio import asyncio_setup as loop_setup
+
+        loop_setup(use_subprocess=use_subprocess)
+    else:  # pragma: no cover
+        from uvicorn.loops.uvloop import uvloop_setup
+
+        uvloop_setup(use_subprocess=use_subprocess)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/loops/uvloop.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/loops/uvloop.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e2fd1eb05efc526f660bcf10e865d2fe781a239
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/loops/uvloop.py
@@ -0,0 +1,7 @@
+import asyncio
+
+import uvloop
+
+
+def uvloop_setup(use_subprocess: bool = False) -> None:
+    asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/middleware/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/middleware/__init__.py
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/middleware/message_logger.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/middleware/message_logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..0174bcce6d48de946214d59bcae5543d38d89c3a
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/middleware/message_logger.py
@@ -0,0 +1,87 @@
+import logging
+from typing import Any
+
+from uvicorn._types import (
+    ASGI3Application,
+    ASGIReceiveCallable,
+    ASGIReceiveEvent,
+    ASGISendCallable,
+    ASGISendEvent,
+    WWWScope,
+)
+from uvicorn.logging import TRACE_LOG_LEVEL
+
+PLACEHOLDER_FORMAT = {
+    "body": "<{length} bytes>",
+    "bytes": "<{length} bytes>",
+    "text": "<{length} chars>",
+    "headers": "<...>",
+}
+
+
+def message_with_placeholders(message: Any) -> Any:
+    """
+    Return an ASGI message, with any body-type content omitted and replaced
+    with a placeholder.
+    """
+    new_message = message.copy()
+    for attr in PLACEHOLDER_FORMAT.keys():
+        if message.get(attr) is not None:
+            content = message[attr]
+            placeholder = PLACEHOLDER_FORMAT[attr].format(length=len(content))
+            new_message[attr] = placeholder
+    return new_message
+
+
+class MessageLoggerMiddleware:
+    def __init__(self, app: "ASGI3Application"):
+        self.task_counter = 0
+        self.app = app
+        self.logger = logging.getLogger("uvicorn.asgi")
+
+        def trace(message: Any, *args: Any, **kwargs: Any) -> None:
+            self.logger.log(TRACE_LOG_LEVEL, message, *args, **kwargs)
+
+        self.logger.trace = trace  # type: ignore
+
+    async def __call__(
+        self,
+        scope: "WWWScope",
+        receive: "ASGIReceiveCallable",
+        send: "ASGISendCallable",
+    ) -> None:
+        self.task_counter += 1
+
+        task_counter = self.task_counter
+        client = scope.get("client")
+        prefix = "%s:%d - ASGI" % (client[0], client[1]) if client else "ASGI"
+
+        async def inner_receive() -> "ASGIReceiveEvent":
+            message = await receive()
+            logged_message = message_with_placeholders(message)
+            log_text = "%s [%d] Receive %s"
+            self.logger.trace(  # type: ignore
+                log_text, prefix, task_counter, logged_message
+            )
+            return message
+
+        async def inner_send(message: "ASGISendEvent") -> None:
+            logged_message = message_with_placeholders(message)
+            log_text = "%s [%d] Send %s"
+            self.logger.trace(  # type: ignore
+                log_text, prefix, task_counter, logged_message
+            )
+            await send(message)
+
+        logged_scope = message_with_placeholders(scope)
+        log_text = "%s [%d] Started scope=%s"
+        self.logger.trace(log_text, prefix, task_counter, logged_scope)  # type: ignore
+        try:
+            await self.app(scope, inner_receive, inner_send)
+        except BaseException as exc:
+            log_text = "%s [%d] Raised exception"
+            self.logger.trace(log_text, prefix, task_counter)  # type: ignore
+            raise exc from None
+        else:
+            log_text = "%s [%d] Completed"
+            self.logger.trace(log_text, prefix, task_counter)  # type: ignore
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c3609de6707ee3707ef65902ac75879fbe05c86
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py
@@ -0,0 +1,142 @@
+from __future__ import annotations
+
+import ipaddress
+
+from uvicorn._types import ASGI3Application, ASGIReceiveCallable, ASGISendCallable, Scope
+
+
+class ProxyHeadersMiddleware:
+    """Middleware for handling known proxy headers
+
+    This middleware can be used when a known proxy is fronting the application,
+    and is trusted to be properly setting the `X-Forwarded-Proto` and
+    `X-Forwarded-For` headers with the connecting client information.
+
+    Modifies the `client` and `scheme` information so that they reference
+    the connecting client, rather that the connecting proxy.
+
+    References:
+    - <https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers#Proxies>
+    - <https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/X-Forwarded-For>
+    """
+
+    def __init__(self, app: ASGI3Application, trusted_hosts: list[str] | str = "127.0.0.1") -> None:
+        self.app = app
+        self.trusted_hosts = _TrustedHosts(trusted_hosts)
+
+    async def __call__(self, scope: Scope, receive: ASGIReceiveCallable, send: ASGISendCallable) -> None:
+        if scope["type"] == "lifespan":
+            return await self.app(scope, receive, send)
+
+        client_addr = scope.get("client")
+        client_host = client_addr[0] if client_addr else None
+
+        if client_host in self.trusted_hosts:
+            headers = dict(scope["headers"])
+
+            if b"x-forwarded-proto" in headers:
+                x_forwarded_proto = headers[b"x-forwarded-proto"].decode("latin1").strip()
+
+                if x_forwarded_proto in {"http", "https", "ws", "wss"}:
+                    if scope["type"] == "websocket":
+                        scope["scheme"] = x_forwarded_proto.replace("http", "ws")
+                    else:
+                        scope["scheme"] = x_forwarded_proto
+
+            if b"x-forwarded-for" in headers:
+                x_forwarded_for = headers[b"x-forwarded-for"].decode("latin1")
+                host = self.trusted_hosts.get_trusted_client_host(x_forwarded_for)
+
+                if host:
+                    # If the x-forwarded-for header is empty then host is an empty string.
+                    # Only set the client if we actually got something usable.
+                    # See: https://github.com/encode/uvicorn/issues/1068
+
+                    # We've lost the connecting client's port information by now,
+                    # so only include the host.
+                    port = 0
+                    scope["client"] = (host, port)
+
+        return await self.app(scope, receive, send)
+
+
+def _parse_raw_hosts(value: str) -> list[str]:
+    return [item.strip() for item in value.split(",")]
+
+
+class _TrustedHosts:
+    """Container for trusted hosts and networks"""
+
+    def __init__(self, trusted_hosts: list[str] | str) -> None:
+        self.always_trust: bool = trusted_hosts in ("*", ["*"])
+
+        self.trusted_literals: set[str] = set()
+        self.trusted_hosts: set[ipaddress.IPv4Address | ipaddress.IPv6Address] = set()
+        self.trusted_networks: set[ipaddress.IPv4Network | ipaddress.IPv6Network] = set()
+
+        # Notes:
+        # - We separate hosts from literals as there are many ways to write
+        #   an IPv6 Address so we need to compare by object.
+        # - We don't convert IP Address to single host networks (e.g. /32 / 128) as
+        #   it more efficient to do an address lookup in a set than check for
+        #   membership in each network.
+        # - We still allow literals as it might be possible that we receive a
+        #   something that isn't an IP Address e.g. a unix socket.
+
+        if not self.always_trust:
+            if isinstance(trusted_hosts, str):
+                trusted_hosts = _parse_raw_hosts(trusted_hosts)
+
+            for host in trusted_hosts:
+                # Note: because we always convert invalid IP types to literals it
+                # is not possible for the user to know they provided a malformed IP
+                # type - this may lead to unexpected / difficult to debug behaviour.
+
+                if "/" in host:
+                    # Looks like a network
+                    try:
+                        self.trusted_networks.add(ipaddress.ip_network(host))
+                    except ValueError:
+                        # Was not a valid IP Network
+                        self.trusted_literals.add(host)
+                else:
+                    try:
+                        self.trusted_hosts.add(ipaddress.ip_address(host))
+                    except ValueError:
+                        # Was not a valid IP Address
+                        self.trusted_literals.add(host)
+
+    def __contains__(self, host: str | None) -> bool:
+        if self.always_trust:
+            return True
+
+        if not host:
+            return False
+
+        try:
+            ip = ipaddress.ip_address(host)
+            if ip in self.trusted_hosts:
+                return True
+            return any(ip in net for net in self.trusted_networks)
+
+        except ValueError:
+            return host in self.trusted_literals
+
+    def get_trusted_client_host(self, x_forwarded_for: str) -> str:
+        """Extract the client host from x_forwarded_for header
+
+        In general this is the first "untrusted" host in the forwarded for list.
+        """
+        x_forwarded_for_hosts = _parse_raw_hosts(x_forwarded_for)
+
+        if self.always_trust:
+            return x_forwarded_for_hosts[0]
+
+        # Note: each proxy appends to the header list so check it in reverse order
+        for host in reversed(x_forwarded_for_hosts):
+            if host not in self:
+                return host
+
+        # All hosts are trusted meaning that the client was also a trusted proxy
+        # See https://github.com/encode/uvicorn/issues/1068#issuecomment-855371576
+        return x_forwarded_for_hosts[0]
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/middleware/wsgi.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/middleware/wsgi.py
new file mode 100644
index 0000000000000000000000000000000000000000..d53a8b06d933ef4bbc43b4149d0ebe61b7433eb1
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/middleware/wsgi.py
@@ -0,0 +1,199 @@
+from __future__ import annotations
+
+import asyncio
+import concurrent.futures
+import io
+import sys
+import warnings
+from collections import deque
+from collections.abc import Iterable
+
+from uvicorn._types import (
+    ASGIReceiveCallable,
+    ASGIReceiveEvent,
+    ASGISendCallable,
+    ASGISendEvent,
+    Environ,
+    ExcInfo,
+    HTTPRequestEvent,
+    HTTPResponseBodyEvent,
+    HTTPResponseStartEvent,
+    HTTPScope,
+    StartResponse,
+    WSGIApp,
+)
+
+
+def build_environ(scope: HTTPScope, message: ASGIReceiveEvent, body: io.BytesIO) -> Environ:
+    """
+    Builds a scope and request message into a WSGI environ object.
+    """
+    script_name = scope.get("root_path", "").encode("utf8").decode("latin1")
+    path_info = scope["path"].encode("utf8").decode("latin1")
+    if path_info.startswith(script_name):
+        path_info = path_info[len(script_name) :]
+    environ = {
+        "REQUEST_METHOD": scope["method"],
+        "SCRIPT_NAME": script_name,
+        "PATH_INFO": path_info,
+        "QUERY_STRING": scope["query_string"].decode("ascii"),
+        "SERVER_PROTOCOL": "HTTP/%s" % scope["http_version"],
+        "wsgi.version": (1, 0),
+        "wsgi.url_scheme": scope.get("scheme", "http"),
+        "wsgi.input": body,
+        "wsgi.errors": sys.stdout,
+        "wsgi.multithread": True,
+        "wsgi.multiprocess": True,
+        "wsgi.run_once": False,
+    }
+
+    # Get server name and port - required in WSGI, not in ASGI
+    server = scope.get("server")
+    if server is None:
+        server = ("localhost", 80)
+    environ["SERVER_NAME"] = server[0]
+    environ["SERVER_PORT"] = server[1]
+
+    # Get client IP address
+    client = scope.get("client")
+    if client is not None:
+        environ["REMOTE_ADDR"] = client[0]
+
+    # Go through headers and make them into environ entries
+    for name, value in scope.get("headers", []):
+        name_str: str = name.decode("latin1")
+        if name_str == "content-length":
+            corrected_name = "CONTENT_LENGTH"
+        elif name_str == "content-type":
+            corrected_name = "CONTENT_TYPE"
+        else:
+            corrected_name = "HTTP_%s" % name_str.upper().replace("-", "_")
+        # HTTPbis say only ASCII chars are allowed in headers, but we latin1
+        # just in case
+        value_str: str = value.decode("latin1")
+        if corrected_name in environ:
+            corrected_name_environ = environ[corrected_name]
+            assert isinstance(corrected_name_environ, str)
+            value_str = corrected_name_environ + "," + value_str
+        environ[corrected_name] = value_str
+    return environ
+
+
+class _WSGIMiddleware:
+    def __init__(self, app: WSGIApp, workers: int = 10):
+        warnings.warn(
+            "Uvicorn's native WSGI implementation is deprecated, you should switch to a2wsgi (`pip install a2wsgi`).",
+            DeprecationWarning,
+        )
+        self.app = app
+        self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=workers)
+
+    async def __call__(
+        self,
+        scope: HTTPScope,
+        receive: ASGIReceiveCallable,
+        send: ASGISendCallable,
+    ) -> None:
+        assert scope["type"] == "http"
+        instance = WSGIResponder(self.app, self.executor, scope)
+        await instance(receive, send)
+
+
+class WSGIResponder:
+    def __init__(
+        self,
+        app: WSGIApp,
+        executor: concurrent.futures.ThreadPoolExecutor,
+        scope: HTTPScope,
+    ):
+        self.app = app
+        self.executor = executor
+        self.scope = scope
+        self.status = None
+        self.response_headers = None
+        self.send_event = asyncio.Event()
+        self.send_queue: deque[ASGISendEvent | None] = deque()
+        self.loop: asyncio.AbstractEventLoop = asyncio.get_event_loop()
+        self.response_started = False
+        self.exc_info: ExcInfo | None = None
+
+    async def __call__(self, receive: ASGIReceiveCallable, send: ASGISendCallable) -> None:
+        message: HTTPRequestEvent = await receive()  # type: ignore[assignment]
+        body = io.BytesIO(message.get("body", b""))
+        more_body = message.get("more_body", False)
+        if more_body:
+            body.seek(0, io.SEEK_END)
+            while more_body:
+                body_message: HTTPRequestEvent = (
+                    await receive()  # type: ignore[assignment]
+                )
+                body.write(body_message.get("body", b""))
+                more_body = body_message.get("more_body", False)
+            body.seek(0)
+        environ = build_environ(self.scope, message, body)
+        self.loop = asyncio.get_event_loop()
+        wsgi = self.loop.run_in_executor(self.executor, self.wsgi, environ, self.start_response)
+        sender = self.loop.create_task(self.sender(send))
+        try:
+            await asyncio.wait_for(wsgi, None)
+        finally:
+            self.send_queue.append(None)
+            self.send_event.set()
+            await asyncio.wait_for(sender, None)
+        if self.exc_info is not None:
+            raise self.exc_info[0].with_traceback(self.exc_info[1], self.exc_info[2])
+
+    async def sender(self, send: ASGISendCallable) -> None:
+        while True:
+            if self.send_queue:
+                message = self.send_queue.popleft()
+                if message is None:
+                    return
+                await send(message)
+            else:
+                await self.send_event.wait()
+                self.send_event.clear()
+
+    def start_response(
+        self,
+        status: str,
+        response_headers: Iterable[tuple[str, str]],
+        exc_info: ExcInfo | None = None,
+    ) -> None:
+        self.exc_info = exc_info
+        if not self.response_started:
+            self.response_started = True
+            status_code_str, _ = status.split(" ", 1)
+            status_code = int(status_code_str)
+            headers = [(name.encode("ascii"), value.encode("ascii")) for name, value in response_headers]
+            http_response_start_event: HTTPResponseStartEvent = {
+                "type": "http.response.start",
+                "status": status_code,
+                "headers": headers,
+            }
+            self.send_queue.append(http_response_start_event)
+            self.loop.call_soon_threadsafe(self.send_event.set)
+
+    def wsgi(self, environ: Environ, start_response: StartResponse) -> None:
+        for chunk in self.app(environ, start_response):  # type: ignore
+            response_body: HTTPResponseBodyEvent = {
+                "type": "http.response.body",
+                "body": chunk,
+                "more_body": True,
+            }
+            self.send_queue.append(response_body)
+            self.loop.call_soon_threadsafe(self.send_event.set)
+
+        empty_body: HTTPResponseBodyEvent = {
+            "type": "http.response.body",
+            "body": b"",
+            "more_body": False,
+        }
+        self.send_queue.append(empty_body)
+        self.loop.call_soon_threadsafe(self.send_event.set)
+
+
+try:
+    from a2wsgi import WSGIMiddleware
+except ModuleNotFoundError:  # pragma: no cover
+    WSGIMiddleware = _WSGIMiddleware  # type: ignore[misc, assignment]
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/http/flow_control.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/http/flow_control.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d1b5fa2d3366fd6584181a26c8569a0207900cc
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/http/flow_control.py
@@ -0,0 +1,54 @@
+import asyncio
+
+from uvicorn._types import ASGIReceiveCallable, ASGISendCallable, Scope
+
+CLOSE_HEADER = (b"connection", b"close")
+
+HIGH_WATER_LIMIT = 65536
+
+
+class FlowControl:
+    def __init__(self, transport: asyncio.Transport) -> None:
+        self._transport = transport
+        self.read_paused = False
+        self.write_paused = False
+        self._is_writable_event = asyncio.Event()
+        self._is_writable_event.set()
+
+    async def drain(self) -> None:
+        await self._is_writable_event.wait()  # pragma: full coverage
+
+    def pause_reading(self) -> None:
+        if not self.read_paused:
+            self.read_paused = True
+            self._transport.pause_reading()
+
+    def resume_reading(self) -> None:
+        if self.read_paused:
+            self.read_paused = False
+            self._transport.resume_reading()
+
+    def pause_writing(self) -> None:
+        if not self.write_paused:  # pragma: full coverage
+            self.write_paused = True
+            self._is_writable_event.clear()
+
+    def resume_writing(self) -> None:
+        if self.write_paused:  # pragma: full coverage
+            self.write_paused = False
+            self._is_writable_event.set()
+
+
+async def service_unavailable(scope: Scope, receive: ASGIReceiveCallable, send: ASGISendCallable) -> None:
+    await send(
+        {
+            "type": "http.response.start",
+            "status": 503,
+            "headers": [
+                (b"content-type", b"text/plain; charset=utf-8"),
+                (b"content-length", b"19"),
+                (b"connection", b"close"),
+            ],
+        }
+    )
+    await send({"type": "http.response.body", "body": b"Service Unavailable", "more_body": False})
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/http/httptools_impl.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/http/httptools_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8795ed35c598d9635961bac628c4129b83844b7
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/http/httptools_impl.py
@@ -0,0 +1,570 @@
+from __future__ import annotations
+
+import asyncio
+import http
+import logging
+import re
+import urllib
+from asyncio.events import TimerHandle
+from collections import deque
+from typing import Any, Callable, Literal, cast
+
+import httptools
+
+from uvicorn._types import (
+    ASGI3Application,
+    ASGIReceiveEvent,
+    ASGISendEvent,
+    HTTPRequestEvent,
+    HTTPResponseStartEvent,
+    HTTPScope,
+)
+from uvicorn.config import Config
+from uvicorn.logging import TRACE_LOG_LEVEL
+from uvicorn.protocols.http.flow_control import CLOSE_HEADER, HIGH_WATER_LIMIT, FlowControl, service_unavailable
+from uvicorn.protocols.utils import get_client_addr, get_local_addr, get_path_with_query_string, get_remote_addr, is_ssl
+from uvicorn.server import ServerState
+
+HEADER_RE = re.compile(b'[\x00-\x1f\x7f()<>@,;:[]={} \t\\"]')
+HEADER_VALUE_RE = re.compile(b"[\x00-\x08\x0a-\x1f\x7f]")
+
+
+def _get_status_line(status_code: int) -> bytes:
+    try:
+        phrase = http.HTTPStatus(status_code).phrase.encode()
+    except ValueError:
+        phrase = b""
+    return b"".join([b"HTTP/1.1 ", str(status_code).encode(), b" ", phrase, b"\r\n"])
+
+
+STATUS_LINE = {status_code: _get_status_line(status_code) for status_code in range(100, 600)}
+
+
+class HttpToolsProtocol(asyncio.Protocol):
+    def __init__(
+        self,
+        config: Config,
+        server_state: ServerState,
+        app_state: dict[str, Any],
+        _loop: asyncio.AbstractEventLoop | None = None,
+    ) -> None:
+        if not config.loaded:
+            config.load()
+
+        self.config = config
+        self.app = config.loaded_app
+        self.loop = _loop or asyncio.get_event_loop()
+        self.logger = logging.getLogger("uvicorn.error")
+        self.access_logger = logging.getLogger("uvicorn.access")
+        self.access_log = self.access_logger.hasHandlers()
+        self.parser = httptools.HttpRequestParser(self)
+
+        try:
+            # Enable dangerous leniencies to allow server to a response on the first request from a pipelined request.
+            self.parser.set_dangerous_leniencies(lenient_data_after_close=True)
+        except AttributeError:  # pragma: no cover
+            # httptools < 0.6.3
+            pass
+
+        self.ws_protocol_class = config.ws_protocol_class
+        self.root_path = config.root_path
+        self.limit_concurrency = config.limit_concurrency
+        self.app_state = app_state
+
+        # Timeouts
+        self.timeout_keep_alive_task: TimerHandle | None = None
+        self.timeout_keep_alive = config.timeout_keep_alive
+
+        # Global state
+        self.server_state = server_state
+        self.connections = server_state.connections
+        self.tasks = server_state.tasks
+
+        # Per-connection state
+        self.transport: asyncio.Transport = None  # type: ignore[assignment]
+        self.flow: FlowControl = None  # type: ignore[assignment]
+        self.server: tuple[str, int] | None = None
+        self.client: tuple[str, int] | None = None
+        self.scheme: Literal["http", "https"] | None = None
+        self.pipeline: deque[tuple[RequestResponseCycle, ASGI3Application]] = deque()
+
+        # Per-request state
+        self.scope: HTTPScope = None  # type: ignore[assignment]
+        self.headers: list[tuple[bytes, bytes]] = None  # type: ignore[assignment]
+        self.expect_100_continue = False
+        self.cycle: RequestResponseCycle = None  # type: ignore[assignment]
+
+    # Protocol interface
+    def connection_made(  # type: ignore[override]
+        self, transport: asyncio.Transport
+    ) -> None:
+        self.connections.add(self)
+
+        self.transport = transport
+        self.flow = FlowControl(transport)
+        self.server = get_local_addr(transport)
+        self.client = get_remote_addr(transport)
+        self.scheme = "https" if is_ssl(transport) else "http"
+
+        if self.logger.level <= TRACE_LOG_LEVEL:
+            prefix = "%s:%d - " % self.client if self.client else ""
+            self.logger.log(TRACE_LOG_LEVEL, "%sHTTP connection made", prefix)
+
+    def connection_lost(self, exc: Exception | None) -> None:
+        self.connections.discard(self)
+
+        if self.logger.level <= TRACE_LOG_LEVEL:
+            prefix = "%s:%d - " % self.client if self.client else ""
+            self.logger.log(TRACE_LOG_LEVEL, "%sHTTP connection lost", prefix)
+
+        if self.cycle and not self.cycle.response_complete:
+            self.cycle.disconnected = True
+        if self.cycle is not None:
+            self.cycle.message_event.set()
+        if self.flow is not None:
+            self.flow.resume_writing()
+        if exc is None:
+            self.transport.close()
+            self._unset_keepalive_if_required()
+
+        self.parser = None
+
+    def eof_received(self) -> None:
+        pass
+
+    def _unset_keepalive_if_required(self) -> None:
+        if self.timeout_keep_alive_task is not None:
+            self.timeout_keep_alive_task.cancel()
+            self.timeout_keep_alive_task = None
+
+    def _get_upgrade(self) -> bytes | None:
+        connection = []
+        upgrade = None
+        for name, value in self.headers:
+            if name == b"connection":
+                connection = [token.lower().strip() for token in value.split(b",")]
+            if name == b"upgrade":
+                upgrade = value.lower()
+        if b"upgrade" in connection:
+            return upgrade
+        return None  # pragma: full coverage
+
+    def _should_upgrade_to_ws(self) -> bool:
+        if self.ws_protocol_class is None:
+            return False
+        return True
+
+    def _unsupported_upgrade_warning(self) -> None:
+        self.logger.warning("Unsupported upgrade request.")
+        if not self._should_upgrade_to_ws():
+            msg = "No supported WebSocket library detected. Please use \"pip install 'uvicorn[standard]'\", or install 'websockets' or 'wsproto' manually."  # noqa: E501
+            self.logger.warning(msg)
+
+    def _should_upgrade(self) -> bool:
+        upgrade = self._get_upgrade()
+        return upgrade == b"websocket" and self._should_upgrade_to_ws()
+
+    def data_received(self, data: bytes) -> None:
+        self._unset_keepalive_if_required()
+
+        try:
+            self.parser.feed_data(data)
+        except httptools.HttpParserError:
+            msg = "Invalid HTTP request received."
+            self.logger.warning(msg)
+            self.send_400_response(msg)
+            return
+        except httptools.HttpParserUpgrade:
+            if self._should_upgrade():
+                self.handle_websocket_upgrade()
+            else:
+                self._unsupported_upgrade_warning()
+
+    def handle_websocket_upgrade(self) -> None:
+        if self.logger.level <= TRACE_LOG_LEVEL:
+            prefix = "%s:%d - " % self.client if self.client else ""
+            self.logger.log(TRACE_LOG_LEVEL, "%sUpgrading to WebSocket", prefix)
+
+        self.connections.discard(self)
+        method = self.scope["method"].encode()
+        output = [method, b" ", self.url, b" HTTP/1.1\r\n"]
+        for name, value in self.scope["headers"]:
+            output += [name, b": ", value, b"\r\n"]
+        output.append(b"\r\n")
+        protocol = self.ws_protocol_class(  # type: ignore[call-arg, misc]
+            config=self.config,
+            server_state=self.server_state,
+            app_state=self.app_state,
+        )
+        protocol.connection_made(self.transport)
+        protocol.data_received(b"".join(output))
+        self.transport.set_protocol(protocol)
+
+    def send_400_response(self, msg: str) -> None:
+        content = [STATUS_LINE[400]]
+        for name, value in self.server_state.default_headers:
+            content.extend([name, b": ", value, b"\r\n"])  # pragma: full coverage
+        content.extend(
+            [
+                b"content-type: text/plain; charset=utf-8\r\n",
+                b"content-length: " + str(len(msg)).encode("ascii") + b"\r\n",
+                b"connection: close\r\n",
+                b"\r\n",
+                msg.encode("ascii"),
+            ]
+        )
+        self.transport.write(b"".join(content))
+        self.transport.close()
+
+    def on_message_begin(self) -> None:
+        self.url = b""
+        self.expect_100_continue = False
+        self.headers = []
+        self.scope = {  # type: ignore[typeddict-item]
+            "type": "http",
+            "asgi": {"version": self.config.asgi_version, "spec_version": "2.3"},
+            "http_version": "1.1",
+            "server": self.server,
+            "client": self.client,
+            "scheme": self.scheme,  # type: ignore[typeddict-item]
+            "root_path": self.root_path,
+            "headers": self.headers,
+            "state": self.app_state.copy(),
+        }
+
+    # Parser callbacks
+    def on_url(self, url: bytes) -> None:
+        self.url += url
+
+    def on_header(self, name: bytes, value: bytes) -> None:
+        name = name.lower()
+        if name == b"expect" and value.lower() == b"100-continue":
+            self.expect_100_continue = True
+        self.headers.append((name, value))
+
+    def on_headers_complete(self) -> None:
+        http_version = self.parser.get_http_version()
+        method = self.parser.get_method()
+        self.scope["method"] = method.decode("ascii")
+        if http_version != "1.1":
+            self.scope["http_version"] = http_version
+        if self.parser.should_upgrade() and self._should_upgrade():
+            return
+        parsed_url = httptools.parse_url(self.url)
+        raw_path = parsed_url.path
+        path = raw_path.decode("ascii")
+        if "%" in path:
+            path = urllib.parse.unquote(path)
+        full_path = self.root_path + path
+        full_raw_path = self.root_path.encode("ascii") + raw_path
+        self.scope["path"] = full_path
+        self.scope["raw_path"] = full_raw_path
+        self.scope["query_string"] = parsed_url.query or b""
+
+        # Handle 503 responses when 'limit_concurrency' is exceeded.
+        if self.limit_concurrency is not None and (
+            len(self.connections) >= self.limit_concurrency or len(self.tasks) >= self.limit_concurrency
+        ):
+            app = service_unavailable
+            message = "Exceeded concurrency limit."
+            self.logger.warning(message)
+        else:
+            app = self.app
+
+        existing_cycle = self.cycle
+        self.cycle = RequestResponseCycle(
+            scope=self.scope,
+            transport=self.transport,
+            flow=self.flow,
+            logger=self.logger,
+            access_logger=self.access_logger,
+            access_log=self.access_log,
+            default_headers=self.server_state.default_headers,
+            message_event=asyncio.Event(),
+            expect_100_continue=self.expect_100_continue,
+            keep_alive=http_version != "1.0",
+            on_response=self.on_response_complete,
+        )
+        if existing_cycle is None or existing_cycle.response_complete:
+            # Standard case - start processing the request.
+            task = self.loop.create_task(self.cycle.run_asgi(app))
+            task.add_done_callback(self.tasks.discard)
+            self.tasks.add(task)
+        else:
+            # Pipelined HTTP requests need to be queued up.
+            self.flow.pause_reading()
+            self.pipeline.appendleft((self.cycle, app))
+
+    def on_body(self, body: bytes) -> None:
+        if (self.parser.should_upgrade() and self._should_upgrade()) or self.cycle.response_complete:
+            return
+        self.cycle.body += body
+        if len(self.cycle.body) > HIGH_WATER_LIMIT:
+            self.flow.pause_reading()
+        self.cycle.message_event.set()
+
+    def on_message_complete(self) -> None:
+        if (self.parser.should_upgrade() and self._should_upgrade()) or self.cycle.response_complete:
+            return
+        self.cycle.more_body = False
+        self.cycle.message_event.set()
+
+    def on_response_complete(self) -> None:
+        # Callback for pipelined HTTP requests to be started.
+        self.server_state.total_requests += 1
+
+        if self.transport.is_closing():
+            return
+
+        self._unset_keepalive_if_required()
+
+        # Unpause data reads if needed.
+        self.flow.resume_reading()
+
+        # Unblock any pipelined events. If there are none, arm the
+        # Keep-Alive timeout instead.
+        if self.pipeline:
+            cycle, app = self.pipeline.pop()
+            task = self.loop.create_task(cycle.run_asgi(app))
+            task.add_done_callback(self.tasks.discard)
+            self.tasks.add(task)
+        else:
+            self.timeout_keep_alive_task = self.loop.call_later(
+                self.timeout_keep_alive, self.timeout_keep_alive_handler
+            )
+
+    def shutdown(self) -> None:
+        """
+        Called by the server to commence a graceful shutdown.
+        """
+        if self.cycle is None or self.cycle.response_complete:
+            self.transport.close()
+        else:
+            self.cycle.keep_alive = False
+
+    def pause_writing(self) -> None:
+        """
+        Called by the transport when the write buffer exceeds the high water mark.
+        """
+        self.flow.pause_writing()  # pragma: full coverage
+
+    def resume_writing(self) -> None:
+        """
+        Called by the transport when the write buffer drops below the low water mark.
+        """
+        self.flow.resume_writing()  # pragma: full coverage
+
+    def timeout_keep_alive_handler(self) -> None:
+        """
+        Called on a keep-alive connection if no new data is received after a short
+        delay.
+        """
+        if not self.transport.is_closing():
+            self.transport.close()
+
+
+class RequestResponseCycle:
+    def __init__(
+        self,
+        scope: HTTPScope,
+        transport: asyncio.Transport,
+        flow: FlowControl,
+        logger: logging.Logger,
+        access_logger: logging.Logger,
+        access_log: bool,
+        default_headers: list[tuple[bytes, bytes]],
+        message_event: asyncio.Event,
+        expect_100_continue: bool,
+        keep_alive: bool,
+        on_response: Callable[..., None],
+    ):
+        self.scope = scope
+        self.transport = transport
+        self.flow = flow
+        self.logger = logger
+        self.access_logger = access_logger
+        self.access_log = access_log
+        self.default_headers = default_headers
+        self.message_event = message_event
+        self.on_response = on_response
+
+        # Connection state
+        self.disconnected = False
+        self.keep_alive = keep_alive
+        self.waiting_for_100_continue = expect_100_continue
+
+        # Request state
+        self.body = b""
+        self.more_body = True
+
+        # Response state
+        self.response_started = False
+        self.response_complete = False
+        self.chunked_encoding: bool | None = None
+        self.expected_content_length = 0
+
+    # ASGI exception wrapper
+    async def run_asgi(self, app: ASGI3Application) -> None:
+        try:
+            result = await app(  # type: ignore[func-returns-value]
+                self.scope, self.receive, self.send
+            )
+        except BaseException as exc:
+            msg = "Exception in ASGI application\n"
+            self.logger.error(msg, exc_info=exc)
+            if not self.response_started:
+                await self.send_500_response()
+            else:
+                self.transport.close()
+        else:
+            if result is not None:
+                msg = "ASGI callable should return None, but returned '%s'."
+                self.logger.error(msg, result)
+                self.transport.close()
+            elif not self.response_started and not self.disconnected:
+                msg = "ASGI callable returned without starting response."
+                self.logger.error(msg)
+                await self.send_500_response()
+            elif not self.response_complete and not self.disconnected:
+                msg = "ASGI callable returned without completing response."
+                self.logger.error(msg)
+                self.transport.close()
+        finally:
+            self.on_response = lambda: None
+
+    async def send_500_response(self) -> None:
+        await self.send(
+            {
+                "type": "http.response.start",
+                "status": 500,
+                "headers": [
+                    (b"content-type", b"text/plain; charset=utf-8"),
+                    (b"content-length", b"21"),
+                    (b"connection", b"close"),
+                ],
+            }
+        )
+        await self.send({"type": "http.response.body", "body": b"Internal Server Error", "more_body": False})
+
+    # ASGI interface
+    async def send(self, message: ASGISendEvent) -> None:
+        message_type = message["type"]
+
+        if self.flow.write_paused and not self.disconnected:
+            await self.flow.drain()  # pragma: full coverage
+
+        if self.disconnected:
+            return  # pragma: full coverage
+
+        if not self.response_started:
+            # Sending response status line and headers
+            if message_type != "http.response.start":
+                msg = "Expected ASGI message 'http.response.start', but got '%s'."
+                raise RuntimeError(msg % message_type)
+            message = cast("HTTPResponseStartEvent", message)
+
+            self.response_started = True
+            self.waiting_for_100_continue = False
+
+            status_code = message["status"]
+            headers = self.default_headers + list(message.get("headers", []))
+
+            if CLOSE_HEADER in self.scope["headers"] and CLOSE_HEADER not in headers:
+                headers = headers + [CLOSE_HEADER]
+
+            if self.access_log:
+                self.access_logger.info(
+                    '%s - "%s %s HTTP/%s" %d',
+                    get_client_addr(self.scope),
+                    self.scope["method"],
+                    get_path_with_query_string(self.scope),
+                    self.scope["http_version"],
+                    status_code,
+                )
+
+            # Write response status line and headers
+            content = [STATUS_LINE[status_code]]
+
+            for name, value in headers:
+                if HEADER_RE.search(name):
+                    raise RuntimeError("Invalid HTTP header name.")  # pragma: full coverage
+                if HEADER_VALUE_RE.search(value):
+                    raise RuntimeError("Invalid HTTP header value.")
+
+                name = name.lower()
+                if name == b"content-length" and self.chunked_encoding is None:
+                    self.expected_content_length = int(value.decode())
+                    self.chunked_encoding = False
+                elif name == b"transfer-encoding" and value.lower() == b"chunked":
+                    self.expected_content_length = 0
+                    self.chunked_encoding = True
+                elif name == b"connection" and value.lower() == b"close":
+                    self.keep_alive = False
+                content.extend([name, b": ", value, b"\r\n"])
+
+            if self.chunked_encoding is None and self.scope["method"] != "HEAD" and status_code not in (204, 304):
+                # Neither content-length nor transfer-encoding specified
+                self.chunked_encoding = True
+                content.append(b"transfer-encoding: chunked\r\n")
+
+            content.append(b"\r\n")
+            self.transport.write(b"".join(content))
+
+        elif not self.response_complete:
+            # Sending response body
+            if message_type != "http.response.body":
+                msg = "Expected ASGI message 'http.response.body', but got '%s'."
+                raise RuntimeError(msg % message_type)
+
+            body = cast(bytes, message.get("body", b""))
+            more_body = message.get("more_body", False)
+
+            # Write response body
+            if self.scope["method"] == "HEAD":
+                self.expected_content_length = 0
+            elif self.chunked_encoding:
+                if body:
+                    content = [b"%x\r\n" % len(body), body, b"\r\n"]
+                else:
+                    content = []
+                if not more_body:
+                    content.append(b"0\r\n\r\n")
+                self.transport.write(b"".join(content))
+            else:
+                num_bytes = len(body)
+                if num_bytes > self.expected_content_length:
+                    raise RuntimeError("Response content longer than Content-Length")
+                else:
+                    self.expected_content_length -= num_bytes
+                self.transport.write(body)
+
+            # Handle response completion
+            if not more_body:
+                if self.expected_content_length != 0:
+                    raise RuntimeError("Response content shorter than Content-Length")
+                self.response_complete = True
+                self.message_event.set()
+                if not self.keep_alive:
+                    self.transport.close()
+                self.on_response()
+
+        else:
+            # Response already sent
+            msg = "Unexpected ASGI message '%s' sent, after response already completed."
+            raise RuntimeError(msg % message_type)
+
+    async def receive(self) -> ASGIReceiveEvent:
+        if self.waiting_for_100_continue and not self.transport.is_closing():
+            self.transport.write(b"HTTP/1.1 100 Continue\r\n\r\n")
+            self.waiting_for_100_continue = False
+
+        if not self.disconnected and not self.response_complete:
+            self.flow.resume_reading()
+            await self.message_event.wait()
+            self.message_event.clear()
+
+        if self.disconnected or self.response_complete:
+            return {"type": "http.disconnect"}
+        message: HTTPRequestEvent = {"type": "http.request", "body": self.body, "more_body": self.more_body}
+        self.body = b""
+        return message
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1d6f01d53b33f0cba9d61b8f7ba2e8c079e101f
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/utils.py
@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+import asyncio
+import urllib.parse
+
+from uvicorn._types import WWWScope
+
+
+class ClientDisconnected(OSError): ...
+
+
+def get_remote_addr(transport: asyncio.Transport) -> tuple[str, int] | None:
+    socket_info = transport.get_extra_info("socket")
+    if socket_info is not None:
+        try:
+            info = socket_info.getpeername()
+            return (str(info[0]), int(info[1])) if isinstance(info, tuple) else None
+        except OSError:  # pragma: no cover
+            # This case appears to inconsistently occur with uvloop
+            # bound to a unix domain socket.
+            return None
+
+    info = transport.get_extra_info("peername")
+    if info is not None and isinstance(info, (list, tuple)) and len(info) == 2:
+        return (str(info[0]), int(info[1]))
+    return None
+
+
+def get_local_addr(transport: asyncio.Transport) -> tuple[str, int] | None:
+    socket_info = transport.get_extra_info("socket")
+    if socket_info is not None:
+        info = socket_info.getsockname()
+
+        return (str(info[0]), int(info[1])) if isinstance(info, tuple) else None
+    info = transport.get_extra_info("sockname")
+    if info is not None and isinstance(info, (list, tuple)) and len(info) == 2:
+        return (str(info[0]), int(info[1]))
+    return None
+
+
+def is_ssl(transport: asyncio.Transport) -> bool:
+    return bool(transport.get_extra_info("sslcontext"))
+
+
+def get_client_addr(scope: WWWScope) -> str:
+    client = scope.get("client")
+    if not client:
+        return ""
+    return "%s:%d" % client
+
+
+def get_path_with_query_string(scope: WWWScope) -> str:
+    path_with_query_string = urllib.parse.quote(scope["path"])
+    if scope["query_string"]:
+        path_with_query_string = "{}?{}".format(path_with_query_string, scope["query_string"].decode("ascii"))
+    return path_with_query_string
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/__init__.py
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/__pycache__/websockets_impl.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/__pycache__/websockets_impl.cpython-312.pyc
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/__pycache__/wsproto_impl.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/__pycache__/wsproto_impl.cpython-312.pyc
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..09e24071e8f9ab620662505e6b69d720dfcb0e77
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_impl.py
@@ -0,0 +1,387 @@
+from __future__ import annotations
+
+import asyncio
+import http
+import logging
+from collections.abc import Sequence
+from typing import Any, Literal, Optional, cast
+from urllib.parse import unquote
+
+import websockets
+import websockets.legacy.handshake
+from websockets.datastructures import Headers
+from websockets.exceptions import ConnectionClosed
+from websockets.extensions.base import ServerExtensionFactory
+from websockets.extensions.permessage_deflate import ServerPerMessageDeflateFactory
+from websockets.legacy.server import HTTPResponse
+from websockets.server import WebSocketServerProtocol
+from websockets.typing import Subprotocol
+
+from uvicorn._types import (
+    ASGI3Application,
+    ASGISendEvent,
+    WebSocketAcceptEvent,
+    WebSocketCloseEvent,
+    WebSocketConnectEvent,
+    WebSocketDisconnectEvent,
+    WebSocketReceiveEvent,
+    WebSocketResponseBodyEvent,
+    WebSocketResponseStartEvent,
+    WebSocketScope,
+    WebSocketSendEvent,
+)
+from uvicorn.config import Config
+from uvicorn.logging import TRACE_LOG_LEVEL
+from uvicorn.protocols.utils import (
+    ClientDisconnected,
+    get_client_addr,
+    get_local_addr,
+    get_path_with_query_string,
+    get_remote_addr,
+    is_ssl,
+)
+from uvicorn.server import ServerState
+
+
+class Server:
+    closing = False
+
+    def register(self, ws: WebSocketServerProtocol) -> None:
+        pass
+
+    def unregister(self, ws: WebSocketServerProtocol) -> None:
+        pass
+
+    def is_serving(self) -> bool:
+        return not self.closing
+
+
+class WebSocketProtocol(WebSocketServerProtocol):
+    extra_headers: list[tuple[str, str]]
+    logger: logging.Logger | logging.LoggerAdapter[Any]
+
+    def __init__(
+        self,
+        config: Config,
+        server_state: ServerState,
+        app_state: dict[str, Any],
+        _loop: asyncio.AbstractEventLoop | None = None,
+    ):
+        if not config.loaded:
+            config.load()
+
+        self.config = config
+        self.app = cast(ASGI3Application, config.loaded_app)
+        self.loop = _loop or asyncio.get_event_loop()
+        self.root_path = config.root_path
+        self.app_state = app_state
+
+        # Shared server state
+        self.connections = server_state.connections
+        self.tasks = server_state.tasks
+
+        # Connection state
+        self.transport: asyncio.Transport = None  # type: ignore[assignment]
+        self.server: tuple[str, int] | None = None
+        self.client: tuple[str, int] | None = None
+        self.scheme: Literal["wss", "ws"] = None  # type: ignore[assignment]
+
+        # Connection events
+        self.scope: WebSocketScope
+        self.handshake_started_event = asyncio.Event()
+        self.handshake_completed_event = asyncio.Event()
+        self.closed_event = asyncio.Event()
+        self.initial_response: HTTPResponse | None = None
+        self.connect_sent = False
+        self.lost_connection_before_handshake = False
+        self.accepted_subprotocol: Subprotocol | None = None
+
+        self.ws_server: Server = Server()  # type: ignore[assignment]
+
+        extensions: list[ServerExtensionFactory] = []
+        if self.config.ws_per_message_deflate:
+            extensions.append(ServerPerMessageDeflateFactory())
+
+        super().__init__(
+            ws_handler=self.ws_handler,
+            ws_server=self.ws_server,  # type: ignore[arg-type]
+            max_size=self.config.ws_max_size,
+            max_queue=self.config.ws_max_queue,
+            ping_interval=self.config.ws_ping_interval,
+            ping_timeout=self.config.ws_ping_timeout,
+            extensions=extensions,
+            logger=logging.getLogger("uvicorn.error"),
+        )
+        self.server_header = None
+        self.extra_headers = [
+            (name.decode("latin-1"), value.decode("latin-1")) for name, value in server_state.default_headers
+        ]
+
+    def connection_made(  # type: ignore[override]
+        self, transport: asyncio.Transport
+    ) -> None:
+        self.connections.add(self)
+        self.transport = transport
+        self.server = get_local_addr(transport)
+        self.client = get_remote_addr(transport)
+        self.scheme = "wss" if is_ssl(transport) else "ws"
+
+        if self.logger.isEnabledFor(TRACE_LOG_LEVEL):
+            prefix = "%s:%d - " % self.client if self.client else ""
+            self.logger.log(TRACE_LOG_LEVEL, "%sWebSocket connection made", prefix)
+
+        super().connection_made(transport)
+
+    def connection_lost(self, exc: Exception | None) -> None:
+        self.connections.remove(self)
+
+        if self.logger.isEnabledFor(TRACE_LOG_LEVEL):
+            prefix = "%s:%d - " % self.client if self.client else ""
+            self.logger.log(TRACE_LOG_LEVEL, "%sWebSocket connection lost", prefix)
+
+        self.lost_connection_before_handshake = not self.handshake_completed_event.is_set()
+        self.handshake_completed_event.set()
+        super().connection_lost(exc)
+        if exc is None:
+            self.transport.close()
+
+    def shutdown(self) -> None:
+        self.ws_server.closing = True
+        if self.handshake_completed_event.is_set():
+            self.fail_connection(1012)
+        else:
+            self.send_500_response()
+        self.transport.close()
+
+    def on_task_complete(self, task: asyncio.Task[None]) -> None:
+        self.tasks.discard(task)
+
+    async def process_request(self, path: str, request_headers: Headers) -> HTTPResponse | None:
+        """
+        This hook is called to determine if the websocket should return
+        an HTTP response and close.
+
+        Our behavior here is to start the ASGI application, and then wait
+        for either `accept` or `close` in order to determine if we should
+        close the connection.
+        """
+        path_portion, _, query_string = path.partition("?")
+
+        websockets.legacy.handshake.check_request(request_headers)
+
+        subprotocols: list[str] = []
+        for header in request_headers.get_all("Sec-WebSocket-Protocol"):
+            subprotocols.extend([token.strip() for token in header.split(",")])
+
+        asgi_headers = [
+            (name.encode("ascii"), value.encode("ascii", errors="surrogateescape"))
+            for name, value in request_headers.raw_items()
+        ]
+        path = unquote(path_portion)
+        full_path = self.root_path + path
+        full_raw_path = self.root_path.encode("ascii") + path_portion.encode("ascii")
+
+        self.scope = {
+            "type": "websocket",
+            "asgi": {"version": self.config.asgi_version, "spec_version": "2.4"},
+            "http_version": "1.1",
+            "scheme": self.scheme,
+            "server": self.server,
+            "client": self.client,
+            "root_path": self.root_path,
+            "path": full_path,
+            "raw_path": full_raw_path,
+            "query_string": query_string.encode("ascii"),
+            "headers": asgi_headers,
+            "subprotocols": subprotocols,
+            "state": self.app_state.copy(),
+            "extensions": {"websocket.http.response": {}},
+        }
+        task = self.loop.create_task(self.run_asgi())
+        task.add_done_callback(self.on_task_complete)
+        self.tasks.add(task)
+        await self.handshake_started_event.wait()
+        return self.initial_response
+
+    def process_subprotocol(
+        self, headers: Headers, available_subprotocols: Sequence[Subprotocol] | None
+    ) -> Subprotocol | None:
+        """
+        We override the standard 'process_subprotocol' behavior here so that
+        we return whatever subprotocol is sent in the 'accept' message.
+        """
+        return self.accepted_subprotocol
+
+    def send_500_response(self) -> None:
+        msg = b"Internal Server Error"
+        content = [
+            b"HTTP/1.1 500 Internal Server Error\r\ncontent-type: text/plain; charset=utf-8\r\n",
+            b"content-length: " + str(len(msg)).encode("ascii") + b"\r\n",
+            b"connection: close\r\n",
+            b"\r\n",
+            msg,
+        ]
+        self.transport.write(b"".join(content))
+        # Allow handler task to terminate cleanly, as websockets doesn't cancel it by
+        # itself (see https://github.com/encode/uvicorn/issues/920)
+        self.handshake_started_event.set()
+
+    async def ws_handler(self, protocol: WebSocketServerProtocol, path: str) -> Any:  # type: ignore[override]
+        """
+        This is the main handler function for the 'websockets' implementation
+        to call into. We just wait for close then return, and instead allow
+        'send' and 'receive' events to drive the flow.
+        """
+        self.handshake_completed_event.set()
+        await self.wait_closed()
+
+    async def run_asgi(self) -> None:
+        """
+        Wrapper around the ASGI callable, handling exceptions and unexpected
+        termination states.
+        """
+        try:
+            result = await self.app(self.scope, self.asgi_receive, self.asgi_send)  # type: ignore[func-returns-value]
+        except ClientDisconnected:  # pragma: full coverage
+            self.closed_event.set()
+            self.transport.close()
+        except BaseException:
+            self.closed_event.set()
+            self.logger.exception("Exception in ASGI application\n")
+            if not self.handshake_started_event.is_set():
+                self.send_500_response()
+            else:
+                await self.handshake_completed_event.wait()
+            self.transport.close()
+        else:
+            self.closed_event.set()
+            if not self.handshake_started_event.is_set():
+                self.logger.error("ASGI callable returned without sending handshake.")
+                self.send_500_response()
+                self.transport.close()
+            elif result is not None:
+                self.logger.error("ASGI callable should return None, but returned '%s'.", result)
+                await self.handshake_completed_event.wait()
+                self.transport.close()
+
+    async def asgi_send(self, message: ASGISendEvent) -> None:
+        message_type = message["type"]
+
+        if not self.handshake_started_event.is_set():
+            if message_type == "websocket.accept":
+                message = cast("WebSocketAcceptEvent", message)
+                self.logger.info(
+                    '%s - "WebSocket %s" [accepted]',
+                    get_client_addr(self.scope),
+                    get_path_with_query_string(self.scope),
+                )
+                self.initial_response = None
+                self.accepted_subprotocol = cast(Optional[Subprotocol], message.get("subprotocol"))
+                if "headers" in message:
+                    self.extra_headers.extend(
+                        # ASGI spec requires bytes
+                        # But for compatibility we need to convert it to strings
+                        (name.decode("latin-1"), value.decode("latin-1"))
+                        for name, value in message["headers"]
+                    )
+                self.handshake_started_event.set()
+
+            elif message_type == "websocket.close":
+                message = cast("WebSocketCloseEvent", message)
+                self.logger.info(
+                    '%s - "WebSocket %s" 403',
+                    get_client_addr(self.scope),
+                    get_path_with_query_string(self.scope),
+                )
+                self.initial_response = (http.HTTPStatus.FORBIDDEN, [], b"")
+                self.handshake_started_event.set()
+                self.closed_event.set()
+
+            elif message_type == "websocket.http.response.start":
+                message = cast("WebSocketResponseStartEvent", message)
+                self.logger.info(
+                    '%s - "WebSocket %s" %d',
+                    get_client_addr(self.scope),
+                    get_path_with_query_string(self.scope),
+                    message["status"],
+                )
+                # websockets requires the status to be an enum. look it up.
+                status = http.HTTPStatus(message["status"])
+                headers = [
+                    (name.decode("latin-1"), value.decode("latin-1")) for name, value in message.get("headers", [])
+                ]
+                self.initial_response = (status, headers, b"")
+                self.handshake_started_event.set()
+
+            else:
+                msg = (
+                    "Expected ASGI message 'websocket.accept', 'websocket.close', "
+                    "or 'websocket.http.response.start' but got '%s'."
+                )
+                raise RuntimeError(msg % message_type)
+
+        elif not self.closed_event.is_set() and self.initial_response is None:
+            await self.handshake_completed_event.wait()
+
+            try:
+                if message_type == "websocket.send":
+                    message = cast("WebSocketSendEvent", message)
+                    bytes_data = message.get("bytes")
+                    text_data = message.get("text")
+                    data = text_data if bytes_data is None else bytes_data
+                    await self.send(data)  # type: ignore[arg-type]
+
+                elif message_type == "websocket.close":
+                    message = cast("WebSocketCloseEvent", message)
+                    code = message.get("code", 1000)
+                    reason = message.get("reason", "") or ""
+                    await self.close(code, reason)
+                    self.closed_event.set()
+
+                else:
+                    msg = "Expected ASGI message 'websocket.send' or 'websocket.close', but got '%s'."
+                    raise RuntimeError(msg % message_type)
+            except ConnectionClosed as exc:
+                raise ClientDisconnected from exc
+
+        elif self.initial_response is not None:
+            if message_type == "websocket.http.response.body":
+                message = cast("WebSocketResponseBodyEvent", message)
+                body = self.initial_response[2] + message["body"]
+                self.initial_response = self.initial_response[:2] + (body,)
+                if not message.get("more_body", False):
+                    self.closed_event.set()
+            else:
+                msg = "Expected ASGI message 'websocket.http.response.body' but got '%s'."
+                raise RuntimeError(msg % message_type)
+
+        else:
+            msg = "Unexpected ASGI message '%s', after sending 'websocket.close' or response already completed."
+            raise RuntimeError(msg % message_type)
+
+    async def asgi_receive(self) -> WebSocketDisconnectEvent | WebSocketConnectEvent | WebSocketReceiveEvent:
+        if not self.connect_sent:
+            self.connect_sent = True
+            return {"type": "websocket.connect"}
+
+        await self.handshake_completed_event.wait()
+
+        if self.lost_connection_before_handshake:
+            # If the handshake failed or the app closed before handshake completion,
+            # use 1006 Abnormal Closure.
+            return {"type": "websocket.disconnect", "code": 1006}
+
+        if self.closed_event.is_set():
+            return {"type": "websocket.disconnect", "code": 1005}
+
+        try:
+            data = await self.recv()
+        except ConnectionClosed:
+            self.closed_event.set()
+            if self.ws_server.closing:
+                return {"type": "websocket.disconnect", "code": 1012}
+            return {"type": "websocket.disconnect", "code": self.close_code or 1005, "reason": self.close_reason}
+
+        if isinstance(data, str):
+            return {"type": "websocket.receive", "text": data}
+        return {"type": "websocket.receive", "bytes": data}
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_sansio_impl.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_sansio_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..abd4c2b30c3a3fb9570379348391d9683b381826
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/protocols/websockets/websockets_sansio_impl.py
@@ -0,0 +1,417 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+import sys
+from asyncio.transports import BaseTransport, Transport
+from http import HTTPStatus
+from typing import Any, Literal, cast
+from urllib.parse import unquote
+
+from websockets.exceptions import InvalidState
+from websockets.extensions.permessage_deflate import ServerPerMessageDeflateFactory
+from websockets.frames import Frame, Opcode
+from websockets.http11 import Request
+from websockets.server import ServerProtocol
+
+from uvicorn._types import (
+    ASGIReceiveEvent,
+    ASGISendEvent,
+    WebSocketAcceptEvent,
+    WebSocketCloseEvent,
+    WebSocketResponseBodyEvent,
+    WebSocketResponseStartEvent,
+    WebSocketScope,
+    WebSocketSendEvent,
+)
+from uvicorn.config import Config
+from uvicorn.logging import TRACE_LOG_LEVEL
+from uvicorn.protocols.utils import (
+    ClientDisconnected,
+    get_local_addr,
+    get_path_with_query_string,
+    get_remote_addr,
+    is_ssl,
+)
+from uvicorn.server import ServerState
+
+if sys.version_info >= (3, 11):  # pragma: no cover
+    from typing import assert_never
+else:  # pragma: no cover
+    from typing_extensions import assert_never
+
+
+class WebSocketsSansIOProtocol(asyncio.Protocol):
+    def __init__(
+        self,
+        config: Config,
+        server_state: ServerState,
+        app_state: dict[str, Any],
+        _loop: asyncio.AbstractEventLoop | None = None,
+    ) -> None:
+        if not config.loaded:
+            config.load()  # pragma: no cover
+
+        self.config = config
+        self.app = config.loaded_app
+        self.loop = _loop or asyncio.get_event_loop()
+        self.logger = logging.getLogger("uvicorn.error")
+        self.root_path = config.root_path
+        self.app_state = app_state
+
+        # Shared server state
+        self.connections = server_state.connections
+        self.tasks = server_state.tasks
+        self.default_headers = server_state.default_headers
+
+        # Connection state
+        self.transport: asyncio.Transport = None  # type: ignore[assignment]
+        self.server: tuple[str, int] | None = None
+        self.client: tuple[str, int] | None = None
+        self.scheme: Literal["wss", "ws"] = None  # type: ignore[assignment]
+
+        # WebSocket state
+        self.queue: asyncio.Queue[ASGIReceiveEvent] = asyncio.Queue()
+        self.handshake_initiated = False
+        self.handshake_complete = False
+        self.close_sent = False
+        self.initial_response: tuple[int, list[tuple[str, str]], bytes] | None = None
+
+        extensions = []
+        if self.config.ws_per_message_deflate:
+            extensions = [
+                ServerPerMessageDeflateFactory(
+                    server_max_window_bits=12,
+                    client_max_window_bits=12,
+                    compress_settings={"memLevel": 5},
+                )
+            ]
+        self.conn = ServerProtocol(
+            extensions=extensions,
+            max_size=self.config.ws_max_size,
+            logger=logging.getLogger("uvicorn.error"),
+        )
+
+        self.read_paused = False
+        self.writable = asyncio.Event()
+        self.writable.set()
+
+        # Buffers
+        self.bytes = b""
+
+    def connection_made(self, transport: BaseTransport) -> None:
+        """Called when a connection is made."""
+        transport = cast(Transport, transport)
+        self.connections.add(self)
+        self.transport = transport
+        self.server = get_local_addr(transport)
+        self.client = get_remote_addr(transport)
+        self.scheme = "wss" if is_ssl(transport) else "ws"
+
+        if self.logger.level <= TRACE_LOG_LEVEL:
+            prefix = "%s:%d - " % self.client if self.client else ""
+            self.logger.log(TRACE_LOG_LEVEL, "%sWebSocket connection made", prefix)
+
+    def connection_lost(self, exc: Exception | None) -> None:
+        code = 1005 if self.handshake_complete else 1006
+        self.queue.put_nowait({"type": "websocket.disconnect", "code": code})
+        self.connections.remove(self)
+
+        if self.logger.level <= TRACE_LOG_LEVEL:
+            prefix = "%s:%d - " % self.client if self.client else ""
+            self.logger.log(TRACE_LOG_LEVEL, "%sWebSocket connection lost", prefix)
+
+        self.handshake_complete = True
+        if exc is None:
+            self.transport.close()
+
+    def eof_received(self) -> None:
+        pass
+
+    def shutdown(self) -> None:
+        if self.handshake_complete:
+            self.queue.put_nowait({"type": "websocket.disconnect", "code": 1012})
+            self.conn.send_close(1012)
+            output = self.conn.data_to_send()
+            self.transport.write(b"".join(output))
+        else:
+            self.send_500_response()
+        self.transport.close()
+
+    def data_received(self, data: bytes) -> None:
+        self.conn.receive_data(data)
+        if self.conn.parser_exc is not None:  # pragma: no cover
+            self.handle_parser_exception()
+            return
+        self.handle_events()
+
+    def handle_events(self) -> None:
+        for event in self.conn.events_received():
+            if isinstance(event, Request):
+                self.handle_connect(event)
+            if isinstance(event, Frame):
+                if event.opcode == Opcode.CONT:
+                    self.handle_cont(event)  # pragma: no cover
+                elif event.opcode == Opcode.TEXT:
+                    self.handle_text(event)
+                elif event.opcode == Opcode.BINARY:
+                    self.handle_bytes(event)
+                elif event.opcode == Opcode.PING:
+                    self.handle_ping()
+                elif event.opcode == Opcode.PONG:
+                    pass  # pragma: no cover
+                elif event.opcode == Opcode.CLOSE:
+                    self.handle_close(event)
+                else:
+                    assert_never(event.opcode)  # pragma: no cover
+
+    # Event handlers
+
+    def handle_connect(self, event: Request) -> None:
+        self.request = event
+        self.response = self.conn.accept(event)
+        self.handshake_initiated = True
+        if self.response.status_code != 101:
+            self.handshake_complete = True
+            self.close_sent = True
+            self.conn.send_response(self.response)
+            output = self.conn.data_to_send()
+            self.transport.write(b"".join(output))
+            self.transport.close()
+            return
+
+        headers = [
+            (key.encode("ascii"), value.encode("ascii", errors="surrogateescape"))
+            for key, value in event.headers.raw_items()
+        ]
+        raw_path, _, query_string = event.path.partition("?")
+        self.scope: WebSocketScope = {
+            "type": "websocket",
+            "asgi": {"version": self.config.asgi_version, "spec_version": "2.3"},
+            "http_version": "1.1",
+            "scheme": self.scheme,
+            "server": self.server,
+            "client": self.client,
+            "root_path": self.root_path,
+            "path": unquote(raw_path),
+            "raw_path": raw_path.encode("ascii"),
+            "query_string": query_string.encode("ascii"),
+            "headers": headers,
+            "subprotocols": event.headers.get_all("Sec-WebSocket-Protocol"),
+            "state": self.app_state.copy(),
+            "extensions": {"websocket.http.response": {}},
+        }
+        self.queue.put_nowait({"type": "websocket.connect"})
+        task = self.loop.create_task(self.run_asgi())
+        task.add_done_callback(self.on_task_complete)
+        self.tasks.add(task)
+
+    def handle_cont(self, event: Frame) -> None:  # pragma: no cover
+        self.bytes += event.data
+        if event.fin:
+            self.send_receive_event_to_app()
+
+    def handle_text(self, event: Frame) -> None:
+        self.bytes = event.data
+        self.curr_msg_data_type: Literal["text", "bytes"] = "text"
+        if event.fin:
+            self.send_receive_event_to_app()
+
+    def handle_bytes(self, event: Frame) -> None:
+        self.bytes = event.data
+        self.curr_msg_data_type = "bytes"
+        if event.fin:
+            self.send_receive_event_to_app()
+
+    def send_receive_event_to_app(self) -> None:
+        if self.curr_msg_data_type == "text":
+            try:
+                self.queue.put_nowait({"type": "websocket.receive", "text": self.bytes.decode()})
+            except UnicodeDecodeError:  # pragma: no cover
+                self.logger.exception("Invalid UTF-8 sequence received from client.")
+                self.conn.send_close(1007)
+                self.handle_parser_exception()
+                return
+        else:
+            self.queue.put_nowait({"type": "websocket.receive", "bytes": self.bytes})
+        if not self.read_paused:
+            self.read_paused = True
+            self.transport.pause_reading()
+
+    def handle_ping(self) -> None:
+        output = self.conn.data_to_send()
+        self.transport.write(b"".join(output))
+
+    def handle_close(self, event: Frame) -> None:
+        if not self.close_sent and not self.transport.is_closing():
+            assert self.conn.close_rcvd is not None
+            code = self.conn.close_rcvd.code
+            reason = self.conn.close_rcvd.reason
+            self.queue.put_nowait({"type": "websocket.disconnect", "code": code, "reason": reason})
+
+            output = self.conn.data_to_send()
+            self.transport.write(b"".join(output))
+            self.transport.close()
+
+    def handle_parser_exception(self) -> None:  # pragma: no cover
+        assert self.conn.close_sent is not None
+        code = self.conn.close_sent.code
+        reason = self.conn.close_sent.reason
+        self.queue.put_nowait({"type": "websocket.disconnect", "code": code, "reason": reason})
+
+        output = self.conn.data_to_send()
+        self.transport.write(b"".join(output))
+        self.close_sent = True
+        self.transport.close()
+
+    def on_task_complete(self, task: asyncio.Task[None]) -> None:
+        self.tasks.discard(task)
+
+    async def run_asgi(self) -> None:
+        try:
+            result = await self.app(self.scope, self.receive, self.send)
+        except ClientDisconnected:
+            self.transport.close()  # pragma: no cover
+        except BaseException:
+            self.logger.exception("Exception in ASGI application\n")
+            self.send_500_response()
+            self.transport.close()
+        else:
+            if not self.handshake_complete:
+                self.logger.error("ASGI callable returned without completing handshake.")
+                self.send_500_response()
+                self.transport.close()
+            elif result is not None:
+                self.logger.error("ASGI callable should return None, but returned '%s'.", result)
+                self.transport.close()
+
+    def send_500_response(self) -> None:
+        if self.initial_response or self.handshake_complete:
+            return
+        response = self.conn.reject(500, "Internal Server Error")
+        self.conn.send_response(response)
+        output = self.conn.data_to_send()
+        self.transport.write(b"".join(output))
+
+    async def send(self, message: ASGISendEvent) -> None:
+        await self.writable.wait()
+
+        message_type = message["type"]
+
+        if not self.handshake_complete and self.initial_response is None:
+            if message_type == "websocket.accept":
+                message = cast(WebSocketAcceptEvent, message)
+                self.logger.info(
+                    '%s - "WebSocket %s" [accepted]',
+                    self.scope["client"],
+                    get_path_with_query_string(self.scope),
+                )
+                headers = [
+                    (name.decode("latin-1").lower(), value.decode("latin-1").lower())
+                    for name, value in (self.default_headers + list(message.get("headers", [])))
+                ]
+                accepted_subprotocol = message.get("subprotocol")
+                if accepted_subprotocol:
+                    headers.append(("Sec-WebSocket-Protocol", accepted_subprotocol))
+                self.response.headers.update(headers)
+
+                if not self.transport.is_closing():
+                    self.handshake_complete = True
+                    self.conn.send_response(self.response)
+                    output = self.conn.data_to_send()
+                    self.transport.write(b"".join(output))
+
+            elif message_type == "websocket.close":
+                message = cast(WebSocketCloseEvent, message)
+                self.queue.put_nowait({"type": "websocket.disconnect", "code": 1006})
+                self.logger.info(
+                    '%s - "WebSocket %s" 403',
+                    self.scope["client"],
+                    get_path_with_query_string(self.scope),
+                )
+                response = self.conn.reject(HTTPStatus.FORBIDDEN, "")
+                self.conn.send_response(response)
+                output = self.conn.data_to_send()
+                self.close_sent = True
+                self.handshake_complete = True
+                self.transport.write(b"".join(output))
+                self.transport.close()
+            elif message_type == "websocket.http.response.start" and self.initial_response is None:
+                message = cast(WebSocketResponseStartEvent, message)
+                if not (100 <= message["status"] < 600):
+                    raise RuntimeError("Invalid HTTP status code '%d' in response." % message["status"])
+                self.logger.info(
+                    '%s - "WebSocket %s" %d',
+                    self.scope["client"],
+                    get_path_with_query_string(self.scope),
+                    message["status"],
+                )
+                headers = [
+                    (name.decode("latin-1"), value.decode("latin-1"))
+                    for name, value in list(message.get("headers", []))
+                ]
+                self.initial_response = (message["status"], headers, b"")
+            else:
+                msg = (
+                    "Expected ASGI message 'websocket.accept', 'websocket.close' "
+                    "or 'websocket.http.response.start' "
+                    "but got '%s'."
+                )
+                raise RuntimeError(msg % message_type)
+
+        elif not self.close_sent and self.initial_response is None:
+            try:
+                if message_type == "websocket.send":
+                    message = cast(WebSocketSendEvent, message)
+                    bytes_data = message.get("bytes")
+                    text_data = message.get("text")
+                    if text_data:
+                        self.conn.send_text(text_data.encode())
+                    elif bytes_data:
+                        self.conn.send_binary(bytes_data)
+                    output = self.conn.data_to_send()
+                    self.transport.write(b"".join(output))
+
+                elif message_type == "websocket.close" and not self.transport.is_closing():
+                    message = cast(WebSocketCloseEvent, message)
+                    code = message.get("code", 1000)
+                    reason = message.get("reason", "") or ""
+                    self.queue.put_nowait({"type": "websocket.disconnect", "code": code, "reason": reason})
+                    self.conn.send_close(code, reason)
+                    output = self.conn.data_to_send()
+                    self.transport.write(b"".join(output))
+                    self.close_sent = True
+                    self.transport.close()
+                else:
+                    msg = "Expected ASGI message 'websocket.send' or 'websocket.close', but got '%s'."
+                    raise RuntimeError(msg % message_type)
+            except InvalidState:
+                raise ClientDisconnected()
+        elif self.initial_response is not None:
+            if message_type == "websocket.http.response.body":
+                message = cast(WebSocketResponseBodyEvent, message)
+                body = self.initial_response[2] + message["body"]
+                self.initial_response = self.initial_response[:2] + (body,)
+                if not message.get("more_body", False):
+                    response = self.conn.reject(self.initial_response[0], body.decode())
+                    response.headers.update(self.initial_response[1])
+                    self.queue.put_nowait({"type": "websocket.disconnect", "code": 1006})
+                    self.conn.send_response(response)
+                    output = self.conn.data_to_send()
+                    self.close_sent = True
+                    self.transport.write(b"".join(output))
+                    self.transport.close()
+            else:  # pragma: no cover
+                msg = "Expected ASGI message 'websocket.http.response.body' but got '%s'."
+                raise RuntimeError(msg % message_type)
+
+        else:
+            msg = "Unexpected ASGI message '%s', after sending 'websocket.close'."
+            raise RuntimeError(msg % message_type)
+
+    async def receive(self) -> ASGIReceiveEvent:
+        message = await self.queue.get()
+        if self.read_paused and self.queue.empty():
+            self.read_paused = False
+            self.transport.resume_reading()
+        return message
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfceb6b94ce36dad006b1f81b816d45ce70ab3d6
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/__init__.py
@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from uvicorn.supervisors.basereload import BaseReload
+from uvicorn.supervisors.multiprocess import Multiprocess
+
+if TYPE_CHECKING:
+    ChangeReload: type[BaseReload]
+else:
+    try:
+        from uvicorn.supervisors.watchfilesreload import WatchFilesReload as ChangeReload
+    except ImportError:  # pragma: no cover
+        from uvicorn.supervisors.statreload import StatReload as ChangeReload
+
+__all__ = ["Multiprocess", "ChangeReload"]
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/__pycache__/basereload.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/__pycache__/basereload.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..648e3406c9f120a3a204698df9a4411137838105
Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/__pycache__/basereload.cpython-312.pyc differ
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/__pycache__/multiprocess.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/__pycache__/multiprocess.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6a137b073152822bf6169af2e86effb0d979db95
Binary files /dev/null and b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/__pycache__/multiprocess.cpython-312.pyc differ
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/basereload.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/basereload.py
new file mode 100644
index 0000000000000000000000000000000000000000..eed999230228235f9bd4e532feef05d3108e5930
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/basereload.py
@@ -0,0 +1,126 @@
+from __future__ import annotations
+
+import logging
+import os
+import signal
+import sys
+import threading
+from collections.abc import Iterator
+from pathlib import Path
+from socket import socket
+from types import FrameType
+from typing import Callable
+
+import click
+
+from uvicorn._subprocess import get_subprocess
+from uvicorn.config import Config
+
+HANDLED_SIGNALS = (
+    signal.SIGINT,  # Unix signal 2. Sent by Ctrl+C.
+    signal.SIGTERM,  # Unix signal 15. Sent by `kill <pid>`.
+)
+
+logger = logging.getLogger("uvicorn.error")
+
+
+class BaseReload:
+    def __init__(
+        self,
+        config: Config,
+        target: Callable[[list[socket] | None], None],
+        sockets: list[socket],
+    ) -> None:
+        self.config = config
+        self.target = target
+        self.sockets = sockets
+        self.should_exit = threading.Event()
+        self.pid = os.getpid()
+        self.is_restarting = False
+        self.reloader_name: str | None = None
+
+    def signal_handler(self, sig: int, frame: FrameType | None) -> None:  # pragma: full coverage
+        """
+        A signal handler that is registered with the parent process.
+        """
+        if sys.platform == "win32" and self.is_restarting:
+            self.is_restarting = False
+        else:
+            self.should_exit.set()
+
+    def run(self) -> None:
+        self.startup()
+        for changes in self:
+            if changes:
+                logger.warning(
+                    "%s detected changes in %s. Reloading...",
+                    self.reloader_name,
+                    ", ".join(map(_display_path, changes)),
+                )
+                self.restart()
+
+        self.shutdown()
+
+    def pause(self) -> None:
+        if self.should_exit.wait(self.config.reload_delay):
+            raise StopIteration()
+
+    def __iter__(self) -> Iterator[list[Path] | None]:
+        return self
+
+    def __next__(self) -> list[Path] | None:
+        return self.should_restart()
+
+    def startup(self) -> None:
+        message = f"Started reloader process [{self.pid}] using {self.reloader_name}"
+        color_message = "Started reloader process [{}] using {}".format(
+            click.style(str(self.pid), fg="cyan", bold=True),
+            click.style(str(self.reloader_name), fg="cyan", bold=True),
+        )
+        logger.info(message, extra={"color_message": color_message})
+
+        for sig in HANDLED_SIGNALS:
+            signal.signal(sig, self.signal_handler)
+
+        self.process = get_subprocess(config=self.config, target=self.target, sockets=self.sockets)
+        self.process.start()
+
+    def restart(self) -> None:
+        if sys.platform == "win32":  # pragma: py-not-win32
+            self.is_restarting = True
+            assert self.process.pid is not None
+            os.kill(self.process.pid, signal.CTRL_C_EVENT)
+
+            # This is a workaround to ensure the Ctrl+C event is processed
+            sys.stdout.write(" ")  # This has to be a non-empty string
+            sys.stdout.flush()
+        else:  # pragma: py-win32
+            self.process.terminate()
+        self.process.join()
+
+        self.process = get_subprocess(config=self.config, target=self.target, sockets=self.sockets)
+        self.process.start()
+
+    def shutdown(self) -> None:
+        if sys.platform == "win32":
+            self.should_exit.set()  # pragma: py-not-win32
+        else:
+            self.process.terminate()  # pragma: py-win32
+        self.process.join()
+
+        for sock in self.sockets:
+            sock.close()
+
+        message = f"Stopping reloader process [{str(self.pid)}]"
+        color_message = "Stopping reloader process [{}]".format(click.style(str(self.pid), fg="cyan", bold=True))
+        logger.info(message, extra={"color_message": color_message})
+
+    def should_restart(self) -> list[Path] | None:
+        raise NotImplementedError("Reload strategies should override should_restart()")
+
+
+def _display_path(path: Path) -> str:
+    try:
+        return f"'{path.relative_to(Path.cwd())}'"
+    except ValueError:
+        return f"'{path}'"
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/multiprocess.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/multiprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..e198fe780bf5d24f7eba828a28a279d98133361c
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/multiprocess.py
@@ -0,0 +1,222 @@
+from __future__ import annotations
+
+import logging
+import os
+import signal
+import threading
+from multiprocessing import Pipe
+from socket import socket
+from typing import Any, Callable
+
+import click
+
+from uvicorn._subprocess import get_subprocess
+from uvicorn.config import Config
+
+SIGNALS = {
+    getattr(signal, f"SIG{x}"): x
+    for x in "INT TERM BREAK HUP QUIT TTIN TTOU USR1 USR2 WINCH".split()
+    if hasattr(signal, f"SIG{x}")
+}
+
+logger = logging.getLogger("uvicorn.error")
+
+
+class Process:
+    def __init__(
+        self,
+        config: Config,
+        target: Callable[[list[socket] | None], None],
+        sockets: list[socket],
+    ) -> None:
+        self.real_target = target
+
+        self.parent_conn, self.child_conn = Pipe()
+        self.process = get_subprocess(config, self.target, sockets)
+
+    def ping(self, timeout: float = 5) -> bool:
+        self.parent_conn.send(b"ping")
+        if self.parent_conn.poll(timeout):
+            self.parent_conn.recv()
+            return True
+        return False
+
+    def pong(self) -> None:
+        self.child_conn.recv()
+        self.child_conn.send(b"pong")
+
+    def always_pong(self) -> None:
+        while True:
+            self.pong()
+
+    def target(self, sockets: list[socket] | None = None) -> Any:  # pragma: no cover
+        if os.name == "nt":  # pragma: py-not-win32
+            # Windows doesn't support SIGTERM, so we use SIGBREAK instead.
+            # And then we raise SIGTERM when SIGBREAK is received.
+            # https://learn.microsoft.com/zh-cn/cpp/c-runtime-library/reference/signal?view=msvc-170
+            signal.signal(
+                signal.SIGBREAK,  # type: ignore[attr-defined]
+                lambda sig, frame: signal.raise_signal(signal.SIGTERM),
+            )
+
+        threading.Thread(target=self.always_pong, daemon=True).start()
+        return self.real_target(sockets)
+
+    def is_alive(self, timeout: float = 5) -> bool:
+        if not self.process.is_alive():
+            return False  # pragma: full coverage
+
+        return self.ping(timeout)
+
+    def start(self) -> None:
+        self.process.start()
+
+    def terminate(self) -> None:
+        if self.process.exitcode is None:  # Process is still running
+            assert self.process.pid is not None
+            if os.name == "nt":  # pragma: py-not-win32
+                # Windows doesn't support SIGTERM.
+                # So send SIGBREAK, and then in process raise SIGTERM.
+                os.kill(self.process.pid, signal.CTRL_BREAK_EVENT)  # type: ignore[attr-defined]
+            else:
+                os.kill(self.process.pid, signal.SIGTERM)
+            logger.info(f"Terminated child process [{self.process.pid}]")
+
+            self.parent_conn.close()
+            self.child_conn.close()
+
+    def kill(self) -> None:
+        # In Windows, the method will call `TerminateProcess` to kill the process.
+        # In Unix, the method will send SIGKILL to the process.
+        self.process.kill()
+
+    def join(self) -> None:
+        logger.info(f"Waiting for child process [{self.process.pid}]")
+        self.process.join()
+
+    @property
+    def pid(self) -> int | None:
+        return self.process.pid
+
+
+class Multiprocess:
+    def __init__(
+        self,
+        config: Config,
+        target: Callable[[list[socket] | None], None],
+        sockets: list[socket],
+    ) -> None:
+        self.config = config
+        self.target = target
+        self.sockets = sockets
+
+        self.processes_num = config.workers
+        self.processes: list[Process] = []
+
+        self.should_exit = threading.Event()
+
+        self.signal_queue: list[int] = []
+        for sig in SIGNALS:
+            signal.signal(sig, lambda sig, frame: self.signal_queue.append(sig))
+
+    def init_processes(self) -> None:
+        for _ in range(self.processes_num):
+            process = Process(self.config, self.target, self.sockets)
+            process.start()
+            self.processes.append(process)
+
+    def terminate_all(self) -> None:
+        for process in self.processes:
+            process.terminate()
+
+    def join_all(self) -> None:
+        for process in self.processes:
+            process.join()
+
+    def restart_all(self) -> None:
+        for idx, process in enumerate(self.processes):
+            process.terminate()
+            process.join()
+            new_process = Process(self.config, self.target, self.sockets)
+            new_process.start()
+            self.processes[idx] = new_process
+
+    def run(self) -> None:
+        message = f"Started parent process [{os.getpid()}]"
+        color_message = "Started parent process [{}]".format(click.style(str(os.getpid()), fg="cyan", bold=True))
+        logger.info(message, extra={"color_message": color_message})
+
+        self.init_processes()
+
+        while not self.should_exit.wait(0.5):
+            self.handle_signals()
+            self.keep_subprocess_alive()
+
+        self.terminate_all()
+        self.join_all()
+
+        message = f"Stopping parent process [{os.getpid()}]"
+        color_message = "Stopping parent process [{}]".format(click.style(str(os.getpid()), fg="cyan", bold=True))
+        logger.info(message, extra={"color_message": color_message})
+
+    def keep_subprocess_alive(self) -> None:
+        if self.should_exit.is_set():
+            return  # parent process is exiting, no need to keep subprocess alive
+
+        for idx, process in enumerate(self.processes):
+            if process.is_alive():
+                continue
+
+            process.kill()  # process is hung, kill it
+            process.join()
+
+            if self.should_exit.is_set():
+                return  # pragma: full coverage
+
+            logger.info(f"Child process [{process.pid}] died")
+            process = Process(self.config, self.target, self.sockets)
+            process.start()
+            self.processes[idx] = process
+
+    def handle_signals(self) -> None:
+        for sig in tuple(self.signal_queue):
+            self.signal_queue.remove(sig)
+            sig_name = SIGNALS[sig]
+            sig_handler = getattr(self, f"handle_{sig_name.lower()}", None)
+            if sig_handler is not None:
+                sig_handler()
+            else:  # pragma: no cover
+                logger.debug(f"Received signal {sig_name}, but no handler is defined for it.")
+
+    def handle_int(self) -> None:
+        logger.info("Received SIGINT, exiting.")
+        self.should_exit.set()
+
+    def handle_term(self) -> None:
+        logger.info("Received SIGTERM, exiting.")
+        self.should_exit.set()
+
+    def handle_break(self) -> None:  # pragma: py-not-win32
+        logger.info("Received SIGBREAK, exiting.")
+        self.should_exit.set()
+
+    def handle_hup(self) -> None:  # pragma: py-win32
+        logger.info("Received SIGHUP, restarting processes.")
+        self.restart_all()
+
+    def handle_ttin(self) -> None:  # pragma: py-win32
+        logger.info("Received SIGTTIN, increasing the number of processes.")
+        self.processes_num += 1
+        process = Process(self.config, self.target, self.sockets)
+        process.start()
+        self.processes.append(process)
+
+    def handle_ttou(self) -> None:  # pragma: py-win32
+        logger.info("Received SIGTTOU, decreasing number of processes.")
+        if self.processes_num <= 1:
+            logger.info("Already reached one process, cannot decrease the number of processes anymore.")
+            return
+        self.processes_num -= 1
+        process = self.processes.pop()
+        process.terminate()
+        process.join()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/statreload.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/statreload.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9cbd525b252cd843cb4020ba92425659653b32b
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/statreload.py
@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+import logging
+from collections.abc import Iterator
+from pathlib import Path
+from socket import socket
+from typing import Callable
+
+from uvicorn.config import Config
+from uvicorn.supervisors.basereload import BaseReload
+
+logger = logging.getLogger("uvicorn.error")
+
+
+class StatReload(BaseReload):
+    def __init__(
+        self,
+        config: Config,
+        target: Callable[[list[socket] | None], None],
+        sockets: list[socket],
+    ) -> None:
+        super().__init__(config, target, sockets)
+        self.reloader_name = "StatReload"
+        self.mtimes: dict[Path, float] = {}
+
+        if config.reload_excludes or config.reload_includes:
+            logger.warning("--reload-include and --reload-exclude have no effect unless watchfiles is installed.")
+
+    def should_restart(self) -> list[Path] | None:
+        self.pause()
+
+        for file in self.iter_py_files():
+            try:
+                mtime = file.stat().st_mtime
+            except OSError:  # pragma: nocover
+                continue
+
+            old_time = self.mtimes.get(file)
+            if old_time is None:
+                self.mtimes[file] = mtime
+                continue
+            elif mtime > old_time:
+                return [file]
+        return None
+
+    def restart(self) -> None:
+        self.mtimes = {}
+        return super().restart()
+
+    def iter_py_files(self) -> Iterator[Path]:
+        for reload_dir in self.config.reload_dirs:
+            for path in list(reload_dir.rglob("*.py")):
+                yield path.resolve()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/watchfilesreload.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/watchfilesreload.py
new file mode 100644
index 0000000000000000000000000000000000000000..c13dc745a42d8a3564ce50e483a20d9144648ee5
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvicorn/supervisors/watchfilesreload.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+from pathlib import Path
+from socket import socket
+from typing import Callable
+
+from watchfiles import watch
+
+from uvicorn.config import Config
+from uvicorn.supervisors.basereload import BaseReload
+
+
+class FileFilter:
+    def __init__(self, config: Config):
+        default_includes = ["*.py"]
+        self.includes = [default for default in default_includes if default not in config.reload_excludes]
+        self.includes.extend(config.reload_includes)
+        self.includes = list(set(self.includes))
+
+        default_excludes = [".*", ".py[cod]", ".sw.*", "~*"]
+        self.excludes = [default for default in default_excludes if default not in config.reload_includes]
+        self.exclude_dirs = []
+        for e in config.reload_excludes:
+            p = Path(e)
+            try:
+                is_dir = p.is_dir()
+            except OSError:  # pragma: no cover
+                # gets raised on Windows for values like "*.py"
+                is_dir = False
+
+            if is_dir:
+                self.exclude_dirs.append(p)
+            else:
+                self.excludes.append(e)  # pragma: full coverage
+        self.excludes = list(set(self.excludes))
+
+    def __call__(self, path: Path) -> bool:
+        for include_pattern in self.includes:
+            if path.match(include_pattern):
+                if str(path).endswith(include_pattern):
+                    return True  # pragma: full coverage
+
+                for exclude_dir in self.exclude_dirs:
+                    if exclude_dir in path.parents:
+                        return False
+
+                for exclude_pattern in self.excludes:
+                    if path.match(exclude_pattern):
+                        return False  # pragma: full coverage
+
+                return True
+        return False
+
+
+class WatchFilesReload(BaseReload):
+    def __init__(
+        self,
+        config: Config,
+        target: Callable[[list[socket] | None], None],
+        sockets: list[socket],
+    ) -> None:
+        super().__init__(config, target, sockets)
+        self.reloader_name = "WatchFiles"
+        self.reload_dirs = []
+        for directory in config.reload_dirs:
+            self.reload_dirs.append(directory)
+
+        self.watch_filter = FileFilter(config)
+        self.watcher = watch(
+            *self.reload_dirs,
+            watch_filter=None,
+            stop_event=self.should_exit,
+            # using yield_on_timeout here mostly to make sure tests don't
+            # hang forever, won't affect the class's behavior
+            yield_on_timeout=True,
+        )
+
+    def should_restart(self) -> list[Path] | None:
+        self.pause()
+
+        changes = next(self.watcher)
+        if changes:
+            unique_paths = {Path(c[1]) for c in changes}
+            return [p for p in unique_paths if self.watch_filter(p)]
+        return None
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/__pycache__/_noop.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/__pycache__/_noop.cpython-312.pyc
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/__pycache__/_version.cpython-312.pyc b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/__pycache__/_version.cpython-312.pyc
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/check.pxd b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/check.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..86cfd8fb8e065bc3f662395d571a4b43eaae2a87
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/check.pxd
@@ -0,0 +1,14 @@
+cdef class UVCheck(UVHandle):
+    cdef:
+        Handle h
+        bint running
+
+    # All "inline" methods are final
+
+    cdef _init(self, Loop loop, Handle h)
+
+    cdef inline stop(self)
+    cdef inline start(self)
+
+    @staticmethod
+    cdef UVCheck new(Loop loop, Handle h)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/handle.pyx b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/handle.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..2c96458b1c897fd9a88211b683dd44d2d329ec09
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/handle.pyx
@@ -0,0 +1,395 @@
+cdef class UVHandle:
+    """A base class for all libuv handles.
+
+    Automatically manages memory deallocation and closing.
+
+    Important:
+
+       1. call "_ensure_alive()" before calling any libuv functions on
+          your handles.
+
+       2. call "__ensure_handle_data" in *all* libuv handle callbacks.
+    """
+
+    def __cinit__(self):
+        self._closed = 0
+        self._inited = 0
+        self._has_handle = 1
+        self._handle = NULL
+        self._loop = None
+        self._source_traceback = None
+
+    def __init__(self):
+        raise TypeError(
+            '{} is not supposed to be instantiated from Python'.format(
+                self.__class__.__name__))
+
+    def __dealloc__(self):
+        if UVLOOP_DEBUG:
+            if self._loop is not None:
+                if self._inited:
+                    self._loop._debug_handles_current.subtract([
+                        self.__class__.__name__])
+            else:
+                # No "@cython.no_gc_clear" decorator on this UVHandle
+                raise RuntimeError(
+                    '{} without @no_gc_clear; loop was set to None by GC'
+                    .format(self.__class__.__name__))
+
+        if self._handle is NULL:
+            return
+
+        # -> When we're at this point, something is wrong <-
+
+        if self._handle.loop is NULL:
+            # The handle wasn't initialized with "uv_{handle}_init"
+            self._closed = 1
+            self._free()
+            raise RuntimeError(
+                '{} is open in __dealloc__ with loop set to NULL'
+                .format(self.__class__.__name__))
+
+        if self._closed:
+            # So _handle is not NULL and self._closed == 1?
+            raise RuntimeError(
+                '{}.__dealloc__: _handle is NULL, _closed == 1'.format(
+                    self.__class__.__name__))
+
+        # The handle is dealloced while open.  Let's try to close it.
+        # Situations when this is possible include unhandled exceptions,
+        # errors during Handle.__cinit__/__init__ etc.
+        if self._inited:
+            self._handle.data = NULL
+            uv.uv_close(self._handle, __uv_close_handle_cb)  # void; no errors
+            self._handle = NULL
+            self._warn_unclosed()
+        else:
+            # The handle was allocated, but not initialized
+            self._closed = 1
+            self._free()
+
+    cdef _free(self):
+        if self._handle == NULL:
+            return
+
+        if UVLOOP_DEBUG and self._inited:
+            self._loop._debug_uv_handles_freed += 1
+
+        PyMem_RawFree(self._handle)
+        self._handle = NULL
+
+    cdef _warn_unclosed(self):
+        if self._source_traceback is not None:
+            try:
+                tb = ''.join(tb_format_list(self._source_traceback))
+                tb = 'object created at (most recent call last):\n{}'.format(
+                    tb.rstrip())
+            except Exception as ex:
+                msg = (
+                    'unclosed resource {!r}; could not serialize '
+                    'debug traceback: {}: {}'
+                ).format(self, type(ex).__name__, ex)
+            else:
+                msg = 'unclosed resource {!r}; {}'.format(self, tb)
+        else:
+            msg = 'unclosed resource {!r}'.format(self)
+        warnings_warn(msg, ResourceWarning)
+
+    cdef inline _abort_init(self):
+        if self._handle is not NULL:
+            self._free()
+
+        try:
+            if UVLOOP_DEBUG:
+                name = self.__class__.__name__
+                if self._inited:
+                    raise RuntimeError(
+                        '_abort_init: {}._inited is set'.format(name))
+                if self._closed:
+                    raise RuntimeError(
+                        '_abort_init: {}._closed is set'.format(name))
+        finally:
+            self._closed = 1
+
+    cdef inline _finish_init(self):
+        self._inited = 1
+        if self._has_handle == 1:
+            self._handle.data = <void*>self
+        if self._loop._debug:
+            self._source_traceback = extract_stack()
+        if UVLOOP_DEBUG:
+            cls_name = self.__class__.__name__
+            self._loop._debug_uv_handles_total += 1
+            self._loop._debug_handles_total.update([cls_name])
+            self._loop._debug_handles_current.update([cls_name])
+
+    cdef inline _start_init(self, Loop loop):
+        if UVLOOP_DEBUG:
+            if self._loop is not None:
+                raise RuntimeError(
+                    '{}._start_init can only be called once'.format(
+                        self.__class__.__name__))
+
+        self._loop = loop
+
+    cdef inline bint _is_alive(self):
+        cdef bint res
+        res = self._closed != 1 and self._inited == 1
+        if UVLOOP_DEBUG:
+            if res and self._has_handle == 1:
+                name = self.__class__.__name__
+                if self._handle is NULL:
+                    raise RuntimeError(
+                        '{} is alive, but _handle is NULL'.format(name))
+                if self._loop is None:
+                    raise RuntimeError(
+                        '{} is alive, but _loop is None'.format(name))
+                if self._handle.loop is not self._loop.uvloop:
+                    raise RuntimeError(
+                        '{} is alive, but _handle.loop is not '
+                        'initialized'.format(name))
+                if self._handle.data is not <void*>self:
+                    raise RuntimeError(
+                        '{} is alive, but _handle.data is not '
+                        'initialized'.format(name))
+        return res
+
+    cdef inline _ensure_alive(self):
+        if not self._is_alive():
+            raise RuntimeError(
+                'unable to perform operation on {!r}; '
+                'the handler is closed'.format(self))
+
+    cdef _fatal_error(self, exc, throw, reason=None):
+        # Fatal error means an error that was returned by the
+        # underlying libuv handle function.  We usually can't
+        # recover from that, hence we just close the handle.
+        self._close()
+
+        if throw or self._loop is None:
+            raise exc
+        else:
+            self._loop._handle_exception(exc)
+
+    cdef _error(self, exc, throw):
+        # A non-fatal error is usually an error that was caught
+        # by the handler, but was originated in the client code
+        # (not in libuv).  In this case we either want to simply
+        # raise or log it.
+        if throw or self._loop is None:
+            raise exc
+        else:
+            self._loop._handle_exception(exc)
+
+    cdef _close(self):
+        if self._closed == 1:
+            return
+
+        self._closed = 1
+
+        if self._handle is NULL:
+            return
+
+        if UVLOOP_DEBUG:
+            if self._handle.data is NULL:
+                raise RuntimeError(
+                    '{}._close: _handle.data is NULL'.format(
+                        self.__class__.__name__))
+
+            if <object>self._handle.data is not self:
+                raise RuntimeError(
+                    '{}._close: _handle.data is not UVHandle/self'.format(
+                        self.__class__.__name__))
+
+            if uv.uv_is_closing(self._handle):
+                raise RuntimeError(
+                    '{}._close: uv_is_closing() is true'.format(
+                        self.__class__.__name__))
+
+        # We want the handle wrapper (UVHandle) to stay alive until
+        # the closing callback fires.
+        Py_INCREF(self)
+        uv.uv_close(self._handle, __uv_close_handle_cb)  # void; no errors
+
+    def __repr__(self):
+        return '<{} closed={} {:#x}>'.format(
+            self.__class__.__name__,
+            self._closed,
+            id(self))
+
+
+cdef class UVSocketHandle(UVHandle):
+
+    def __cinit__(self):
+        self._fileobj = None
+        self.__cached_socket = None
+
+    cdef _fileno(self):
+        cdef:
+            int fd
+            int err
+
+        self._ensure_alive()
+        err = uv.uv_fileno(self._handle, <uv.uv_os_fd_t*>&fd)
+        if err < 0:
+            raise convert_error(err)
+
+        return fd
+
+    cdef _new_socket(self):
+        raise NotImplementedError
+
+    cdef inline _get_socket(self):
+        if self.__cached_socket is not None:
+            return self.__cached_socket
+
+        if not self._is_alive():
+            return None
+
+        self.__cached_socket = self._new_socket()
+        if UVLOOP_DEBUG:
+            # We don't "dup" for the "__cached_socket".
+            assert self.__cached_socket.fileno() == self._fileno()
+        return self.__cached_socket
+
+    cdef inline _attach_fileobj(self, object file):
+        # When we create a TCP/PIPE/etc connection/server based on
+        # a Python file object, we need to close the file object when
+        # the uv handle is closed.
+        socket_inc_io_ref(file)
+        self._fileobj = file
+
+    cdef _close(self):
+        if self.__cached_socket is not None:
+            (<PseudoSocket>self.__cached_socket)._fd = -1
+
+        UVHandle._close(self)
+
+        try:
+            # This code will only run for transports created from
+            # Python sockets, i.e. with `loop.create_server(sock=sock)` etc.
+            if self._fileobj is not None:
+                if isinstance(self._fileobj, socket_socket):
+                    # Detaching the socket object is the ideal solution:
+                    # * libuv will actually close the FD;
+                    # * detach() call will reset FD for the Python socket
+                    #   object, which means that it won't be closed 2nd time
+                    #   when the socket object is GCed.
+                    #
+                    # No need to call `socket_dec_io_ref()`, as
+                    # `socket.detach()` ignores `socket._io_refs`.
+                    self._fileobj.detach()
+                else:
+                    try:
+                        # `socket.close()` will raise an EBADF because libuv
+                        # has already closed the underlying FD.
+                        self._fileobj.close()
+                    except OSError as ex:
+                        if ex.errno != errno_EBADF:
+                            raise
+        except Exception as ex:
+            self._loop.call_exception_handler({
+                'exception': ex,
+                'transport': self,
+                'message': f'could not close attached file object '
+                           f'{self._fileobj!r}',
+            })
+        finally:
+            self._fileobj = None
+
+    cdef _open(self, int sockfd):
+        raise NotImplementedError
+
+
+cdef inline bint __ensure_handle_data(uv.uv_handle_t* handle,
+                                      const char* handle_ctx):
+
+    cdef Loop loop
+
+    if UVLOOP_DEBUG:
+        if handle.loop is NULL:
+            raise RuntimeError(
+                'handle.loop is NULL in __ensure_handle_data')
+
+        if handle.loop.data is NULL:
+            raise RuntimeError(
+                'handle.loop.data is NULL in __ensure_handle_data')
+
+    if handle.data is NULL:
+        loop = <Loop>handle.loop.data
+        loop.call_exception_handler({
+            'message': '{} called with handle.data == NULL'.format(
+                handle_ctx.decode('latin-1'))
+        })
+        return 0
+
+    if handle.data is NULL:
+        # The underlying UVHandle object was GCed with an open uv_handle_t.
+        loop = <Loop>handle.loop.data
+        loop.call_exception_handler({
+            'message': '{} called after destroying the UVHandle'.format(
+                handle_ctx.decode('latin-1'))
+        })
+        return 0
+
+    return 1
+
+
+cdef void __uv_close_handle_cb(uv.uv_handle_t* handle) noexcept with gil:
+    cdef UVHandle h
+
+    if handle.data is NULL:
+        # The original UVHandle is long dead. Just free the mem of
+        # the uv_handle_t* handler.
+
+        if UVLOOP_DEBUG:
+            if handle.loop == NULL or handle.loop.data == NULL:
+                raise RuntimeError(
+                    '__uv_close_handle_cb: handle.loop is invalid')
+            (<Loop>handle.loop.data)._debug_uv_handles_freed += 1
+
+        PyMem_RawFree(handle)
+    else:
+        h = <UVHandle>handle.data
+        try:
+            if UVLOOP_DEBUG:
+                if not h._has_handle:
+                    raise RuntimeError(
+                        'has_handle=0 in __uv_close_handle_cb')
+                h._loop._debug_handles_closed.update([
+                    h.__class__.__name__])
+            h._free()
+        finally:
+            Py_DECREF(h)  # Was INCREFed in UVHandle._close
+
+
+cdef void __close_all_handles(Loop loop) noexcept:
+    uv.uv_walk(loop.uvloop,
+               __uv_walk_close_all_handles_cb,
+               <void*>loop)  # void
+
+
+cdef void __uv_walk_close_all_handles_cb(
+    uv.uv_handle_t* handle,
+    void* arg,
+) noexcept with gil:
+
+    cdef:
+        Loop loop = <Loop>arg
+        UVHandle h
+
+    if uv.uv_is_closing(handle):
+        # The handle is closed or is closing.
+        return
+
+    if handle.data is NULL:
+        # This shouldn't happen. Ever.
+        loop.call_exception_handler({
+            'message': 'handle.data is NULL in __close_all_handles_cb'
+        })
+        return
+
+    h = <UVHandle>handle.data
+    if not h._closed:
+        h._warn_unclosed()
+        h._close()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/idle.pxd b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/idle.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..cf7b19f64b46551d815c4e6d4f6545e3d70b2636
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/idle.pxd
@@ -0,0 +1,14 @@
+cdef class UVIdle(UVHandle):
+    cdef:
+        Handle h
+        bint running
+
+    # All "inline" methods are final
+
+    cdef _init(self, Loop loop, Handle h)
+
+    cdef inline stop(self)
+    cdef inline start(self)
+
+    @staticmethod
+    cdef UVIdle new(Loop loop, Handle h)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/pipe.pxd b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/pipe.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..56fc2658bb1e223bbc342283bdf641aba77d4733
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/pipe.pxd
@@ -0,0 +1,33 @@
+cdef class UnixServer(UVStreamServer):
+
+    cdef bind(self, str path)
+
+    @staticmethod
+    cdef UnixServer new(Loop loop, object protocol_factory, Server server,
+                        object backlog,
+                        object ssl,
+                        object ssl_handshake_timeout,
+                        object ssl_shutdown_timeout)
+
+
+cdef class UnixTransport(UVStream):
+
+    @staticmethod
+    cdef UnixTransport new(Loop loop, object protocol, Server server,
+                           object waiter, object context)
+
+    cdef connect(self, char* addr)
+
+
+cdef class ReadUnixTransport(UVStream):
+
+    @staticmethod
+    cdef ReadUnixTransport new(Loop loop, object protocol, Server server,
+                               object waiter)
+
+
+cdef class WriteUnixTransport(UVStream):
+
+    @staticmethod
+    cdef WriteUnixTransport new(Loop loop, object protocol, Server server,
+                                object waiter)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/pipe.pyx b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/pipe.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..4b95ed6e9deb41bf3f014b06af673f1a18a55557
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/pipe.pyx
@@ -0,0 +1,247 @@
+cdef __pipe_init_uv_handle(UVStream handle, Loop loop):
+    cdef int err
+
+    handle._handle = <uv.uv_handle_t*>PyMem_RawMalloc(sizeof(uv.uv_pipe_t))
+    if handle._handle is NULL:
+        handle._abort_init()
+        raise MemoryError()
+
+    # Initialize pipe handle with ipc=0.
+    # ipc=1 means that libuv will use recvmsg/sendmsg
+    # instead of recv/send.
+    err = uv.uv_pipe_init(handle._loop.uvloop,
+                          <uv.uv_pipe_t*>handle._handle,
+                          0)
+    # UV_HANDLE_READABLE allows calling uv_read_start() on this pipe
+    # even if it is O_WRONLY, see also #317, libuv/libuv#2058
+    handle._handle.flags |= uv.UV_INTERNAL_HANDLE_READABLE
+    if err < 0:
+        handle._abort_init()
+        raise convert_error(err)
+
+    handle._finish_init()
+
+
+cdef __pipe_open(UVStream handle, int fd):
+    cdef int err
+    err = uv.uv_pipe_open(<uv.uv_pipe_t *>handle._handle,
+                          <uv.uv_os_fd_t>fd)
+    if err < 0:
+        exc = convert_error(err)
+        raise exc
+
+
+cdef __pipe_get_socket(UVSocketHandle handle):
+    fileno = handle._fileno()
+    return PseudoSocket(uv.AF_UNIX, uv.SOCK_STREAM, 0, fileno)
+
+
+@cython.no_gc_clear
+cdef class UnixServer(UVStreamServer):
+
+    @staticmethod
+    cdef UnixServer new(Loop loop, object protocol_factory, Server server,
+                        object backlog,
+                        object ssl,
+                        object ssl_handshake_timeout,
+                        object ssl_shutdown_timeout):
+
+        cdef UnixServer handle
+        handle = UnixServer.__new__(UnixServer)
+        handle._init(loop, protocol_factory, server, backlog,
+                     ssl, ssl_handshake_timeout, ssl_shutdown_timeout)
+        __pipe_init_uv_handle(<UVStream>handle, loop)
+        return handle
+
+    cdef _new_socket(self):
+        return __pipe_get_socket(<UVSocketHandle>self)
+
+    cdef _open(self, int sockfd):
+        self._ensure_alive()
+        __pipe_open(<UVStream>self, sockfd)
+        self._mark_as_open()
+
+    cdef bind(self, str path):
+        cdef int err
+        self._ensure_alive()
+        err = uv.uv_pipe_bind(<uv.uv_pipe_t *>self._handle,
+                              path.encode())
+        if err < 0:
+            exc = convert_error(err)
+            self._fatal_error(exc, True)
+            return
+
+        self._mark_as_open()
+
+    cdef UVStream _make_new_transport(self, object protocol, object waiter,
+                                      object context):
+        cdef UnixTransport tr
+        tr = UnixTransport.new(self._loop, protocol, self._server, waiter,
+                               context)
+        return <UVStream>tr
+
+    cdef _close(self):
+        sock = self._fileobj
+        if sock is not None and sock in self._loop._unix_server_sockets:
+            path = sock.getsockname()
+        else:
+            path = None
+
+        UVStreamServer._close(self)
+
+        if path is not None:
+            prev_ino = self._loop._unix_server_sockets[sock]
+            del self._loop._unix_server_sockets[sock]
+            try:
+                if os_stat(path).st_ino == prev_ino:
+                    os_unlink(path)
+            except FileNotFoundError:
+                pass
+            except OSError as err:
+                aio_logger.error('Unable to clean up listening UNIX socket '
+                                 '%r: %r', path, err)
+
+
+@cython.no_gc_clear
+cdef class UnixTransport(UVStream):
+
+    @staticmethod
+    cdef UnixTransport new(Loop loop, object protocol, Server server,
+                           object waiter, object context):
+
+        cdef UnixTransport handle
+        handle = UnixTransport.__new__(UnixTransport)
+        handle._init(loop, protocol, server, waiter, context)
+        __pipe_init_uv_handle(<UVStream>handle, loop)
+        return handle
+
+    cdef _new_socket(self):
+        return __pipe_get_socket(<UVSocketHandle>self)
+
+    cdef _open(self, int sockfd):
+        __pipe_open(<UVStream>self, sockfd)
+
+    cdef connect(self, char* addr):
+        cdef _PipeConnectRequest req
+        req = _PipeConnectRequest(self._loop, self)
+        req.connect(addr)
+
+
+@cython.no_gc_clear
+cdef class ReadUnixTransport(UVStream):
+
+    @staticmethod
+    cdef ReadUnixTransport new(Loop loop, object protocol, Server server,
+                               object waiter):
+        cdef ReadUnixTransport handle
+        handle = ReadUnixTransport.__new__(ReadUnixTransport)
+        # This is only used in connect_read_pipe() and subprocess_shell/exec()
+        # directly, we could simply copy the current context.
+        handle._init(loop, protocol, server, waiter, Context_CopyCurrent())
+        __pipe_init_uv_handle(<UVStream>handle, loop)
+        return handle
+
+    cdef _new_socket(self):
+        return __pipe_get_socket(<UVSocketHandle>self)
+
+    cdef _open(self, int sockfd):
+        __pipe_open(<UVStream>self, sockfd)
+
+    def get_write_buffer_limits(self):
+        raise NotImplementedError
+
+    def set_write_buffer_limits(self, high=None, low=None):
+        raise NotImplementedError
+
+    def get_write_buffer_size(self):
+        raise NotImplementedError
+
+    def write(self, data):
+        raise NotImplementedError
+
+    def writelines(self, list_of_data):
+        raise NotImplementedError
+
+    def write_eof(self):
+        raise NotImplementedError
+
+    def can_write_eof(self):
+        raise NotImplementedError
+
+    def abort(self):
+        raise NotImplementedError
+
+
+@cython.no_gc_clear
+cdef class WriteUnixTransport(UVStream):
+
+    @staticmethod
+    cdef WriteUnixTransport new(Loop loop, object protocol, Server server,
+                                object waiter):
+        cdef WriteUnixTransport handle
+        handle = WriteUnixTransport.__new__(WriteUnixTransport)
+
+        # We listen for read events on write-end of the pipe. When
+        # the read-end is close, the uv_stream_t.read callback will
+        # receive an error -- we want to silence that error, and just
+        # close the transport.
+        handle._close_on_read_error()
+
+        # This is only used in connect_write_pipe() and subprocess_shell/exec()
+        # directly, we could simply copy the current context.
+        handle._init(loop, protocol, server, waiter, Context_CopyCurrent())
+        __pipe_init_uv_handle(<UVStream>handle, loop)
+        return handle
+
+    cdef _new_socket(self):
+        return __pipe_get_socket(<UVSocketHandle>self)
+
+    cdef _open(self, int sockfd):
+        __pipe_open(<UVStream>self, sockfd)
+
+    def pause_reading(self):
+        raise NotImplementedError
+
+    def resume_reading(self):
+        raise NotImplementedError
+
+
+cdef class _PipeConnectRequest(UVRequest):
+    cdef:
+        UnixTransport transport
+        uv.uv_connect_t _req_data
+
+    def __cinit__(self, loop, transport):
+        self.request = <uv.uv_req_t*> &self._req_data
+        self.request.data = <void*>self
+        self.transport = transport
+
+    cdef connect(self, char* addr):
+        # uv_pipe_connect returns void
+        uv.uv_pipe_connect(<uv.uv_connect_t*>self.request,
+                           <uv.uv_pipe_t*>self.transport._handle,
+                           addr,
+                           __pipe_connect_callback)
+
+cdef void __pipe_connect_callback(
+    uv.uv_connect_t* req,
+    int status,
+) noexcept with gil:
+    cdef:
+        _PipeConnectRequest wrapper
+        UnixTransport transport
+
+    wrapper = <_PipeConnectRequest> req.data
+    transport = wrapper.transport
+
+    if status < 0:
+        exc = convert_error(status)
+    else:
+        exc = None
+
+    try:
+        transport._on_connect(exc)
+    except BaseException as ex:
+        wrapper.transport._fatal_error(ex, False)
+    finally:
+        wrapper.on_done()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/stream.pyx b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/stream.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..9fbc5a51a41678c479f93482192d22fdb6e31e86
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/handles/stream.pyx
@@ -0,0 +1,1019 @@
+cdef extern from *:
+    '''
+    enum {__PREALLOCED_BUFS = 4};
+    '''
+    const bint __PREALLOCED_BUFS
+
+
+@cython.no_gc_clear
+@cython.freelist(DEFAULT_FREELIST_SIZE)
+cdef class _StreamWriteContext:
+    # used to hold additional write request information for uv_write
+
+    cdef:
+        uv.uv_write_t   req
+
+        list            buffers
+
+        uv.uv_buf_t     uv_bufs_sml[__PREALLOCED_BUFS]
+        Py_buffer       py_bufs_sml[__PREALLOCED_BUFS]
+        bint            py_bufs_sml_inuse
+
+        uv.uv_buf_t*    uv_bufs
+        Py_buffer*      py_bufs
+        size_t          py_bufs_len
+
+        uv.uv_buf_t*    uv_bufs_start
+        size_t          uv_bufs_len
+
+        UVStream        stream
+
+        bint            closed
+
+    cdef free_bufs(self):
+        cdef size_t i
+
+        if self.uv_bufs is not NULL:
+            PyMem_RawFree(self.uv_bufs)
+            self.uv_bufs = NULL
+            if UVLOOP_DEBUG:
+                if self.py_bufs_sml_inuse:
+                    raise RuntimeError(
+                        '_StreamWriteContext.close: uv_bufs != NULL and '
+                        'py_bufs_sml_inuse is True')
+
+        if self.py_bufs is not NULL:
+            for i from 0 <= i < self.py_bufs_len:
+                PyBuffer_Release(&self.py_bufs[i])
+            PyMem_RawFree(self.py_bufs)
+            self.py_bufs = NULL
+            if UVLOOP_DEBUG:
+                if self.py_bufs_sml_inuse:
+                    raise RuntimeError(
+                        '_StreamWriteContext.close: py_bufs != NULL and '
+                        'py_bufs_sml_inuse is True')
+
+        if self.py_bufs_sml_inuse:
+            for i from 0 <= i < self.py_bufs_len:
+                PyBuffer_Release(&self.py_bufs_sml[i])
+            self.py_bufs_sml_inuse = 0
+
+        self.py_bufs_len = 0
+        self.buffers = None
+
+    cdef close(self):
+        if self.closed:
+            return
+        self.closed = 1
+        self.free_bufs()
+        Py_DECREF(self)
+
+    cdef advance_uv_buf(self, size_t sent):
+        # Advance the pointer to first uv_buf and the
+        # pointer to first byte in that buffer.
+        #
+        # We do this after a "uv_try_write" call, which
+        # sometimes sends only a portion of data.
+        # We then call "advance_uv_buf" on the write
+        # context, and reuse it in a "uv_write" call.
+
+        cdef:
+            uv.uv_buf_t* buf
+            size_t idx
+
+        for idx from 0 <= idx < self.uv_bufs_len:
+            buf = &self.uv_bufs_start[idx]
+            if buf.len > sent:
+                buf.len -= sent
+                buf.base = buf.base + sent
+                self.uv_bufs_start = buf
+                self.uv_bufs_len -= idx
+                return
+            else:
+                sent -= self.uv_bufs_start[idx].len
+
+            if UVLOOP_DEBUG:
+                if sent < 0:
+                    raise RuntimeError('fatal: sent < 0 in advance_uv_buf')
+
+        raise RuntimeError('fatal: Could not advance _StreamWriteContext')
+
+    @staticmethod
+    cdef _StreamWriteContext new(UVStream stream, list buffers):
+        cdef:
+            _StreamWriteContext ctx
+            int uv_bufs_idx = 0
+            size_t py_bufs_len = 0
+            int i
+
+            Py_buffer* p_pybufs
+            uv.uv_buf_t* p_uvbufs
+
+        ctx = _StreamWriteContext.__new__(_StreamWriteContext)
+        ctx.stream = None
+        ctx.closed = 1
+        ctx.py_bufs_len = 0
+        ctx.py_bufs_sml_inuse = 0
+        ctx.uv_bufs = NULL
+        ctx.py_bufs = NULL
+        ctx.buffers = buffers
+        ctx.stream = stream
+
+        if len(buffers) <= __PREALLOCED_BUFS:
+            # We've got a small number of buffers to write, don't
+            # need to use malloc.
+            ctx.py_bufs_sml_inuse = 1
+            p_pybufs = <Py_buffer*>&ctx.py_bufs_sml
+            p_uvbufs = <uv.uv_buf_t*>&ctx.uv_bufs_sml
+
+        else:
+            for buf in buffers:
+                if UVLOOP_DEBUG:
+                    if not isinstance(buf, (bytes, bytearray, memoryview)):
+                        raise RuntimeError(
+                            'invalid data in writebuf: an instance of '
+                            'bytes, bytearray or memoryview was expected, '
+                            'got {}'.format(type(buf)))
+
+                if not PyBytes_CheckExact(buf):
+                    py_bufs_len += 1
+
+            if py_bufs_len > 0:
+                ctx.py_bufs = <Py_buffer*>PyMem_RawMalloc(
+                    py_bufs_len * sizeof(Py_buffer))
+                if ctx.py_bufs is NULL:
+                    raise MemoryError()
+
+            ctx.uv_bufs = <uv.uv_buf_t*>PyMem_RawMalloc(
+                len(buffers) * sizeof(uv.uv_buf_t))
+            if ctx.uv_bufs is NULL:
+                raise MemoryError()
+
+            p_pybufs = ctx.py_bufs
+            p_uvbufs = ctx.uv_bufs
+
+        py_bufs_len = 0
+        for buf in buffers:
+            if PyBytes_CheckExact(buf):
+                # We can only use this hack for bytes since it's
+                # immutable.  For everything else it is only safe to
+                # use buffer protocol.
+                p_uvbufs[uv_bufs_idx].base = PyBytes_AS_STRING(buf)
+                p_uvbufs[uv_bufs_idx].len = Py_SIZE(buf)
+
+            else:
+                try:
+                    PyObject_GetBuffer(
+                        buf, &p_pybufs[py_bufs_len], PyBUF_SIMPLE)
+                except Exception:
+                    # This shouldn't ever happen, as `UVStream._buffer_write`
+                    # casts non-bytes objects to `memoryviews`.
+                    ctx.py_bufs_len = py_bufs_len
+                    ctx.free_bufs()
+                    raise
+
+                p_uvbufs[uv_bufs_idx].base = <char*>p_pybufs[py_bufs_len].buf
+                p_uvbufs[uv_bufs_idx].len = p_pybufs[py_bufs_len].len
+
+                py_bufs_len += 1
+
+            uv_bufs_idx += 1
+
+        ctx.uv_bufs_start = p_uvbufs
+        ctx.uv_bufs_len = uv_bufs_idx
+
+        ctx.py_bufs_len = py_bufs_len
+        ctx.req.data = <void*> ctx
+
+        if UVLOOP_DEBUG:
+            stream._loop._debug_stream_write_ctx_total += 1
+            stream._loop._debug_stream_write_ctx_cnt += 1
+
+        # Do incref after everything else is done.
+        # Under no circumstances we want `ctx` to be GCed while
+        # libuv is still working with `ctx.uv_bufs`.
+        Py_INCREF(ctx)
+        ctx.closed = 0
+        return ctx
+
+    def __dealloc__(self):
+        if not self.closed:
+            # Because we do an INCREF in _StreamWriteContext.new,
+            # __dealloc__ shouldn't ever happen with `self.closed == 1`
+            raise RuntimeError(
+                'open _StreamWriteContext is being deallocated')
+
+        if UVLOOP_DEBUG:
+            if self.stream is not None:
+                self.stream._loop._debug_stream_write_ctx_cnt -= 1
+                self.stream = None
+
+
+@cython.no_gc_clear
+cdef class UVStream(UVBaseTransport):
+
+    def __cinit__(self):
+        self.__shutting_down = 0
+        self.__reading = 0
+        self.__read_error_close = 0
+        self.__buffered = 0
+        self._eof = 0
+        self._buffer = []
+        self._buffer_size = 0
+
+        self._protocol_get_buffer = None
+        self._protocol_buffer_updated = None
+
+        self._read_pybuf_acquired = False
+
+    cdef _set_protocol(self, object protocol):
+        if protocol is None:
+            raise TypeError('protocol is required')
+
+        UVBaseTransport._set_protocol(self, protocol)
+
+        if (hasattr(protocol, 'get_buffer') and
+                not isinstance(protocol, aio_Protocol)):
+            try:
+                self._protocol_get_buffer = protocol.get_buffer
+                self._protocol_buffer_updated = protocol.buffer_updated
+                self.__buffered = 1
+            except AttributeError:
+                pass
+        else:
+            self.__buffered = 0
+
+    cdef _clear_protocol(self):
+        UVBaseTransport._clear_protocol(self)
+        self._protocol_get_buffer = None
+        self._protocol_buffer_updated = None
+        self.__buffered = 0
+
+    cdef inline _shutdown(self):
+        cdef int err
+
+        if self.__shutting_down:
+            return
+        self.__shutting_down = 1
+
+        self._ensure_alive()
+
+        self._shutdown_req.data = <void*> self
+        err = uv.uv_shutdown(&self._shutdown_req,
+                             <uv.uv_stream_t*> self._handle,
+                             __uv_stream_on_shutdown)
+        if err < 0:
+            exc = convert_error(err)
+            self._fatal_error(exc, True)
+            return
+
+    cdef inline _accept(self, UVStream server):
+        cdef int err
+        self._ensure_alive()
+
+        err = uv.uv_accept(<uv.uv_stream_t*>server._handle,
+                           <uv.uv_stream_t*>self._handle)
+        if err < 0:
+            exc = convert_error(err)
+            self._fatal_error(exc, True)
+            return
+
+        self._on_accept()
+
+    cdef inline _close_on_read_error(self):
+        self.__read_error_close = 1
+
+    cdef bint _is_reading(self):
+        return self.__reading
+
+    cdef _start_reading(self):
+        cdef int err
+
+        if self._closing:
+            return
+
+        self._ensure_alive()
+
+        if self.__reading:
+            return
+
+        if self.__buffered:
+            err = uv.uv_read_start(<uv.uv_stream_t*>self._handle,
+                                   __uv_stream_buffered_alloc,
+                                   __uv_stream_buffered_on_read)
+        else:
+            err = uv.uv_read_start(<uv.uv_stream_t*>self._handle,
+                                   __loop_alloc_buffer,
+                                   __uv_stream_on_read)
+        if err < 0:
+            exc = convert_error(err)
+            self._fatal_error(exc, True)
+            return
+        else:
+            # UVStream must live until the read callback is called
+            self.__reading_started()
+
+    cdef inline __reading_started(self):
+        if self.__reading:
+            return
+        self.__reading = 1
+        Py_INCREF(self)
+
+    cdef inline __reading_stopped(self):
+        if not self.__reading:
+            return
+        self.__reading = 0
+        Py_DECREF(self)
+
+    cdef _stop_reading(self):
+        cdef int err
+
+        if not self.__reading:
+            return
+
+        self._ensure_alive()
+
+        # From libuv docs:
+        #    This function is idempotent and may be safely
+        #    called on a stopped stream.
+        err = uv.uv_read_stop(<uv.uv_stream_t*>self._handle)
+        if err < 0:
+            exc = convert_error(err)
+            self._fatal_error(exc, True)
+            return
+        else:
+            self.__reading_stopped()
+
+    cdef inline _try_write(self, object data):
+        cdef:
+            ssize_t written
+            bint used_buf = 0
+            Py_buffer py_buf
+            void* buf
+            size_t blen
+            int saved_errno
+            int fd
+
+        if (<uv.uv_stream_t*>self._handle).write_queue_size != 0:
+            raise RuntimeError(
+                'UVStream._try_write called with data in uv buffers')
+
+        if PyBytes_CheckExact(data):
+            # We can only use this hack for bytes since it's
+            # immutable.  For everything else it is only safe to
+            # use buffer protocol.
+            buf = <void*>PyBytes_AS_STRING(data)
+            blen = Py_SIZE(data)
+        else:
+            PyObject_GetBuffer(data, &py_buf, PyBUF_SIMPLE)
+            used_buf = 1
+            buf = py_buf.buf
+            blen = py_buf.len
+
+        if blen == 0:
+            # Empty data, do nothing.
+            return 0
+
+        fd = self._fileno()
+        # Use `unistd.h/write` directly, it's faster than
+        # uv_try_write -- less layers of code.  The error
+        # checking logic is copied from libuv.
+        written = system.write(fd, buf, blen)
+        while written == -1 and (
+                errno.errno == errno.EINTR or
+                (system.PLATFORM_IS_APPLE and
+                    errno.errno == errno.EPROTOTYPE)):
+            # From libuv code (unix/stream.c):
+            #   Due to a possible kernel bug at least in OS X 10.10 "Yosemite",
+            #   EPROTOTYPE can be returned while trying to write to a socket
+            #   that is shutting down. If we retry the write, we should get
+            #   the expected EPIPE instead.
+            written = system.write(fd, buf, blen)
+        saved_errno = errno.errno
+
+        if used_buf:
+            PyBuffer_Release(&py_buf)
+
+        if written < 0:
+            if saved_errno == errno.EAGAIN or \
+                    saved_errno == system.EWOULDBLOCK:
+                return -1
+            else:
+                exc = convert_error(-saved_errno)
+                self._fatal_error(exc, True)
+                return
+
+        if UVLOOP_DEBUG:
+            self._loop._debug_stream_write_tries += 1
+
+        if <size_t>written == blen:
+            return 0
+
+        return written
+
+    cdef inline _buffer_write(self, object data):
+        cdef int dlen
+
+        if not PyBytes_CheckExact(data):
+            data = memoryview(data).cast('b')
+
+        dlen = len(data)
+        if not dlen:
+            return
+
+        self._buffer_size += dlen
+        self._buffer.append(data)
+
+    cdef inline _initiate_write(self):
+        if (not self._protocol_paused and
+                (<uv.uv_stream_t*>self._handle).write_queue_size == 0 and
+                self._buffer_size > self._high_water):
+            # Fast-path.  If:
+            #   - the protocol isn't yet paused,
+            #   - there is no data in libuv buffers for this stream,
+            #   - the protocol will be paused if we continue to buffer data
+            #
+            # Then:
+            #   - Try to write all buffered data right now.
+            all_sent = self._exec_write()
+            if UVLOOP_DEBUG:
+                if self._buffer_size != 0 or self._buffer != []:
+                    raise RuntimeError(
+                        '_buffer_size is not 0 after a successful _exec_write')
+
+            # There is no need to call `_queue_write` anymore,
+            # as `uv_write` should be called already.
+
+            if not all_sent:
+                # If not all of the data was sent successfully,
+                # we might need to pause the protocol.
+                self._maybe_pause_protocol()
+
+        elif self._buffer_size > 0:
+            self._maybe_pause_protocol()
+            self._loop._queue_write(self)
+
+    cdef inline _exec_write(self):
+        cdef:
+            int err
+            int buf_len
+            _StreamWriteContext ctx = None
+
+        if self._closed:
+            # If the handle is closed, just return, it's too
+            # late to do anything.
+            return
+
+        buf_len = len(self._buffer)
+        if not buf_len:
+            return
+
+        if (<uv.uv_stream_t*>self._handle).write_queue_size == 0:
+            # libuv internal write buffers for this stream are empty.
+            if buf_len == 1:
+                # If we only have one piece of data to send, let's
+                # use our fast implementation of try_write.
+                data = self._buffer[0]
+                sent = self._try_write(data)
+
+                if sent is None:
+                    # A `self._fatal_error` was called.
+                    # It might not raise an exception under some
+                    # conditions.
+                    self._buffer_size = 0
+                    self._buffer.clear()
+                    if not self._closing:
+                        # This should never happen.
+                        raise RuntimeError(
+                            'stream is open after UVStream._try_write '
+                            'returned None')
+                    return
+
+                if sent == 0:
+                    # All data was successfully written.
+                    self._buffer_size = 0
+                    self._buffer.clear()
+                    # on_write will call "maybe_resume_protocol".
+                    self._on_write()
+                    return True
+
+                if sent > 0:
+                    if UVLOOP_DEBUG:
+                        if sent == len(data):
+                            raise RuntimeError(
+                                '_try_write sent all data and returned '
+                                'non-zero')
+
+                    if PyBytes_CheckExact(data):
+                        # Cast bytes to memoryview to avoid copying
+                        # data that wasn't sent.
+                        data = memoryview(data)
+                    data = data[sent:]
+
+                    self._buffer_size -= sent
+                    self._buffer[0] = data
+
+                # At this point it's either data was sent partially,
+                # or an EAGAIN has happened.
+
+            else:
+                ctx = _StreamWriteContext.new(self, self._buffer)
+
+                err = uv.uv_try_write(<uv.uv_stream_t*>self._handle,
+                                      ctx.uv_bufs_start,
+                                      ctx.uv_bufs_len)
+
+                if err > 0:
+                    # Some data was successfully sent.
+
+                    if <size_t>err == self._buffer_size:
+                        # Everything was sent.
+                        ctx.close()
+                        self._buffer.clear()
+                        self._buffer_size = 0
+                        # on_write will call "maybe_resume_protocol".
+                        self._on_write()
+                        return True
+
+                    try:
+                        # Advance pointers to uv_bufs in `ctx`,
+                        # we will reuse it soon for a uv_write
+                        # call.
+                        ctx.advance_uv_buf(<ssize_t>err)
+                    except Exception as ex:  # This should never happen.
+                        # Let's try to close the `ctx` anyways.
+                        ctx.close()
+                        self._fatal_error(ex, True)
+                        self._buffer.clear()
+                        self._buffer_size = 0
+                        return
+
+                elif err != uv.UV_EAGAIN:
+                    ctx.close()
+                    exc = convert_error(err)
+                    self._fatal_error(exc, True)
+                    self._buffer.clear()
+                    self._buffer_size = 0
+                    return
+
+                # fall through
+
+        if ctx is None:
+            ctx = _StreamWriteContext.new(self, self._buffer)
+
+        err = uv.uv_write(&ctx.req,
+                          <uv.uv_stream_t*>self._handle,
+                          ctx.uv_bufs_start,
+                          ctx.uv_bufs_len,
+                          __uv_stream_on_write)
+
+        self._buffer_size = 0
+        # Can't use `_buffer.clear()` here: `ctx` holds a reference to
+        # the `_buffer`.
+        self._buffer = []
+
+        if err < 0:
+            # close write context
+            ctx.close()
+
+            exc = convert_error(err)
+            self._fatal_error(exc, True)
+            return
+
+        self._maybe_resume_protocol()
+
+    cdef size_t _get_write_buffer_size(self):
+        if self._handle is NULL:
+            return 0
+        return ((<uv.uv_stream_t*>self._handle).write_queue_size +
+                self._buffer_size)
+
+    cdef _close(self):
+        try:
+            if self._read_pybuf_acquired:
+                # Should never happen. libuv always calls uv_alloc/uv_read
+                # in pairs.
+                self._loop.call_exception_handler({
+                    'transport': self,
+                    'message': 'XXX: an allocated buffer in transport._close()'
+                })
+                self._read_pybuf_acquired = 0
+                PyBuffer_Release(&self._read_pybuf)
+
+            self._stop_reading()
+        finally:
+            UVSocketHandle._close(<UVHandle>self)
+
+    cdef inline _on_accept(self):
+        # Ultimately called by __uv_stream_on_listen.
+        self._init_protocol()
+
+    cdef inline _on_eof(self):
+        # Any exception raised here will be caught in
+        # __uv_stream_on_read.
+
+        try:
+            meth = self._protocol.eof_received
+        except AttributeError:
+            keep_open = False
+        else:
+            keep_open = run_in_context(self.context, meth)
+
+        if keep_open:
+            # We're keeping the connection open so the
+            # protocol can write more, but we still can't
+            # receive more, so remove the reader callback.
+            self._stop_reading()
+        else:
+            self.close()
+
+    cdef inline _on_write(self):
+        self._maybe_resume_protocol()
+        if not self._get_write_buffer_size():
+            if self._closing:
+                self._schedule_call_connection_lost(None)
+            elif self._eof:
+                self._shutdown()
+
+    cdef inline _init(self, Loop loop, object protocol, Server server,
+                      object waiter, object context):
+        self.context = context
+        self._set_protocol(protocol)
+        self._start_init(loop)
+
+        if server is not None:
+            self._set_server(server)
+
+        if waiter is not None:
+            self._set_waiter(waiter)
+
+    cdef inline _on_connect(self, object exc):
+        # Called from __tcp_connect_callback (tcp.pyx) and
+        # __pipe_connect_callback (pipe.pyx).
+        if exc is None:
+            self._init_protocol()
+        else:
+            if self._waiter is None:
+                self._fatal_error(exc, False, "connect failed")
+            elif self._waiter.cancelled():
+                # Connect call was cancelled; just close the transport
+                # silently.
+                self._close()
+            elif self._waiter.done():
+                self._fatal_error(exc, False, "connect failed")
+            else:
+                self._waiter.set_exception(exc)
+                self._close()
+
+    # === Public API ===
+
+    def __repr__(self):
+        return '<{} closed={} reading={} {:#x}>'.format(
+            self.__class__.__name__,
+            self._closed,
+            self.__reading,
+            id(self))
+
+    def write(self, object buf):
+        self._ensure_alive()
+
+        if self._eof:
+            raise RuntimeError('Cannot call write() after write_eof()')
+        if not buf:
+            return
+        if self._conn_lost:
+            self._conn_lost += 1
+            return
+        self._buffer_write(buf)
+        self._initiate_write()
+
+    def writelines(self, bufs):
+        self._ensure_alive()
+
+        if self._eof:
+            raise RuntimeError('Cannot call writelines() after write_eof()')
+        if self._conn_lost:
+            self._conn_lost += 1
+            return
+        for buf in bufs:
+            self._buffer_write(buf)
+        self._initiate_write()
+
+    def write_eof(self):
+        self._ensure_alive()
+
+        if self._eof:
+            return
+
+        self._eof = 1
+        if not self._get_write_buffer_size():
+            self._shutdown()
+
+    def can_write_eof(self):
+        return True
+
+    def is_reading(self):
+        return self._is_reading()
+
+    def pause_reading(self):
+        if self._closing or not self._is_reading():
+            return
+        self._stop_reading()
+
+    def resume_reading(self):
+        if self._is_reading() or self._closing:
+            return
+        self._start_reading()
+
+
+cdef void __uv_stream_on_shutdown(uv.uv_shutdown_t* req,
+                                  int status) noexcept with gil:
+
+    # callback for uv_shutdown
+
+    if req.data is NULL:
+        aio_logger.error(
+            'UVStream.shutdown callback called with NULL req.data, status=%r',
+            status)
+        return
+
+    cdef UVStream stream = <UVStream> req.data
+
+    if status < 0 and status != uv.UV_ECANCELED:
+        # From libuv source code:
+        #     The ECANCELED error code is a lie, the shutdown(2) syscall is a
+        #     fait accompli at this point. Maybe we should revisit this in
+        #     v0.11.  A possible reason for leaving it unchanged is that it
+        #     informs the callee that the handle has been destroyed.
+
+        if UVLOOP_DEBUG:
+            stream._loop._debug_stream_shutdown_errors_total += 1
+
+        exc = convert_error(status)
+        stream._fatal_error(
+            exc, False, "error status in uv_stream_t.shutdown callback")
+        return
+
+
+cdef inline bint __uv_stream_on_read_common(
+    UVStream sc,
+    Loop loop,
+    ssize_t nread,
+):
+    if sc._closed:
+        # The stream was closed, there is no reason to
+        # do any work now.
+        sc.__reading_stopped()  # Just in case.
+        return True
+
+    if nread == uv.UV_EOF:
+        # From libuv docs:
+        #     The callee is responsible for stopping closing the stream
+        #     when an error happens by calling uv_read_stop() or uv_close().
+        #     Trying to read from the stream again is undefined.
+        try:
+            if UVLOOP_DEBUG:
+                loop._debug_stream_read_eof_total += 1
+
+            sc._stop_reading()
+            sc._on_eof()
+        except BaseException as ex:
+            if UVLOOP_DEBUG:
+                loop._debug_stream_read_eof_cb_errors_total += 1
+
+            sc._fatal_error(ex, False)
+        finally:
+            return True
+
+    if nread == 0:
+        # From libuv docs:
+        #     nread might be 0, which does not indicate an error or EOF.
+        #     This is equivalent to EAGAIN or EWOULDBLOCK under read(2).
+        return True
+
+    if nread < 0:
+        # From libuv docs:
+        #     The callee is responsible for stopping closing the stream
+        #     when an error happens by calling uv_read_stop() or uv_close().
+        #     Trying to read from the stream again is undefined.
+        #
+        # Therefore, we're closing the stream.  Since "UVHandle._close()"
+        # doesn't raise exceptions unless uvloop is built with DEBUG=1,
+        # we don't need try...finally here.
+
+        if UVLOOP_DEBUG:
+            loop._debug_stream_read_errors_total += 1
+
+        if sc.__read_error_close:
+            # Used for getting notified when a pipe is closed.
+            # See WriteUnixTransport for the explanation.
+            sc._on_eof()
+            return True
+
+        exc = convert_error(nread)
+        sc._fatal_error(
+            exc, False, "error status in uv_stream_t.read callback")
+        return True
+
+    return False
+
+
+cdef inline void __uv_stream_on_read_impl(
+    uv.uv_stream_t* stream,
+    ssize_t nread,
+    const uv.uv_buf_t* buf,
+):
+    cdef:
+        UVStream sc = <UVStream>stream.data
+        Loop loop = sc._loop
+
+    # It's OK to free the buffer early, since nothing will
+    # be able to touch it until this method is done.
+    __loop_free_buffer(loop)
+
+    if __uv_stream_on_read_common(sc, loop, nread):
+        return
+
+    try:
+        if UVLOOP_DEBUG:
+            loop._debug_stream_read_cb_total += 1
+
+        run_in_context1(
+            sc.context,
+            sc._protocol_data_received,
+            loop._recv_buffer[:nread],
+        )
+    except BaseException as exc:
+        if UVLOOP_DEBUG:
+            loop._debug_stream_read_cb_errors_total += 1
+
+        sc._fatal_error(exc, False)
+
+
+cdef inline void __uv_stream_on_write_impl(
+    uv.uv_write_t* req,
+    int status,
+):
+    cdef:
+        _StreamWriteContext ctx = <_StreamWriteContext> req.data
+        UVStream stream = <UVStream>ctx.stream
+
+    ctx.close()
+
+    if stream._closed:
+        # The stream was closed, there is nothing to do.
+        # Even if there is an error, like EPIPE, there
+        # is no reason to report it.
+        return
+
+    if status < 0:
+        if UVLOOP_DEBUG:
+            stream._loop._debug_stream_write_errors_total += 1
+
+        exc = convert_error(status)
+        stream._fatal_error(
+            exc, False, "error status in uv_stream_t.write callback")
+        return
+
+    try:
+        stream._on_write()
+    except BaseException as exc:
+        if UVLOOP_DEBUG:
+            stream._loop._debug_stream_write_cb_errors_total += 1
+
+        stream._fatal_error(exc, False)
+
+
+cdef void __uv_stream_on_read(
+    uv.uv_stream_t* stream,
+    ssize_t nread,
+    const uv.uv_buf_t* buf,
+) noexcept with gil:
+
+    if __ensure_handle_data(<uv.uv_handle_t*>stream,
+                            "UVStream read callback") == 0:
+        return
+
+    # Don't need try-finally, __uv_stream_on_read_impl is void
+    __uv_stream_on_read_impl(stream, nread, buf)
+
+
+cdef void __uv_stream_on_write(
+    uv.uv_write_t* req,
+    int status,
+) noexcept with gil:
+
+    if UVLOOP_DEBUG:
+        if req.data is NULL:
+            aio_logger.error(
+                'UVStream.write callback called with NULL req.data, status=%r',
+                status)
+            return
+
+    # Don't need try-finally, __uv_stream_on_write_impl is void
+    __uv_stream_on_write_impl(req, status)
+
+
+cdef void __uv_stream_buffered_alloc(
+    uv.uv_handle_t* stream,
+    size_t suggested_size,
+    uv.uv_buf_t* uvbuf,
+) noexcept with gil:
+
+    if __ensure_handle_data(<uv.uv_handle_t*>stream,
+                            "UVStream alloc buffer callback") == 0:
+        return
+
+    cdef:
+        UVStream sc = <UVStream>stream.data
+        Loop loop = sc._loop
+        Py_buffer* pybuf = &sc._read_pybuf
+        int got_buf = 0
+
+    if sc._read_pybuf_acquired:
+        uvbuf.len = 0
+        uvbuf.base = NULL
+        return
+
+    sc._read_pybuf_acquired = 0
+    try:
+        buf = run_in_context1(
+            sc.context,
+            sc._protocol_get_buffer,
+            suggested_size,
+        )
+        PyObject_GetBuffer(buf, pybuf, PyBUF_WRITABLE)
+        got_buf = 1
+    except BaseException as exc:
+        # Can't call 'sc._fatal_error' or 'sc._close', libuv will SF.
+        # We'll do it later in __uv_stream_buffered_on_read when we
+        # receive UV_ENOBUFS.
+        uvbuf.len = 0
+        uvbuf.base = NULL
+        return
+
+    if not pybuf.len:
+        uvbuf.len = 0
+        uvbuf.base = NULL
+        if got_buf:
+            PyBuffer_Release(pybuf)
+        return
+
+    sc._read_pybuf_acquired = 1
+    uvbuf.base = <char*>pybuf.buf
+    uvbuf.len = pybuf.len
+
+
+cdef void __uv_stream_buffered_on_read(
+    uv.uv_stream_t* stream,
+    ssize_t nread,
+    const uv.uv_buf_t* buf,
+) noexcept with gil:
+
+    if __ensure_handle_data(<uv.uv_handle_t*>stream,
+                            "UVStream buffered read callback") == 0:
+        return
+
+    cdef:
+        UVStream sc = <UVStream>stream.data
+        Loop loop = sc._loop
+        Py_buffer* pybuf = &sc._read_pybuf
+
+    if nread == uv.UV_ENOBUFS:
+        sc._fatal_error(
+            RuntimeError(
+                'unhandled error (or an empty buffer) in get_buffer()'),
+            False)
+        return
+
+    try:
+        if nread > 0 and not sc._read_pybuf_acquired:
+            # From libuv docs:
+            #     nread is > 0 if there is data available or < 0 on error. When
+            #     we’ve reached EOF, nread will be set to UV_EOF. When
+            #     nread < 0, the buf parameter might not point to a valid
+            #     buffer; in that case buf.len and buf.base are both set to 0.
+            raise RuntimeError(
+                f'no python buffer is allocated in on_read; nread={nread}')
+
+        if nread == 0:
+            # From libuv docs:
+            #     nread might be 0, which does not indicate an error or EOF.
+            #     This is equivalent to EAGAIN or EWOULDBLOCK under read(2).
+            return
+
+        if __uv_stream_on_read_common(sc, loop, nread):
+            return
+
+        if UVLOOP_DEBUG:
+            loop._debug_stream_read_cb_total += 1
+
+        run_in_context1(sc.context, sc._protocol_buffer_updated, nread)
+    except BaseException as exc:
+        if UVLOOP_DEBUG:
+            loop._debug_stream_read_cb_errors_total += 1
+
+        sc._fatal_error(exc, False)
+    finally:
+        sc._read_pybuf_acquired = 0
+        PyBuffer_Release(pybuf)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/includes/flowcontrol.pxd b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/includes/flowcontrol.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..f22f1a7a541cb681b4c6f0f034bb1be248d189f1
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/includes/flowcontrol.pxd
@@ -0,0 +1,23 @@
+# flake8: noqa
+
+
+cdef inline add_flowcontrol_defaults(high, low, int kb):
+    cdef int h, l
+    if high is None:
+        if low is None:
+            h = kb * 1024
+        else:
+            l = low
+            h = 4 * l
+    else:
+        h = high
+    if low is None:
+        l = h // 4
+    else:
+        l = low
+
+    if not h >= l >= 0:
+        raise ValueError('high (%r) must be >= low (%r) must be >= 0' %
+                         (h, l))
+
+    return h, l
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/includes/python.pxd b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/includes/python.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..94007e537381bb41af0749b1e67896db84e0266e
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/uvloop/includes/python.pxd
@@ -0,0 +1,31 @@
+cdef extern from "Python.h":
+    int PY_VERSION_HEX
+
+    unicode PyUnicode_FromString(const char *)
+
+    void* PyMem_RawMalloc(size_t n) nogil
+    void* PyMem_RawRealloc(void *p, size_t n) nogil
+    void* PyMem_RawCalloc(size_t nelem, size_t elsize) nogil
+    void PyMem_RawFree(void *p) nogil
+
+    object PyUnicode_EncodeFSDefault(object)
+    void PyErr_SetInterrupt() nogil
+
+    object PyMemoryView_FromMemory(char *mem, ssize_t size, int flags)
+    object PyMemoryView_FromObject(object obj)
+    int PyMemoryView_Check(object obj)
+
+    cdef enum:
+        PyBUF_WRITE
+
+
+cdef extern from "includes/compat.h":
+    object Context_CopyCurrent()
+    int Context_Enter(object) except -1
+    int Context_Exit(object) except -1
+
+    void PyOS_BeforeFork()
+    void PyOS_AfterFork_Parent()
+    void PyOS_AfterFork_Child()
+
+    void _Py_RestoreSignals()
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/dual_chunk_flash_attn.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/dual_chunk_flash_attn.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa6f3f1b39cca3160a38f9e172d776e0b3c490a0
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/dual_chunk_flash_attn.py
@@ -0,0 +1,1498 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Attention layer with Dual chunk flash attention and sparse attention.
+"""
+import math
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type
+
+import torch
+import torch.distributed
+import torch.nn.functional as F
+
+from vllm import _custom_ops as ops
+from vllm.attention.backends.abstract import AttentionLayer, AttentionType
+from vllm.attention.backends.flash_attn import (FlashAttentionBackend,
+                                                FlashAttentionImpl,
+                                                FlashAttentionMetadata,
+                                                FlashAttentionMetadataBuilder)
+from vllm.distributed.parallel_state import get_tensor_model_parallel_rank
+from vllm.logger import init_logger
+from vllm.utils import async_tensor_h2d
+from vllm.vllm_flash_attn import (flash_attn_varlen_func,
+                                  flash_attn_with_kvcache, sparse_attn_func)
+
+if TYPE_CHECKING:
+    from vllm.worker.model_runner import ModelInputForGPUBuilder
+
+logger = init_logger(__name__)
+
+
+class DualChunkFlashAttentionBackend(FlashAttentionBackend):
+
+    accept_output_buffer: bool = False
+
+    @staticmethod
+    def get_name() -> str:
+        return "DUAL_CHUNK_FLASH_ATTN"
+
+    @staticmethod
+    def get_impl_cls() -> Type["DualChunkFlashAttentionImpl"]:
+        return DualChunkFlashAttentionImpl
+
+    @staticmethod
+    def get_metadata_cls() -> Type["DualChunkFlashAttentionMetadata"]:
+        return DualChunkFlashAttentionMetadata
+
+    @staticmethod
+    def get_builder_cls() -> Type["DualChunkFlashAttentionMetadataBuilder"]:
+        return DualChunkFlashAttentionMetadataBuilder
+
+
+@dataclass
+class DualChunkFlashAttentionMetadata(FlashAttentionMetadata):
+    # Block size of the paged kv cache.
+    block_size: int = 16
+
+    # Original max position embeddings.
+    original_max_position_embeddings: int = 0
+
+    # Chunk size
+    chunk_size: int = 8192
+
+    # Local size
+    local_size: int = 1024
+
+    # (batch_size,). The orig sequence length per sequence.
+    orig_seq_lens: Optional[List[int]] = None
+
+    # orig_seq_lens stored as a tensor.
+    orig_seq_lens_tensor: Optional[torch.Tensor] = None
+
+    # Length scaling factor
+    scaling_factor: Optional[torch.Tensor] = None
+
+    # (batch_size,). Sequence lengths for intra attention.
+    seq_lens_intra: Optional[torch.Tensor] = None
+
+    # Max sequence length for intra attention.
+    max_seq_len_intra: Optional[int] = None
+
+    # (batch_size, num_blocks). Block table for intra attention.
+    block_tables_intra: Optional[torch.Tensor] = None
+
+    # (batch_size,). Sequence lengths for succ attention.
+    seq_lens_succ: Optional[torch.Tensor] = None
+
+    # Max sequence length for succ attention.
+    max_seq_len_succ: Optional[int] = None
+
+    # (batch_size, num_blocks). Block table for succ attention.
+    block_tables_succ: Optional[torch.Tensor] = None
+
+    # (batch_size,). Sequence lengths for inter attention.
+    seq_lens_inter: Optional[torch.Tensor] = None
+
+    # Max sequence length for inter attention.
+    max_seq_len_inter: Optional[int] = None
+
+    _cached_prefill_metadata: Optional[
+        "DualChunkFlashAttentionMetadata"] = None
+    _cached_decode_metadata: Optional["DualChunkFlashAttentionMetadata"] = None
+
+    @property
+    def prefill_metadata(self) -> Optional["DualChunkFlashAttentionMetadata"]:
+        if self.num_prefills == 0:
+            return None
+
+        if self._cached_prefill_metadata is not None:
+            return self._cached_prefill_metadata
+
+        prefill_metadata = super().prefill_metadata
+        if prefill_metadata is None:
+            return None
+
+        prefill_metadata = DualChunkFlashAttentionMetadata(
+            **prefill_metadata.asdict_zerocopy())
+
+        prefill_metadata.orig_seq_lens = (
+            None if self.orig_seq_lens is None else
+            self.orig_seq_lens[:self.num_prefills])
+        prefill_metadata.orig_seq_lens_tensor = (
+            None if self.orig_seq_lens_tensor is None else
+            self.orig_seq_lens_tensor[:self.num_prefills])
+
+        if self.original_max_position_embeddings > 0:
+            assert prefill_metadata.orig_seq_lens_tensor is not None
+            prefill_metadata.scaling_factor = (
+                0.1 * torch.log(prefill_metadata.orig_seq_lens_tensor /
+                                self.original_max_position_embeddings) +
+                1.0).clip(min=1)
+
+        self._cached_prefill_metadata = prefill_metadata
+        return prefill_metadata
+
+    @property
+    def decode_metadata(self) -> Optional["DualChunkFlashAttentionMetadata"]:
+        if self.num_decode_tokens == 0:
+            return None
+
+        if self._cached_decode_metadata is not None:
+            return self._cached_decode_metadata
+
+        decode_metadata = super().decode_metadata
+        if decode_metadata is None:
+            return None
+
+        decode_metadata = DualChunkFlashAttentionMetadata(
+            **decode_metadata.asdict_zerocopy())
+
+        decode_metadata.orig_seq_lens_tensor = (
+            None if self.orig_seq_lens_tensor is None else
+            self.orig_seq_lens_tensor[self.num_prefills:])
+
+        assert decode_metadata.orig_seq_lens_tensor is not None
+        assert decode_metadata.block_tables is not None
+
+        cache_seq_lens = decode_metadata.orig_seq_lens_tensor
+        chunk_len = self.chunk_size - self.local_size
+        chunk_num_curr = (cache_seq_lens - 1) // chunk_len
+        batch_size = decode_metadata.num_decode_tokens
+
+        if self.original_max_position_embeddings > 0:
+            decode_metadata.scaling_factor = (0.1 * torch.log(
+                cache_seq_lens / self.original_max_position_embeddings) +
+                                              1.0).clip(min=1)
+
+        seq_lens_intra = cache_seq_lens - chunk_num_curr * chunk_len
+        max_seq_len_intra = seq_lens_intra.max().item()
+        decode_metadata.seq_lens_intra = seq_lens_intra
+        decode_metadata.max_seq_len_intra = max_seq_len_intra
+
+        block_tables_intra = torch.zeros(
+            batch_size,
+            (max_seq_len_intra - 1) // self.block_size + 1,
+            dtype=decode_metadata.block_tables.dtype,
+            device=decode_metadata.block_tables.device,
+        )
+        for i in range(batch_size):
+            st = chunk_num_curr[i] * chunk_len // self.block_size
+            ed = min(
+                st + (max_seq_len_intra - 1) // self.block_size + 1,
+                (cache_seq_lens[i] - 1) // self.block_size + 1,
+            )
+            block_tables_intra[i, :ed -
+                               st] = decode_metadata.block_tables[i, st:ed]
+        decode_metadata.block_tables_intra = block_tables_intra
+
+        seq_lens_succ = (chunk_num_curr -
+                         (chunk_num_curr - 1).clip(min=0)) * chunk_len
+        max_seq_len_succ = seq_lens_succ.max().item()
+        decode_metadata.seq_lens_succ = seq_lens_succ
+        decode_metadata.max_seq_len_succ = max_seq_len_succ
+        if max_seq_len_succ:
+            block_tables_succ = torch.zeros(
+                batch_size,
+                (max_seq_len_succ - 1) // self.block_size + 1,
+                dtype=decode_metadata.block_tables.dtype,
+                device=decode_metadata.block_tables.device,
+            )
+            for i in range(batch_size):
+                start = ((chunk_num_curr[i] - 1).clip(min=0) * chunk_len //
+                         self.block_size)
+                end = min(
+                    start + (max_seq_len_succ - 1) // self.block_size + 1,
+                    (cache_seq_lens[i] - 1) // self.block_size + 1,
+                )
+                block_tables_succ[
+                    i, :end - start] = decode_metadata.block_tables[i,
+                                                                    start:end]
+            decode_metadata.block_tables_succ = block_tables_succ
+
+        seq_lens_inter = (chunk_num_curr - 1).clip(min=0) * chunk_len
+        max_seq_len_inter = seq_lens_inter.max().item()
+        decode_metadata.seq_lens_inter = seq_lens_inter
+        decode_metadata.max_seq_len_inter = max_seq_len_inter
+
+        self._cached_decode_metadata = decode_metadata
+        return decode_metadata
+
+
+class DualChunkFlashAttentionMetadataBuilder(FlashAttentionMetadataBuilder):
+
+    def prepare(self):
+        super().prepare()
+        self.orig_seq_lens: List[int] = []
+
+    def _add_seq_group(
+            self, inter_data: "ModelInputForGPUBuilder.InterDataForSeqGroup",
+            chunked_prefill_enabled: bool, prefix_cache_hit: bool):
+        super()._add_seq_group(inter_data, chunked_prefill_enabled,
+                               prefix_cache_hit)
+        for prompt_len, seq_len in zip(inter_data.prompt_lens,
+                                       inter_data.seq_lens):
+            self.orig_seq_lens.append(max(prompt_len, seq_len))
+
+    def build(self, seq_lens: List[int], query_lens: List[int],
+              cuda_graph_pad_size: int, batch_size: int):
+        attn_metadata = super().build(seq_lens, query_lens,
+                                      cuda_graph_pad_size, batch_size)
+        attn_metadata = DualChunkFlashAttentionMetadata(
+            **attn_metadata.asdict_zerocopy())
+
+        device = self.runner.device
+        attn_metadata.orig_seq_lens = self.orig_seq_lens
+        attn_metadata.orig_seq_lens_tensor = async_tensor_h2d(
+            self.orig_seq_lens, torch.int, device, self.runner.pin_memory)
+
+        attn_metadata.block_size = self.runner.block_size
+        dual_chunk_attn_config = getattr(self.runner.model_config.hf_config,
+                                         "dual_chunk_attention_config", {})
+        attn_metadata.original_max_position_embeddings = \
+            dual_chunk_attn_config.get("original_max_position_embeddings", 0)
+        attn_metadata.chunk_size = dual_chunk_attn_config.get(
+            "chunk_size", 8192)
+        attn_metadata.local_size = dual_chunk_attn_config.get(
+            "local_size", 1024)
+
+        return attn_metadata
+
+
+class DualChunkFlashAttentionImpl(FlashAttentionImpl):
+    """
+    If the input tensors contain prompt tokens, the layout is as follows:
+    |<--------------- num_prefill_tokens ----------------->|
+    |<--prefill_0-->|<--prefill_1-->|...|<--prefill_N-1--->|
+    Otherwise, the layout is as follows:
+    |<----------------- num_decode_tokens ------------------>|
+    |<--decode_0-->|..........|<--decode_M-1-->|<--padding-->|
+    Generation tokens can contain padding when cuda-graph is used.
+    Currently, prompt tokens don't contain any padding.
+    The prompts might have different lengths, while the generation tokens
+    always have length 1.
+    If chunked prefill is enabled, prefill tokens and decode tokens can be
+    batched together in a flattened 1D query.
+    |<----- num_prefill_tokens ---->|<------- num_decode_tokens --------->|
+    |<-prefill_0->|...|<-prefill_N-1->|<--decode_0-->|...|<--decode_M-1-->|
+    Currently, cuda graph is disabled for chunked prefill, meaning there's no
+    padding between prefill and decode tokens.
+    """
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        num_kv_heads: int,
+        alibi_slopes: Optional[List[float]],
+        sliding_window: Optional[int],
+        kv_cache_dtype: str,
+        logits_soft_cap: Optional[float] = None,
+        attn_type: str = AttentionType.DECODER,
+        kv_sharing_target_layer_name: Optional[str] = None,
+        layer_idx: int = -1,
+        dual_chunk_attention_config: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        if kv_sharing_target_layer_name is not None:
+            raise NotImplementedError("KV sharing is not supported in V0 "
+                                      "DUAL_CHUNK_FLASH_ATTN backend.")
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.scale = float(scale)
+        self.num_kv_heads = num_kv_heads
+        if alibi_slopes is not None:
+            alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
+        self.alibi_slopes = alibi_slopes
+        self.sliding_window = ((sliding_window, sliding_window)
+                               if sliding_window is not None else (-1, -1))
+        self.kv_cache_dtype = kv_cache_dtype
+
+        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
+        if sliding_window is not None:
+            # NOTE(woosuk): flash-attn's sliding window does not work with
+            # paged KV cache.
+            raise ValueError(
+                "Sliding window is not supported in FlashAttention.")
+
+        support_head_sizes = (
+            DualChunkFlashAttentionBackend.get_supported_head_sizes())
+
+        if head_size not in support_head_sizes:
+            raise ValueError(
+                f"Head size {head_size} is not supported by FlashAttention. "
+                f"Supported head sizes are: {support_head_sizes}.")
+
+        assert dual_chunk_attention_config is not None
+        self.chunk_size = dual_chunk_attention_config.get("chunk_size", 8192)
+        self.local_size = dual_chunk_attention_config.get("local_size", 1024)
+        self.original_max_position_embeddings = dual_chunk_attention_config.get(
+            "original_max_position_embeddings", 0)
+        self.sparse_attention_config = dual_chunk_attention_config.get(
+            "sparse_attention_config", None)
+        if not self.sparse_attention_config:
+            logger.warning_once("Sparse attention will not be enabled as "
+                                "sparse attention config is not provided.")
+        self.sparse_attention_enabled = dual_chunk_attention_config.get(
+            "sparse_attention_enabled", self.sparse_attention_config
+            is not None)
+        self.sparse_attention_threshold = dual_chunk_attention_config.get(
+            "sparse_attention_threshold", 32768)
+        self.sparse_attention_last_q = dual_chunk_attention_config.get(
+            "sparse_attention_last_q", 64)
+        self.layer_idx = layer_idx
+        self.dual_chunk_attention_config = dual_chunk_attention_config
+
+        if self.sparse_attention_config:
+            self.sparse_attention_config = {
+                int(i): j
+                for i, j in self.sparse_attention_config[
+                    self.layer_idx].items()
+            }
+            start_head = self.num_heads * get_tensor_model_parallel_rank()
+            end_head = start_head + self.num_heads
+            self.sparse_attention_config = [
+                self.sparse_attention_config[i]
+                for i in range(start_head, end_head)
+            ]
+
+        if self.sparse_attention_enabled:
+            self.arange = torch.arange(self.sparse_attention_last_q,
+                                       device="cuda")
+            self.last_q_mask = (self.arange[None, None, :, None]
+                                >= self.arange[None, None, None, :])
+
+    def forward(  # type: ignore
+        self,
+        layer: AttentionLayer,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: DualChunkFlashAttentionMetadata,
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Forward pass with DualChunkFlashAttention.
+        Args:
+            query: shape = [num_tokens, num_heads * head_size]
+            query_succ: shape = [num_tokens, num_heads * head_size]
+            query_inter: shape = [num_tokens, num_heads * head_size]
+            key: shape = [num_tokens, num_kv_heads * head_size]
+            value: shape = [num_tokens, num_kv_heads * head_size]
+            kv_cache = [2, num_blocks, block_size, num_kv_heads * head_size]
+            attn_metadata: Metadata for attention.
+        Returns:
+            shape = [num_tokens, num_heads * head_size]
+        """
+        assert output is None, "Output tensor not supported for DualChunk"
+
+        if output_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for FlashAttentionImpl")
+
+        (
+            query,
+            query_succ,
+            query_inter,
+            query_succ_critical,
+            query_inter_critical,
+        ) = torch.split(query, query.shape[-1] // 5, dim=-1)
+
+        assert (
+            query_succ is not None and query_inter is not None
+        ), "query_succ and query_inter are required in Dual Chunk Attention."
+
+        num_tokens, hidden_size = query.shape
+
+        # Reshape the query, key, and value tensors.
+        query = query.view(-1, self.num_heads, self.head_size)
+        query_succ = query_succ.view(-1, self.num_heads, self.head_size)
+        query_inter = query_inter.view(-1, self.num_heads, self.head_size)
+        query_succ_critical = query_succ_critical.view(-1, self.num_heads,
+                                                       self.head_size)
+        query_inter_critical = query_inter_critical.view(
+            -1, self.num_heads, self.head_size)
+        key = key.view(-1, self.num_kv_heads, self.head_size)
+        value = value.view(-1, self.num_kv_heads, self.head_size)
+
+        if self.original_max_position_embeddings > 0:
+            if prefill_meta := attn_metadata.prefill_metadata:
+                assert prefill_meta.scaling_factor is not None
+                assert prefill_meta.query_start_loc is not None
+                assert prefill_meta.orig_seq_lens is not None
+                current_start = 0
+                query_start_loc_cpu = prefill_meta.query_start_loc.cpu()
+                for i in range(len(prefill_meta.orig_seq_lens)):
+                    current_end = (current_start +
+                                   (query_start_loc_cpu[i + 1] -
+                                    query_start_loc_cpu[i]).item())
+                    key[current_start:current_end].mul_(
+                        prefill_meta.scaling_factor[i])
+                    current_start = current_end
+                assert current_end <= attn_metadata.num_prefill_tokens
+            if decode_meta := attn_metadata.decode_metadata:
+                assert decode_meta.scaling_factor is not None
+                scaling_factor = decode_meta.scaling_factor
+                key[attn_metadata.num_prefill_tokens:].mul_(
+                    scaling_factor.unsqueeze(-1).unsqueeze(-1))
+
+        if kv_cache is not None and kv_cache.numel() > 0:
+            key_cache = kv_cache[0]
+            value_cache = kv_cache[1]
+
+            # Reshape the input keys and values and store them in the cache.
+            # If kv_cache is not provided, the new key and value tensors are
+            # not cached. This happens during the initial memory profiling run.
+            ops.reshape_and_cache_flash(
+                key,
+                value,
+                key_cache,
+                value_cache,
+                attn_metadata.slot_mapping.flatten(),
+                self.kv_cache_dtype,
+                layer._k_scale,
+                layer._v_scale,
+            )
+
+        num_prefill_tokens = attn_metadata.num_prefill_tokens
+        num_decode_tokens = attn_metadata.num_decode_tokens
+        assert key.shape[0] == num_prefill_tokens + num_decode_tokens
+        assert value.shape[0] == num_prefill_tokens + num_decode_tokens
+        output = torch.empty_like(query)
+
+        # Query for decode. KV is not needed because it is already cached.
+        decode_query = query[num_prefill_tokens:]
+        decode_query_succ = query_succ[num_prefill_tokens:]
+        decode_query_inter = query_inter[num_prefill_tokens:]
+
+        # QKV for prefill.
+        query = query[:num_prefill_tokens]
+        query_succ = query_succ[:num_prefill_tokens]
+        query_inter = query_inter[:num_prefill_tokens]
+        query_succ_critical = query_succ_critical[:num_prefill_tokens]
+        query_inter_critical = query_inter_critical[:num_prefill_tokens]
+        key = key[:num_prefill_tokens]
+        value = value[:num_prefill_tokens]
+        assert query.shape[0] == num_prefill_tokens
+        assert decode_query.shape[0] == num_decode_tokens
+
+        if prefill_meta := attn_metadata.prefill_metadata:
+            # Prompt run.
+            if (kv_cache is None or prefill_meta.block_tables is None
+                    or prefill_meta.block_tables.numel() == 0):
+                # normal attention, called during the profiling run.
+                out = flash_attn_varlen_func(
+                    q=query,
+                    k=key,
+                    v=value,
+                    cu_seqlens_q=prefill_meta.seq_start_loc,
+                    cu_seqlens_k=prefill_meta.seq_start_loc,
+                    max_seqlen_q=prefill_meta.max_prefill_seq_len,
+                    max_seqlen_k=prefill_meta.max_prefill_seq_len,
+                    softmax_scale=self.scale,
+                    causal=True,
+                    window_size=self.sliding_window,
+                    alibi_slopes=self.alibi_slopes,
+                )
+                assert output[:num_prefill_tokens].shape == out.shape
+                output[:num_prefill_tokens] = out
+            else:
+                # prefix-enabled attention
+                assert prefill_meta.seq_lens is not None
+                assert prefill_meta.orig_seq_lens is not None
+                output[:num_prefill_tokens] = (
+                    self._dual_chunk_flash_attn_prefill(
+                        q=query,
+                        q_succ=query_succ,
+                        q_inter=query_inter,
+                        q_succ_critical=query_succ_critical,
+                        q_inter_critical=query_inter_critical,
+                        k=key_cache,
+                        v=value_cache,
+                        cu_seqlens_q=prefill_meta.query_start_loc,
+                        cu_seqlens_k=prefill_meta.seq_start_loc,
+                        orig_seq_lens=prefill_meta.orig_seq_lens,
+                        scaling_factor=prefill_meta.scaling_factor,
+                        softmax_scale=self.scale,
+                        causal=True,
+                        window_size=(-1, -1),
+                        alibi_slopes=self.alibi_slopes,
+                        block_table=prefill_meta.block_tables,
+                        chunk_size=self.chunk_size,
+                        local_size=self.local_size,
+                    ))
+
+        if decode_meta := attn_metadata.decode_metadata:
+            # Decoding run.
+            output[num_prefill_tokens:] = (
+                self._dual_chunk_flash_attn_decoding(
+                    decode_query.unsqueeze(1),
+                    decode_query_succ.unsqueeze(1),
+                    decode_query_inter.unsqueeze(1),
+                    key_cache,
+                    value_cache,
+                    block_table=decode_meta.block_tables,
+                    cache_seqlens=decode_meta.seq_lens_tensor,
+                    softmax_scale=self.scale,
+                    causal=True,
+                    alibi_slopes=self.alibi_slopes,
+                    chunk_size=self.chunk_size,
+                    local_size=self.local_size,
+                    original_max_position_embeddings=self.
+                    original_max_position_embeddings,
+                    decode_meta=decode_meta,
+                ).squeeze(1))
+        # Reshape the output tensor.
+        return output.view(num_tokens, hidden_size)
+
+    def _dual_chunk_flash_attn_prefill(
+        self,
+        q,
+        q_succ,
+        q_inter,
+        q_succ_critical,
+        q_inter_critical,
+        k,
+        v,
+        cu_seqlens_q,
+        cu_seqlens_k,
+        orig_seq_lens: List[int],
+        scaling_factor: torch.Tensor,
+        softmax_scale: float,
+        causal: Optional[bool] = True,
+        window_size: Tuple[int, int] = (-1, -1),
+        alibi_slopes: Optional[torch.Tensor] = None,
+        block_table: Optional[torch.Tensor] = None,
+        chunk_size: int = 8192,
+        local_size: int = 1024,
+    ):
+        if alibi_slopes is not None:
+            raise ValueError(
+                "Dual Chunk Attention does not support alibi_slopes")
+        if not causal:
+            raise ValueError(
+                "Dual Chunk Attention does not support causal=False")
+        if window_size != (-1, -1):
+            raise ValueError(
+                "Dual Chunk Attention does not support window_size")
+
+        cu_seqlens_q_cpu = cu_seqlens_q.cpu().tolist()
+        cu_seqlens_k_cpu = cu_seqlens_k.cpu().tolist()
+        all_outputs = []
+
+        for i in range(0, len(cu_seqlens_q_cpu) - 1):
+            qs = cu_seqlens_q_cpu[i]
+            qe = cu_seqlens_q_cpu[i:i + 2][-1]
+            ks = cu_seqlens_k_cpu[i]
+            ke = cu_seqlens_k_cpu[i:i + 2][-1]
+
+            current_q = q[qs:qe]
+            current_q_succ = q_succ[qs:qe]
+            current_q_inter = q_inter[qs:qe]
+            current_q_succ_critical = q_succ_critical[qs:qe]
+            current_q_inter_critical = q_inter_critical[qs:qe]
+
+            if block_table is None:
+                current_k = k[ks:ke]
+                current_v = v[ks:ke]
+                current_block_table = None
+                current_orig_seq_len = orig_seq_lens[i]
+            else:
+                current_block_table = block_table[i]
+                current_orig_seq_len = orig_seq_lens[i]
+                current_k = k
+                current_v = v
+            sparse_attn_enabled = (self.sparse_attention_enabled
+                                   and current_orig_seq_len
+                                   > self.sparse_attention_threshold)
+
+            if current_q.shape[0] == 0:
+                continue
+
+            if current_k.shape[0] == 0:
+                all_outputs.append(
+                    torch.zeros(
+                        (current_q.shape[0], current_q.shape[1], v.shape[2]),
+                        device=q.device,
+                        dtype=q.dtype,
+                    ))
+                continue
+
+            current_output = torch.empty_like(current_q)
+            group_size = int(current_q.size(-2) / current_k.size(-2))
+
+            if sparse_attn_enabled:
+                num_device_q_heads = current_q.size(-2)
+                heads_vertical_size = torch.empty(size=(num_device_q_heads, ),
+                                                  dtype=torch.int32)
+                heads_slash_size = torch.empty(size=(num_device_q_heads, ),
+                                               dtype=torch.int32)
+                for head_id in range(current_q.size(-2)):
+                    (
+                        ty,
+                        vertical_size,
+                        slash_size,
+                        _,
+                    ) = self.sparse_attention_config[head_id]
+                    assert ty == "vertical_and_slash", "only support slash mode"
+
+                    if vertical_size == 30:
+                        vertical_size += 100
+                    heads_vertical_size[head_id] = vertical_size
+                    heads_slash_size[head_id] = slash_size
+
+                current_output = self._dual_chunk_flash_attn_prefill_func(
+                    current_q,  # allheads
+                    current_q_succ,
+                    current_q_inter,
+                    current_q_succ_critical,
+                    current_q_inter_critical,
+                    current_k,
+                    current_v,
+                    current_block_table,
+                    softmax_scale,
+                    chunk_size,
+                    local_size,
+                    scaling_factor[i].item(),
+                    ke - ks,
+                    sparse_attn_enabled=sparse_attn_enabled,
+                    heads_vertical_size=heads_vertical_size,
+                    heads_slash_size=heads_slash_size,
+                    group_size=group_size)
+            else:
+                for head_id in range(current_q.size(-2)):
+                    # (seq_len, num_heads, head_size)
+                    current_q_head = current_q[:, head_id, :].unsqueeze(1)
+                    current_q_succ_head = \
+                        current_q_succ[:, head_id, :].unsqueeze(1)
+                    current_q_inter_head = \
+                        current_q_inter[:, head_id, :].unsqueeze(1)
+                    current_q_succ_head_critical = \
+                        current_q_succ_critical[:, head_id, :].unsqueeze(1)
+                    current_q_inter_head_critical = \
+                        current_q_inter_critical[:, head_id, :].unsqueeze(1)
+                    if block_table is not None:
+                        current_k_head = current_k[..., head_id //
+                                                   group_size, :].unsqueeze(2)
+                        current_v_head = current_v[..., head_id //
+                                                   group_size, :].unsqueeze(2)
+
+                    else:
+                        current_k_head = current_k[:, head_id, :].unsqueeze(1)
+                        current_v_head = current_v[:, head_id, :].unsqueeze(1)
+
+                    current_out = self._dual_chunk_flash_attn_prefill_func(
+                        current_q_head,
+                        current_q_succ_head,
+                        current_q_inter_head,
+                        current_q_succ_head_critical,
+                        current_q_inter_head_critical,
+                        current_k_head,
+                        current_v_head,
+                        current_block_table,
+                        softmax_scale,
+                        chunk_size,
+                        local_size,
+                        scaling_factor[i].item(),
+                        ke - ks,
+                        sparse_attn_enabled=sparse_attn_enabled,
+                    )
+                    current_output[:, head_id:head_id + 1, :] = current_out
+            all_outputs.append(current_output)
+        return torch.cat(all_outputs, dim=0)
+
+    def _dual_chunk_flash_attn_prefill_func(
+        self,
+        q,
+        q_succ,
+        q_inter,
+        q_succ_critical,
+        q_inter_critical,
+        k,
+        v,
+        block_table,
+        softmax_scale: float,
+        chunk_size: int,
+        local_size: int,
+        scaling_factor: float,
+        k_length: int,
+        sparse_attn_enabled: Optional[bool] = True,
+        heads_vertical_size=None,
+        heads_slash_size=None,
+        group_size=None,
+    ):
+        flash_results = []
+        chunk_len = chunk_size - local_size
+
+        if block_table is not None:
+            block_size = v.shape[1]
+            if chunk_len % block_size != 0:
+                raise ValueError("chunk_len must be divisible by block_size.")
+        else:
+            block_size = 1
+
+        if self.original_max_position_embeddings > 0:
+            softmax_scale = softmax_scale * scaling_factor
+
+        begin = k_length - q.shape[0]
+        while begin < k_length:
+            flash_per_chunk = []
+
+            prev_chunk_end_pos = (begin // chunk_len) * chunk_len
+            next_chunk_end_pos = prev_chunk_end_pos + chunk_len
+            end = min(next_chunk_end_pos, k_length)
+            qbegin = begin - (k_length - q.shape[0])
+            qend = end - (k_length - q.shape[0])
+
+            qk_chunks = []
+            q_states_intra = q[qbegin:qend]
+            # choose critical token
+            if block_table is not None:
+                block_tables_intra = _get_block(block_table, block_size,
+                                                prev_chunk_end_pos, end)
+                k_states_intra = k[block_tables_intra].view(
+                    -1, *k.shape[-2:])[:(end - prev_chunk_end_pos)]
+                v_states_intra = v[block_tables_intra].view(
+                    -1, *v.shape[-2:])[:(end - prev_chunk_end_pos)]
+            else:
+                block_tables_intra = None
+                k_states_intra = k[prev_chunk_end_pos:end]
+                v_states_intra = v[prev_chunk_end_pos:end]
+
+            if sparse_attn_enabled:
+                last_q_size = min(qend - qbegin, self.sparse_attention_last_q)
+                _, num_device_k_heads, head_dim = k_states_intra.shape
+                k_states_intra = (k_states_intra.unsqueeze(2).repeat(
+                    1, 1, group_size,
+                    1).reshape(-1, num_device_k_heads * group_size, head_dim))
+                v_states_intra = (v_states_intra.unsqueeze(2).repeat(
+                    1, 1, group_size,
+                    1).reshape(-1, num_device_k_heads * group_size, head_dim))
+                qk_chunks.append(
+                    (q_states_intra.transpose(0, 1)[:, -last_q_size:] *
+                     softmax_scale) @ k_states_intra.permute(1, 2, 0))
+
+            if prev_chunk_end_pos - chunk_len >= 0:
+                q_states_succ = q_succ[qbegin:qend]
+                q_states_succ_critical = q_succ_critical[qbegin:qend]
+                if block_table is not None:
+                    block_tables_succ = _get_block(
+                        block_table, block_size,
+                        prev_chunk_end_pos - chunk_len, prev_chunk_end_pos)
+                    k_states_succ = k[block_tables_succ].view(
+                        -1, *k.shape[-2:])[:chunk_len]
+                    v_states_succ = v[block_tables_succ].view(
+                        -1, *v.shape[-2:])[:chunk_len]
+                else:
+                    k_states_succ = k[prev_chunk_end_pos -
+                                      chunk_len:prev_chunk_end_pos]
+                    v_states_succ = v[prev_chunk_end_pos -
+                                      chunk_len:prev_chunk_end_pos]
+
+                if sparse_attn_enabled:
+                    k_states_succ = (k_states_succ.unsqueeze(2).repeat(
+                        1, 1, group_size,
+                        1).reshape(-1, num_device_k_heads * group_size,
+                                   head_dim))
+                    v_states_succ = (v_states_succ.unsqueeze(2).repeat(
+                        1, 1, group_size,
+                        1).reshape(-1, num_device_k_heads * group_size,
+                                   head_dim))
+                    qk_chunks.append((q_states_succ_critical.transpose(
+                        0, 1)[:, -last_q_size:] * softmax_scale)
+                                     @ k_states_succ.permute(1, 2, 0))
+
+            if prev_chunk_end_pos - chunk_len * 2 >= 0:
+                q_states_inter = q_inter[qbegin:qend]
+                q_states_inter_critical = q_inter_critical[qbegin:qend]
+                if block_table is not None:
+                    block_tables_inter = _get_block(
+                        block_table, block_size, 0,
+                        prev_chunk_end_pos - chunk_len)
+                    k_states_inter = k[block_tables_inter].view(
+                        -1, *k.shape[-2:])[:(prev_chunk_end_pos - chunk_len)]
+                    v_states_inter = v[block_tables_inter].view(
+                        -1, *v.shape[-2:])[:(prev_chunk_end_pos - chunk_len)]
+                else:
+                    k_states_inter = k[:prev_chunk_end_pos - chunk_len]
+                    v_states_inter = v[:prev_chunk_end_pos - chunk_len]
+
+                if sparse_attn_enabled:
+                    k_states_inter = (k_states_inter.unsqueeze(2).repeat(
+                        1, 1, group_size,
+                        1).reshape(-1, num_device_k_heads * group_size,
+                                   head_dim))
+                    v_states_inter = (v_states_inter.unsqueeze(2).repeat(
+                        1, 1, group_size,
+                        1).reshape(-1, num_device_k_heads * group_size,
+                                   head_dim))
+                    qk_chunks.append((q_states_inter_critical.transpose(
+                        0, 1)[:, -last_q_size:] * softmax_scale)
+                                     @ k_states_inter.permute(1, 2, 0))
+
+            if sparse_attn_enabled:
+                reversed_qk = qk_chunks[::-1]
+                qk = torch.cat(reversed_qk, dim=-1)
+
+                qk[:, :, -last_q_size:] = torch.where(
+                    self.last_q_mask[..., -last_q_size:,
+                                     -last_q_size:].to(qk.device),
+                    qk[:, :, -last_q_size:], -torch.inf)
+                qk = F.softmax(qk, dim=-1, dtype=torch.float32)
+
+                vertical = qk.sum(-2, keepdim=True)
+                vertical[..., :30] = torch.inf
+
+                # Avoid sorting by using the min/max ints to fill the indexer
+                # buffers.
+                int32_max = torch.iinfo(torch.int32).max
+                int32_min = torch.iinfo(torch.int32).min
+                n_heads = qk.size()[0]
+                max_slash_topk = torch.max(heads_slash_size).item()
+                max_vertical_topk = torch.max(heads_vertical_size).item()
+                # store each head's slash topk, vertical topk
+                vertical = vertical.reshape((n_heads, -1))
+                # prevent out of range when prompt size < max_vertical_topk
+                max_vertical_topk = min(vertical.shape[-1], max_vertical_topk)
+                vertical_topk_buffer = torch.topk(vertical, max_vertical_topk,
+                                                  -1).indices
+                slash_topk_buffer = torch.empty(size=(n_heads, max_slash_topk),
+                                                dtype=torch.int64,
+                                                device=qk.device)
+                for head_i in range(n_heads):
+                    #  (nqheads=1, lastq, k_len)
+                    head_score = qk[head_i:head_i + 1, :, :]
+                    slash_scores = _sum_all_diagonal_matrix(head_score)
+                    if head_score.size(1) != 1:
+                        # drop right up corner
+                        slash_scores = slash_scores[..., :-last_q_size + 1]
+                    slash_scores[..., -100:] = torch.inf
+
+                    head_slash_size = heads_slash_size[head_i]
+                    head_slash_size = min(head_slash_size, vertical.size(-1))
+                    slash_topk = torch.topk(slash_scores, head_slash_size,
+                                            -1).indices
+                    #（nheads, max_topk）
+                    slash_topk_buffer[head_i, :head_slash_size] = slash_topk
+
+                    # reset heads topk
+                    heads_slash_size[head_i] = head_slash_size
+                    heads_vertical_size[head_i] = min(
+                        heads_vertical_size[head_i], max_vertical_topk)
+
+                # store
+                vertical_buffer = torch.full((n_heads, max_vertical_topk),
+                                             int32_max,
+                                             dtype=torch.int64,
+                                             device=q.device)
+                slash_buffer = torch.full((n_heads, max_slash_topk),
+                                          int32_min,
+                                          dtype=torch.int64,
+                                          device=q.device)
+                succ_vertical_buffer = torch.full((n_heads, max_vertical_topk),
+                                                  int32_max,
+                                                  dtype=torch.int64,
+                                                  device=q.device)
+                succ_slash_buffer = torch.full((n_heads, max_slash_topk),
+                                               int32_min,
+                                               dtype=torch.int64,
+                                               device=q.device)
+                inter_vertical_buffer = torch.full(
+                    (n_heads, max_vertical_topk),
+                    int32_max,
+                    dtype=torch.int64,
+                    device=q.device)
+                inter_slash_buffer = torch.full((n_heads, max_slash_topk),
+                                                int32_min,
+                                                dtype=torch.int64,
+                                                device=q.device)
+
+                vertical_size_buffer = torch.empty(size=(n_heads, ),
+                                                   dtype=torch.int32,
+                                                   device=q.device)
+                slash_sizes_buffer = torch.empty(size=(n_heads, ),
+                                                 dtype=torch.int32,
+                                                 device=q.device)
+                succ_vertical_size_buffer = torch.empty(size=(n_heads, ),
+                                                        dtype=torch.int32,
+                                                        device=q.device)
+                succ_slash_sizes_buffer = torch.empty(size=(n_heads, ),
+                                                      dtype=torch.int32,
+                                                      device=q.device)
+                inter_vertical_size_buffer = torch.empty(size=(n_heads, ),
+                                                         dtype=torch.int32,
+                                                         device=q.device)
+                inter_slash_sizes_buffer = torch.empty(size=(n_heads, ),
+                                                       dtype=torch.int32,
+                                                       device=q.device)
+
+                for head_i in range(n_heads):
+                    vertical_topk = vertical_topk_buffer[
+                        head_i, :heads_vertical_size[head_i]]
+                    # intra
+                    intra_vertical_indices = vertical_topk[
+                        vertical_topk >=
+                        prev_chunk_end_pos] - prev_chunk_end_pos
+                    if intra_vertical_indices.nelement() == 0:
+                        intra_vertical_indices = torch.cat([
+                            intra_vertical_indices,
+                            torch.arange(0,
+                                         k_states_intra.size(0),
+                                         max(1,
+                                             k_states_intra.size(0) / 5),
+                                         dtype=torch.int32,
+                                         device=intra_vertical_indices.device)
+                        ])
+                    slash_topk = slash_topk_buffer[
+                        head_i, :heads_slash_size[head_i]]
+                    intra_slash_indices = (
+                        (qk.size(-1) - 1) -
+                        slash_topk[slash_topk >= prev_chunk_end_pos])
+                    # fill buffer
+                    v_count = intra_vertical_indices.nelement()
+                    s_count = intra_slash_indices.nelement()
+                    vertical_size_buffer[head_i] = v_count
+                    slash_sizes_buffer[head_i] = s_count
+                    vertical_buffer[head_i, :v_count].copy_(
+                        intra_vertical_indices)
+                    slash_buffer[head_i, :s_count].copy_(intra_slash_indices)
+                    # succ
+                    if prev_chunk_end_pos - chunk_len >= 0:
+                        succ_vertical_indices = vertical_topk[
+                            (vertical_topk < prev_chunk_end_pos)
+                            & (vertical_topk >= prev_chunk_end_pos -
+                               chunk_len)] - (prev_chunk_end_pos - chunk_len)
+                        # TODO: support no vertical
+                        if succ_vertical_indices.nelement() == 0:
+                            succ_vertical_indices = torch.cat([
+                                succ_vertical_indices,
+                                torch.arange(
+                                    0,
+                                    k_states_succ.size(0),
+                                    max(1,
+                                        k_states_succ.size(0) / 5),
+                                    dtype=torch.int32,
+                                    device=intra_vertical_indices.device)
+                            ])
+                        succ_slash_indices = (
+                            (prev_chunk_end_pos + (qend - qbegin) - 1) -
+                            slash_topk[((slash_topk >=
+                                         (prev_chunk_end_pos - chunk_len)) &
+                                        (slash_topk < (prev_chunk_end_pos +
+                                                       (qend - qbegin))))])
+                        if succ_slash_indices.nelement() == 0:
+                            succ_slash_indices = torch.cat([
+                                succ_slash_indices,
+                                torch.arange(
+                                    0,
+                                    k_states_succ.size(0),
+                                    max(1,
+                                        k_states_succ.size(0) / 5),
+                                    dtype=torch.int32,
+                                    device=intra_vertical_indices.device)
+                            ])
+                        # fill buffer
+                        v_count = succ_vertical_indices.nelement()
+                        s_count = succ_slash_indices.nelement()
+                        succ_vertical_size_buffer[head_i] = v_count
+                        succ_slash_sizes_buffer[head_i] = s_count
+                        succ_vertical_buffer[head_i, :v_count].copy_(
+                            succ_vertical_indices)
+                        succ_slash_buffer[head_i, :s_count].copy_(
+                            succ_slash_indices)
+
+                    if prev_chunk_end_pos - 2 * chunk_len >= 0:
+                        inter_vertical_indices = vertical_topk[
+                            vertical_topk < prev_chunk_end_pos - chunk_len]
+
+                        if inter_vertical_indices.nelement() == 0:
+                            inter_vertical_indices = torch.cat([
+                                inter_vertical_indices,
+                                torch.arange(
+                                    0,
+                                    k_states_inter.size(0),
+                                    max(1,
+                                        k_states_inter.size(0) / 5),
+                                    dtype=torch.int32,
+                                    device=intra_vertical_indices.device)
+                            ])
+                        inter_slash_indices = (
+                            (prev_chunk_end_pos - chunk_len +
+                             (qend - qbegin) - 1) -
+                            slash_topk[slash_topk < (prev_chunk_end_pos -
+                                                     chunk_len +
+                                                     (qend - qbegin))])
+                        if inter_slash_indices.nelement() == 0:
+                            inter_slash_indices = torch.cat([
+                                inter_slash_indices,
+                                torch.arange(
+                                    0,
+                                    k_states_inter.size(0),
+                                    max(1,
+                                        k_states_inter.size(0) / 5),
+                                    dtype=torch.int32,
+                                    device=intra_vertical_indices.device)
+                            ])
+                        # fill buffer
+                        v_count = inter_vertical_indices.nelement()
+                        s_count = inter_slash_indices.nelement()
+                        inter_vertical_size_buffer[head_i] = v_count
+                        inter_slash_sizes_buffer[head_i] = s_count
+                        inter_vertical_buffer[head_i, :v_count].copy_(
+                            inter_vertical_indices)
+                        inter_slash_buffer[head_i, :s_count].copy_(
+                            inter_slash_indices)
+            else:
+                intra_vertical_indices, intra_slash_indices = None, None
+                succ_vertical_indices, succ_slash_indices = None, None
+                inter_vertical_indices, inter_slash_indices = None, None
+
+            if sparse_attn_enabled:
+                flash_result = self._do_flash_attn(
+                    q_states_intra,
+                    k_states_intra,
+                    v_states_intra,
+                    softmax_scale=softmax_scale,
+                    causal=True,
+                    stage="intra",
+                    vertical_indices=vertical_buffer,
+                    slash_indices=slash_buffer,
+                    vertical_indices_count=vertical_size_buffer,
+                    slash_indices_count=slash_sizes_buffer,
+                    mergehead_softmax_scale=softmax_scale,
+                    sparse_attn_enabled=sparse_attn_enabled)
+            else:
+                flash_result = self._do_flash_attn(
+                    q_states_intra,
+                    k_states_intra,
+                    v_states_intra,
+                    softmax_scale=softmax_scale,
+                    causal=True,
+                    stage="intra",
+                    vertical_indices=intra_vertical_indices,
+                    slash_indices=intra_slash_indices,
+                    sparse_attn_enabled=sparse_attn_enabled)
+            flash_per_chunk.append(flash_result)
+
+            if prev_chunk_end_pos - chunk_len >= 0:
+                if sparse_attn_enabled:
+                    flash_result = self._do_flash_attn(
+                        q_states_succ,
+                        k_states_succ,
+                        v_states_succ,
+                        softmax_scale=softmax_scale,
+                        causal=False,
+                        stage="succ",
+                        vertical_indices=succ_vertical_buffer,
+                        slash_indices=succ_slash_buffer,
+                        vertical_indices_count=succ_vertical_size_buffer,
+                        slash_indices_count=succ_slash_sizes_buffer,
+                        mergehead_softmax_scale=softmax_scale,
+                        sparse_attn_enabled=sparse_attn_enabled)
+                else:
+                    flash_result = self._do_flash_attn(
+                        q_states_succ,
+                        k_states_succ,
+                        v_states_succ,
+                        softmax_scale=softmax_scale,
+                        causal=False,
+                        stage="succ",
+                        vertical_indices=succ_vertical_indices,
+                        slash_indices=succ_slash_indices,
+                        sparse_attn_enabled=sparse_attn_enabled)
+                flash_per_chunk.append(flash_result)
+
+            if prev_chunk_end_pos - chunk_len * 2 >= 0:
+                if sparse_attn_enabled:
+                    flash_result = self._do_flash_attn(
+                        q_states_inter,
+                        k_states_inter,
+                        v_states_inter,
+                        softmax_scale=softmax_scale,
+                        causal=False,
+                        stage="inter",
+                        vertical_indices=inter_vertical_buffer,
+                        slash_indices=inter_slash_buffer,
+                        vertical_indices_count=inter_vertical_size_buffer,
+                        slash_indices_count=inter_slash_sizes_buffer,
+                        mergehead_softmax_scale=softmax_scale,
+                        sparse_attn_enabled=sparse_attn_enabled)
+                else:
+                    flash_result = self._do_flash_attn(
+                        q_states_inter,
+                        k_states_inter,
+                        v_states_inter,
+                        softmax_scale=softmax_scale,
+                        causal=False,
+                        stage="inter",
+                        vertical_indices=inter_vertical_indices,
+                        slash_indices=inter_slash_indices,
+                        sparse_attn_enabled=sparse_attn_enabled)
+                flash_per_chunk.append(flash_result)
+
+            flash_results.append(flash_per_chunk)
+            begin = end
+
+        attn_output = self._merge_attn_outputs(flash_results)
+        del flash_results
+        return attn_output
+
+    def _do_flash_attn(
+        self,
+        query_states: torch.Tensor,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        softmax_scale: float,
+        causal: bool = True,
+        max_seqlen_k: Optional[int] = None,
+        stage: str = "intra",
+        vertical_indices: Optional[torch.Tensor] = None,
+        slash_indices: Optional[torch.Tensor] = None,
+        vertical_indices_count: Optional[torch.Tensor] = None,
+        slash_indices_count: Optional[torch.Tensor] = None,
+        mergehead_softmax_scale: Optional[float] = None,
+        sparse_attn_enabled: Optional[bool] = False,
+    ):
+        if max_seqlen_k is None:
+            max_seqlen_k = key_states.shape[0]
+
+        q_len = query_states.shape[0]
+        q_heads = query_states.shape[1]
+        h_dim = query_states.shape[-1]
+
+        if sparse_attn_enabled:
+            assert slash_indices is not None
+            if stage == "intra":
+                assert causal
+            else:
+                assert not causal
+
+            query_states = query_states.unsqueeze(0).transpose(1, 2)
+            key_states = key_states.unsqueeze(0).transpose(1, 2)
+            value_states = value_states.unsqueeze(0).transpose(1, 2)
+
+            q = query_states
+            k = key_states
+            v = value_states
+
+            if (vertical_indices_count is not None and \
+                    slash_indices_count is not None):
+                assert mergehead_softmax_scale is not None
+
+                res, s_lse = _vertical_slash_sparse_attention(
+                    q,
+                    k,
+                    v,
+                    vertical_indices,
+                    slash_indices,
+                    mergehead_softmax_scale,
+                    causal=causal,
+                    stage=stage,
+                    vertical_indices_count=vertical_indices_count,
+                    slash_indices_count=slash_indices_count)
+                res = res.view(q_heads, q_len,
+                               h_dim).transpose(0, 1)  # (qlen,nhead,h_dim)
+                s_lse = s_lse.view(
+                    q_heads, q_len,
+                    1).squeeze(-1).unsqueeze(0).float()  # (1, nhead,qlen)
+            else:
+                res, s_lse = _vertical_slash_sparse_attention(q,
+                                                              k,
+                                                              v,
+                                                              vertical_indices,
+                                                              slash_indices,
+                                                              softmax_scale,
+                                                              causal=causal,
+                                                              stage=stage)
+                res = res.view(q_len, q_heads, h_dim)
+                s_lse = s_lse.view(q_len, q_heads, 1).transpose(0, 2).float()
+            return res, s_lse
+
+        output, softmax_lse = flash_attn_varlen_func(
+            q=query_states,
+            k=key_states,
+            v=value_states,
+            softmax_scale=softmax_scale,
+            cu_seqlens_q=torch.tensor([0, query_states.shape[0]],
+                                      dtype=torch.int32,
+                                      device=query_states.device),
+            max_seqlen_q=query_states.shape[0],
+            cu_seqlens_k=torch.tensor([0, max_seqlen_k],
+                                      dtype=torch.int32,
+                                      device=query_states.device),
+            max_seqlen_k=max_seqlen_k,
+            causal=causal,
+            return_softmax_lse=True,
+        )
+        softmax_lse = softmax_lse.view(q_len, q_heads, 1).transpose(0,
+                                                                    2).float()
+        return output, softmax_lse
+
+    def _merge_attn_outputs(
+        self,
+        flash_results: List[List[Tuple[torch.Tensor, torch.Tensor]]],
+        return_lse: Optional[bool] = False,
+    ) -> torch.Tensor:
+        attn_outputs_all = []
+        logits_all = []
+
+        for flash_per_chunk in flash_results:
+            if len(flash_per_chunk) == 1:
+                attn_outputs_all.append(flash_per_chunk[0][0])
+                if return_lse:
+                    logits_all.append(flash_per_chunk[0][1])
+                continue
+
+            attn_outputs = torch.stack([
+                flash_attn_output[0] for flash_attn_output in flash_per_chunk
+            ])
+            logits = torch.stack([
+                flash_attn_output[1] for flash_attn_output in flash_per_chunk
+            ])
+            logits = logits.to(torch.float32)
+
+            if return_lse:
+                max_val = torch.max(logits, dim=0).values
+                diff = torch.abs(logits[0] - logits[1])
+                log_sum_exp = max_val + torch.log1p(torch.exp(-diff))
+                logits_all.append(log_sum_exp)
+
+            max_logits = torch.max(logits, dim=0).values
+            stable_logits = logits - max_logits.unsqueeze(0)
+            lse_s = torch.exp(stable_logits).detach()
+            lse_sum = torch.sum(lse_s, dim=0)
+            lse_s /= lse_sum
+            attn_outputs *= lse_s.unsqueeze(-1).transpose(2, 3).squeeze(1)
+            attn_outputs_all.append(attn_outputs.sum(dim=0))
+
+        if return_lse:
+            return (torch.cat(attn_outputs_all,
+                              dim=0), torch.cat(logits_all, dim=-1))
+        else:
+            return torch.cat(attn_outputs_all, dim=0)
+
+    def _dual_chunk_flash_attn_decoding(
+        self,
+        query: torch.Tensor,
+        query_succ: torch.Tensor,
+        query_inter: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        block_table: torch.Tensor,
+        cache_seqlens: torch.Tensor,
+        softmax_scale: float,
+        causal: bool,
+        alibi_slopes: Optional[torch.Tensor],
+        chunk_size: int,
+        local_size: int,
+        original_max_position_embeddings: int,
+        decode_meta: DualChunkFlashAttentionMetadata,
+    ):
+        if not causal:
+            raise ValueError(
+                "Dual Chunk Attention does not support causal=False")
+
+        block_size = value_cache.shape[1]
+        chunk_len = chunk_size - local_size
+        if chunk_len % block_size != 0:
+            raise ValueError("chunk_len must be divisible by block_size.")
+        if original_max_position_embeddings > 0:
+            assert decode_meta.scaling_factor is not None
+            scaling_factor = decode_meta.scaling_factor
+            query = (query * scaling_factor.view(-1, 1, 1, 1)).to(
+                query.dtype
+            )  # possible for numerical issue, need to fused in the kernel
+            query_succ = (query_succ * scaling_factor.view(-1, 1, 1, 1)).to(
+                query.dtype)
+            query_inter = (query_inter * scaling_factor.view(-1, 1, 1, 1)).to(
+                query.dtype)
+        outputs_list = []
+        softmax_lses_list = []
+
+        # intra-attention
+        intra_output, intra_softmax_lse = (
+            self._dual_chunk_flash_attn_decoding_with_exp_sums(
+                query,
+                key_cache,
+                value_cache,
+                decode_meta.block_tables_intra,
+                decode_meta.seq_lens_intra,
+                softmax_scale,
+                alibi_slopes,
+                causal=False,
+            ))
+        outputs_list.append(intra_output)
+        softmax_lses_list.append(intra_softmax_lse)
+
+        # succ-attention
+        if decode_meta.max_seq_len_succ:
+            succ_output, succ_softmax_lse = (
+                self._dual_chunk_flash_attn_decoding_with_exp_sums(
+                    query_succ,
+                    key_cache,
+                    value_cache,
+                    decode_meta.block_tables_succ,
+                    decode_meta.seq_lens_succ,
+                    softmax_scale,
+                    alibi_slopes,
+                    causal=False,
+                ))
+            outputs_list.append(succ_output)
+            softmax_lses_list.append(succ_softmax_lse)
+
+        # inter-attention
+        if decode_meta.max_seq_len_inter:
+            inter_output, inter_softmax_lse = (
+                self._dual_chunk_flash_attn_decoding_with_exp_sums(
+                    query_inter,
+                    key_cache,
+                    value_cache,
+                    block_table[:, :decode_meta.max_seq_len_inter],
+                    decode_meta.seq_lens_inter,
+                    softmax_scale,
+                    alibi_slopes,
+                    causal=False,
+                ))
+            outputs_list.append(inter_output)
+            softmax_lses_list.append(inter_softmax_lse)
+        outputs = torch.stack(outputs_list, dim=0)
+        del outputs_list
+        softmax_lses = torch.stack(softmax_lses_list, dim=0).to(torch.float32)
+        del softmax_lses_list
+        max_logits = torch.max(softmax_lses, dim=0).values
+        stable_logits = softmax_lses - max_logits.unsqueeze(0)
+        lse_s = torch.exp(stable_logits).detach()
+        lse_sum = torch.sum(lse_s, dim=0)
+        lse_s /= lse_sum
+        outputs *= lse_s.unsqueeze(-1).transpose(2, 3)
+        return outputs.sum(0)
+
+    def _dual_chunk_flash_attn_decoding_with_exp_sums(
+        self,
+        query: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        block_table: torch.Tensor,
+        cache_seqlens: torch.Tensor,
+        softmax_scale: float,
+        alibi_slopes: Optional[torch.Tensor],
+        causal: bool,
+    ):
+        out, softmax_lse = flash_attn_with_kvcache(
+            q=query,
+            k_cache=key_cache,
+            v_cache=value_cache,
+            block_table=block_table,
+            cache_seqlens=cache_seqlens,
+            softmax_scale=softmax_scale,
+            alibi_slopes=alibi_slopes,
+            causal=causal,
+            return_softmax_lse=True,
+        )
+        mask = (cache_seqlens == 0)
+        out[mask] = 0
+        softmax_lse[mask] = -float("inf")
+        return out, softmax_lse
+
+
+def _vertical_slash_sparse_attention(
+    query: torch.Tensor,  # [BATCH, N_HEADS, N_CTX, D_HEAD]
+    key: torch.Tensor,  # [BATCH, N_HEADS, N_KV_CTX, D_HEAD]
+    value: torch.Tensor,  # [BATCH, N_HEADS, N_KV_CTX, D_HEAD]
+    v_idx: torch.Tensor,  # [BATCH, N_HEADS, NNZ_V]
+    s_idx: torch.Tensor,  # [BATCH, N_HEADS, NNZ_S]
+    softmax_scale: float,
+    causal: bool = True,
+    stage: str = "intra",
+    block_size_M: int = 64,
+    block_size_N: int = 64,
+    vertical_indices_count: torch.Tensor = None,  # [N_HEADS,]
+    slash_indices_count: torch.Tensor = None,
+):
+    if stage == "intra":
+        assert causal
+    else:
+        assert not causal
+
+    batch_size, num_heads, context_size, head_dim = query.shape
+    _, _, kv_seq_len, _ = key.shape
+
+    if head_dim not in [16, 32, 64, 128, 256, 512]:
+        target_dim = 2**math.ceil(math.log2(head_dim)) - head_dim
+        query = F.pad(query, [0, target_dim, 0, 0, 0, 0, 0, 0])
+        key = F.pad(key, [0, target_dim, 0, 0, 0, 0, 0, 0])
+        value = F.pad(value, [0, target_dim, 0, 0, 0, 0, 0, 0])
+
+    v_idx = v_idx.to(torch.int32).reshape(
+        (batch_size, num_heads, -1)).sort(dim=-1, descending=False)[0]
+    s_idx = s_idx.to(torch.int32).reshape(
+        (batch_size, num_heads, -1)).sort(dim=-1, descending=True)[0]
+    q_seqlens = torch.tensor([context_size],
+                             dtype=torch.int32,
+                             device=query.device)
+    kv_seqlens = torch.tensor([kv_seq_len],
+                              dtype=torch.int32,
+                              device=query.device)
+
+    if vertical_indices_count is not None and slash_indices_count is not None:
+        (
+            block_count,
+            block_offset,
+            column_count,
+            column_index,
+        ) = ops.convert_vertical_slash_indexes_mergehead(
+            q_seqlens, kv_seqlens, v_idx, s_idx, vertical_indices_count,
+            slash_indices_count, context_size, block_size_M, block_size_N,
+            causal)
+    else:
+        (
+            block_count,
+            block_offset,
+            column_count,
+            column_index,
+        ) = ops.convert_vertical_slash_indexes(q_seqlens, kv_seqlens, v_idx,
+                                               s_idx, context_size,
+                                               block_size_M, block_size_N,
+                                               causal)
+
+    q = query.transpose(1, 2).contiguous()
+    k = key.transpose(1, 2).contiguous()
+    v = value.transpose(1, 2).contiguous()
+    out, lse = sparse_attn_func(
+        q,
+        k,
+        v,
+        block_count,
+        block_offset,
+        column_count,
+        column_index,
+        causal=causal,
+        softmax_scale=softmax_scale,
+        return_softmax_lse=True,
+    )
+    out = out.transpose(1, 2).contiguous()
+    softmax_lse = lse.reshape(*lse.shape, 1)
+    return (out[..., :context_size, :head_dim],
+            softmax_lse[..., :context_size, :])
+
+
+def _sum_all_diagonal_matrix(mat: torch.tensor):
+    h, n, m = mat.shape
+    # Zero matrix used for padding
+    zero_mat = torch.zeros((h, n, n), device=mat.device)
+    # pads the matrix on left and right
+    mat_padded = torch.cat((zero_mat, mat, zero_mat), -1)
+    # Change the strides
+    mat_strided = mat_padded.as_strided((1, n, n + m),
+                                        (n * (2 * n + m), 2 * n + m + 1, 1))
+    # Sums the resulting matrix's columns
+    sum_diags = torch.sum(mat_strided, 1)
+    return sum_diags[:, 1:]  # drop left bottom corner
+
+
+def _get_block(block_table: torch.Tensor, block_size: int, begin: int,
+               end: int):
+    begin_block = begin // block_size
+    end_block = (end - 1) // block_size + 1
+    return block_table[begin_block:end_block]
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/mla/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/mla/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/placeholder_attn.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/placeholder_attn.py
new file mode 100644
index 0000000000000000000000000000000000000000..e630a6c6de8c40795bccebd0fc58143949c2b41b
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/placeholder_attn.py
@@ -0,0 +1,340 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections import defaultdict
+from dataclasses import dataclass
+from itertools import accumulate
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Type
+
+import torch
+
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata,
+                                              AttentionMetadataBuilder)
+from vllm.attention.backends.utils import CommonAttentionState
+from vllm.multimodal import MultiModalPlaceholderMap
+
+if TYPE_CHECKING:
+    from vllm.worker.model_runner import (ModelInputForGPUBuilder)
+from vllm.utils import async_tensor_h2d
+
+# Placeholder attention backend for models like Mamba and pooling models that
+# lack attention.
+
+
+class PlaceholderAttentionBackend(AttentionBackend):
+    """Placeholder backend for when no attention is needed."""
+
+    @staticmethod
+    def get_name() -> str:
+        return "NO_ATTENTION"
+
+    @staticmethod
+    def get_impl_cls() -> Type["PlaceholderAttentionImpl"]:
+        return PlaceholderAttentionImpl
+
+    @staticmethod
+    def get_builder_cls() -> Type["PlaceholderAttentionMetadataBuilder"]:
+        return PlaceholderAttentionMetadataBuilder
+
+    @staticmethod
+    def get_metadata_cls() -> Type["PlaceholderAttentionMetadata"]:
+        return PlaceholderAttentionMetadata
+
+    @staticmethod
+    def get_state_cls() -> Type["CommonAttentionState"]:
+        return CommonAttentionState
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> Tuple[int, ...]:
+        return (1, 1, 1, 1, 1)
+
+    @staticmethod
+    def swap_blocks(
+        src_kv_cache: torch.Tensor,
+        dst_kv_cache: torch.Tensor,
+        src_to_dst: torch.Tensor,
+    ) -> None:
+        return
+
+    @staticmethod
+    def copy_blocks(
+        kv_caches: List[torch.Tensor],
+        src_to_dists: torch.Tensor,
+    ) -> None:
+        return
+
+
+@dataclass
+class PlaceholderAttentionMetadata(AttentionMetadata):
+    """Attention metadata for prefill and decode batched together."""
+    # (batch_size,). The sequence length per sequence. Sequence length means
+    # the computed tokens + new tokens None if it is a decoding.
+    seq_lens: Optional[List[int]]
+    # seq_lens stored as a tensor.
+    seq_lens_tensor: Optional[torch.Tensor]
+
+    # Maximum sequence length among prefill batch. 0 if there are decoding
+    # requests only.
+    max_prefill_seq_len: int
+    # Maximum sequence length among decode batch. 0 if there are prefill
+    # requests only.
+    max_decode_seq_len: int
+    # (batch_size,) A tensor of context lengths (tokens that are computed
+    # so far).
+    context_lens_tensor: Optional[torch.Tensor]
+
+    # Whether or not if cuda graph is enabled.
+    # Cuda-graph is currently enabled for decoding only.
+    # TODO(woosuk): Move `use_cuda_graph` out since it's unrelated to attention.
+    use_cuda_graph: bool
+
+    # Maximum query length in the batch.
+    max_query_len: Optional[int]
+
+    # Max number of query tokens among request in the batch.
+    max_decode_query_len: Optional[int]
+
+    # (batch_size + 1,). The cumulative subquery lengths of the sequences in
+    # the batch, used to index into subquery. E.g., if the subquery length
+    # is [4, 6], it is [0, 4, 10].
+    query_start_loc: Optional[torch.Tensor] = None
+    # (batch_size + 1,). The cumulative sequence lengths of the sequences in
+    # the batch, used to index into sequence. E.g., if the sequence length is
+    # [4, 6], it is [0, 4, 10].
+    seq_start_loc: Optional[torch.Tensor] = None
+
+    # Placeholder.
+    block_tables: Optional[torch.Tensor] = None
+
+    _cached_prefill_metadata: Optional["PlaceholderAttentionMetadata"] = None
+    _cached_decode_metadata: Optional["PlaceholderAttentionMetadata"] = None
+
+    @property
+    def prefill_metadata(self) -> Optional["PlaceholderAttentionMetadata"]:
+        if self.num_prefills == 0:
+            return None
+
+        if self._cached_prefill_metadata is not None:
+            return self._cached_prefill_metadata
+
+        # Compute some attn_metadata fields which default to None
+        query_start_loc = (None if self.query_start_loc is None else
+                           self.query_start_loc[:self.num_prefills + 1])
+        seq_lens = (None if self.seq_lens is None else
+                    self.seq_lens[:self.num_prefills])
+        seq_lens_tensor = (None if self.seq_lens_tensor is None else
+                           self.seq_lens_tensor[:self.num_prefills])
+        seq_start_loc = (None if self.seq_start_loc is None else
+                         self.seq_start_loc[:self.num_prefills + 1])
+        context_lens_tensor = (None if self.context_lens_tensor is None else
+                               self.context_lens_tensor[:self.num_prefills])
+
+        # Placeholders
+        slot_mapping = torch.empty(0)
+        block_tables = torch.empty(0)
+
+        self._cached_prefill_metadata = PlaceholderAttentionMetadata(
+            num_prefills=self.num_prefills,
+            num_prefill_tokens=self.num_prefill_tokens,
+            num_decode_tokens=0,
+            slot_mapping=slot_mapping,
+            multi_modal_placeholder_index_maps=self.
+            multi_modal_placeholder_index_maps,
+            enable_kv_scales_calculation=self.enable_kv_scales_calculation,
+            seq_lens=seq_lens,
+            seq_lens_tensor=seq_lens_tensor,
+            max_decode_query_len=0,
+            max_query_len=self.max_query_len,
+            max_prefill_seq_len=self.max_prefill_seq_len,
+            max_decode_seq_len=0,
+            query_start_loc=query_start_loc,
+            seq_start_loc=seq_start_loc,
+            context_lens_tensor=context_lens_tensor,
+            block_tables=block_tables,
+            use_cuda_graph=False,
+        )
+        return self._cached_prefill_metadata
+
+    @property
+    def decode_metadata(self) -> Optional["PlaceholderAttentionMetadata"]:
+        if self.num_decode_tokens == 0:
+            return None
+
+        if self._cached_decode_metadata is not None:
+            return self._cached_decode_metadata
+        assert self.seq_lens_tensor is not None
+
+        # Placeholders
+        slot_mapping = torch.empty(0)
+        block_tables = torch.empty(0)
+        seq_lens_tensor = (None if self.seq_lens_tensor is None else
+                           self.seq_lens_tensor[self.num_prefills:])
+
+        self._cached_decode_metadata = PlaceholderAttentionMetadata(
+            num_prefills=0,
+            num_prefill_tokens=0,
+            num_decode_tokens=self.num_decode_tokens,
+            slot_mapping=slot_mapping,
+            multi_modal_placeholder_index_maps=None,
+            enable_kv_scales_calculation=True,
+            seq_lens=None,
+            seq_lens_tensor=seq_lens_tensor,
+            max_decode_query_len=self.max_decode_query_len,
+            max_query_len=None,
+            max_prefill_seq_len=0,
+            max_decode_seq_len=self.max_decode_seq_len,
+            query_start_loc=(self.query_start_loc[self.num_prefills:] -
+                             self.query_start_loc[self.num_prefills])
+            if self.query_start_loc is not None else None,
+            seq_start_loc=self.seq_start_loc[self.num_prefills:]
+            if self.seq_start_loc is not None else None,
+            context_lens_tensor=None,
+            block_tables=block_tables,
+            use_cuda_graph=self.use_cuda_graph,
+        )
+        return self._cached_decode_metadata
+
+
+class PlaceholderAttentionMetadataBuilder(
+        AttentionMetadataBuilder[PlaceholderAttentionMetadata]):
+
+    def __init__(self, input_builder: "ModelInputForGPUBuilder"):
+
+        self.input_builder = input_builder
+        self.runner = input_builder.runner
+
+    def prepare(self):
+        self.prefill_seq_lens: List[int] = []
+        self.context_lens: List[int] = []
+        self.curr_seq_lens: List[int] = []
+        self.multimodal_placeholder_maps: Dict[
+            str,
+            MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
+        self.num_prefills = 0
+        self.num_prefill_tokens = 0
+        self.num_decode_tokens = 0
+
+    def _add_seq_group(
+            self, inter_data: "ModelInputForGPUBuilder.InterDataForSeqGroup",
+            chunked_prefill_enabled: bool):
+        """Add a sequence group to the metadata. Specifically update/append
+        1. context length.
+        """
+        is_prompt = inter_data.is_prompt
+
+        for (seq_id, token_len, seq_len, curr_seq_len, query_len, context_len,
+             curr_sliding_window_block) in zip(
+                 inter_data.seq_ids, [len(t) for t in inter_data.input_tokens],
+                 inter_data.orig_seq_lens, inter_data.seq_lens,
+                 inter_data.query_lens, inter_data.context_lens,
+                 inter_data.curr_sliding_window_blocks):
+            self.context_lens.append(context_len)
+
+            if is_prompt:
+                mm_maps = inter_data.multi_modal_placeholder_maps
+                if mm_maps:
+                    for modality, placeholders in mm_maps.items():
+                        self.multimodal_placeholder_maps[modality].extend(
+                            placeholders)
+
+                self.num_prefills += 1
+                self.num_prefill_tokens += token_len
+                self.prefill_seq_lens.append(seq_len)
+            else:
+                self.num_decode_tokens += query_len
+                self.curr_seq_lens.append(curr_seq_len)
+
+    def build(self, seq_lens: List[int], query_lens: List[int],
+              cuda_graph_pad_size: int, batch_size: int):
+        """Build attention metadata with on-device tensors.
+
+        Args:
+            seq_lens: The maybe padded sequence lengths of the input sequences.
+            query_lens: The query lengths of the input sequences.
+            cuda_graph_pad_size: The padding size for cuda graph.
+                                 -1 if cuda graph is not used.
+            batch_size: The maybe padded batch size.
+        """
+
+        # Some input builders such as ModelInputForCPUBuilder do not have the
+        # "inter_data_list" attribute.
+        # Let's check inter_data_list exists before we reference it.
+        if hasattr(self.input_builder, "inter_data_list"):
+            for inter_data in self.input_builder.inter_data_list:
+                self._add_seq_group(inter_data,
+                                    self.input_builder.chunked_prefill_enabled)
+
+        device = self.runner.device
+        use_captured_graph = cuda_graph_pad_size != -1
+
+        max_query_len = max(query_lens)
+        decode_query_lens = query_lens[self.num_prefills:]
+        if len(decode_query_lens) > 0:
+            max_decode_query_len = max(decode_query_lens)
+        else:
+            max_decode_query_len = 1
+        max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
+        max_decode_seq_len = max(self.curr_seq_lens, default=0)
+        num_decode_tokens = self.num_decode_tokens
+        query_start_loc = list(accumulate(query_lens, initial=0))
+        seq_start_loc = list(accumulate(seq_lens, initial=0))
+
+        if use_captured_graph:
+            num_decode_tokens = batch_size - self.num_prefill_tokens
+        assert max_query_len > 0, ("query_lens: {}".format(query_lens))
+
+        assert device is not None
+        context_lens_tensor = async_tensor_h2d(self.context_lens, torch.int,
+                                               device, self.runner.pin_memory)
+        seq_lens_tensor = async_tensor_h2d(seq_lens, torch.int, device,
+                                           self.runner.pin_memory)
+        query_start_loc_tensor = async_tensor_h2d(query_start_loc, torch.int32,
+                                                  device,
+                                                  self.runner.pin_memory)
+        seq_start_loc_tensor = async_tensor_h2d(seq_start_loc, torch.int32,
+                                                device, self.runner.pin_memory)
+
+        placeholder_index_maps = {
+            modality: placeholder_map.index_map()
+            for modality, placeholder_map in
+            self.multimodal_placeholder_maps.items()
+        }
+
+        # Placeholders
+        slot_mapping_tensor = torch.empty(0)
+        block_tables = torch.empty(0)
+
+        return PlaceholderAttentionMetadata(
+            num_prefills=self.num_prefills,
+            slot_mapping=slot_mapping_tensor,
+            multi_modal_placeholder_index_maps=placeholder_index_maps,
+            enable_kv_scales_calculation=True,
+            num_prefill_tokens=self.num_prefill_tokens,
+            num_decode_tokens=num_decode_tokens,
+            seq_lens=seq_lens,
+            seq_lens_tensor=seq_lens_tensor,
+            max_query_len=max_query_len,
+            max_decode_query_len=max_decode_query_len,
+            max_prefill_seq_len=max_prefill_seq_len,
+            max_decode_seq_len=max_decode_seq_len,
+            query_start_loc=query_start_loc_tensor,
+            seq_start_loc=seq_start_loc_tensor,
+            context_lens_tensor=context_lens_tensor,
+            block_tables=block_tables,
+            use_cuda_graph=use_captured_graph,
+        )
+
+
+class PlaceholderAttentionImpl(AttentionImpl):
+
+    def __init__(self, *args, **kwargs) -> None:
+        return
+
+    def forward(self, *args, **kwargs) -> torch.Tensor:
+        raise NotImplementedError
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/triton_mla.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/triton_mla.py
new file mode 100644
index 0000000000000000000000000000000000000000..fba5b5f6bca860ae783e1cd333a9fa31e81a8d46
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/triton_mla.py
@@ -0,0 +1,111 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import List, Optional, Type
+
+import torch
+
+from vllm.attention.backends.abstract import (AttentionType,
+                                              is_quantized_kv_cache)
+from vllm.attention.backends.mla.common import (MLACommonBackend,
+                                                MLACommonImpl,
+                                                MLACommonMetadata)
+from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
+
+
+class TritonMLABackend(MLACommonBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "TRITON_MLA"
+
+    @staticmethod
+    def get_impl_cls() -> Type["TritonMLAImpl"]:
+        return TritonMLAImpl
+
+
+class TritonMLAImpl(MLACommonImpl[MLACommonMetadata]):
+
+    def __init__(
+            self,
+            num_heads: int,
+            head_size: int,
+            scale: float,
+            num_kv_heads: int,
+            alibi_slopes: Optional[List[float]],
+            sliding_window: Optional[int],
+            kv_cache_dtype: str,
+            logits_soft_cap: Optional[float],
+            attn_type: str,
+            kv_sharing_target_layer_name: Optional[str],
+            # MLA Specific Arguments
+            **mla_args) -> None:
+        super().__init__(num_heads, head_size, scale, num_kv_heads,
+                         alibi_slopes, sliding_window, kv_cache_dtype,
+                         logits_soft_cap, attn_type,
+                         kv_sharing_target_layer_name, **mla_args)
+
+        unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "TritonMLAImpl does not support one of the following: "
+                "alibi_slopes, sliding_window, logits_soft_cap")
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError("Encoder self-attention and "
+                                      "encoder/decoder cross-attention "
+                                      "are not implemented for "
+                                      "TritonMLAImpl")
+
+        if is_quantized_kv_cache(self.kv_cache_dtype):
+            raise NotImplementedError(
+                "TritonMLA with FP8 KV cache not yet supported")
+
+    def _forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: MLACommonMetadata,
+    ) -> torch.Tensor:
+        assert kv_c_and_k_pe_cache.numel() > 0
+
+        decode_meta = attn_metadata.decode_metadata
+        assert decode_meta is not None
+        B = q_nope.shape[0]
+
+        q = torch.cat([q_nope, q_pe], dim=-1)
+        o = torch.zeros(B,
+                        self.num_heads,
+                        self.kv_lora_rank,
+                        dtype=q.dtype,
+                        device=q.device)
+
+        num_kv_splits = 4  # TODO: heuristic
+
+        # TODO(lucas) Allocate ahead of time
+        attn_logits = torch.empty(
+            (
+                B,
+                self.num_heads,
+                num_kv_splits,
+                # NOTE(lucas) idk why the +1 is here but sglang has it so we
+                # just mirror that
+                self.kv_lora_rank + 1,
+            ),
+            dtype=torch.float32,
+            device=q.device,
+        )
+
+        # Add a head dim of 1
+        kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.unsqueeze(2)
+        kv_c_cache = kv_c_and_k_pe_cache[..., :self.kv_lora_rank]
+        PAGE_SIZE = kv_c_and_k_pe_cache.size(1)
+
+        # Run MQA
+        decode_attention_fwd(q, kv_c_and_k_pe_cache, kv_c_cache, o,
+                             decode_meta.block_tables,
+                             decode_meta.seq_lens_tensor, attn_logits,
+                             num_kv_splits, self.scale, PAGE_SIZE)
+
+        return self._v_up_proj(o)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/xformers.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/xformers.py
new file mode 100644
index 0000000000000000000000000000000000000000..0bc38b4142901f01eb4a5f3cc1618f69f97e2331
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/backends/xformers.py
@@ -0,0 +1,804 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Attention layer with xFormers and PagedAttention."""
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple, Type
+
+import torch
+from xformers import ops as xops
+from xformers.ops.fmha.attn_bias import (AttentionBias,
+                                         BlockDiagonalCausalMask,
+                                         BlockDiagonalMask,
+                                         LowerTriangularMaskWithTensorBias)
+
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionLayer,
+                                              AttentionMetadata, AttentionType)
+from vllm.attention.backends.utils import (
+    CommonAttentionState, CommonMetadataBuilder,
+    get_num_prefill_decode_query_kv_tokens, get_seq_len_block_table_args,
+    is_all_cross_attn_metadata_set, is_all_encoder_attn_metadata_set)
+from vllm.attention.ops.paged_attn import (PagedAttention,
+                                           PagedAttentionMetadata)
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+class XFormersBackend(AttentionBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "XFORMERS"
+
+    @staticmethod
+    def get_impl_cls() -> Type["XFormersImpl"]:
+        return XFormersImpl
+
+    @staticmethod
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
+        return XFormersMetadata
+
+    @staticmethod
+    def get_builder_cls() -> Type["XFormersMetadataBuilder"]:
+        return XFormersMetadataBuilder
+
+    @staticmethod
+    def get_state_cls() -> Type["CommonAttentionState"]:
+        return CommonAttentionState
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> Tuple[int, ...]:
+        return PagedAttention.get_kv_cache_shape(num_blocks, block_size,
+                                                 num_kv_heads, head_size)
+
+    @staticmethod
+    def swap_blocks(
+        src_kv_cache: torch.Tensor,
+        dst_kv_cache: torch.Tensor,
+        src_to_dst: Dict[int, int],
+    ) -> None:
+        PagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
+
+    @staticmethod
+    def copy_blocks(
+        kv_caches: List[torch.Tensor],
+        src_to_dists: torch.Tensor,
+    ) -> None:
+        PagedAttention.copy_blocks(kv_caches, src_to_dists)
+
+
+@dataclass
+class XFormersMetadata(AttentionMetadata, PagedAttentionMetadata):
+    """Metadata for XFormersbackend.
+
+    NOTE: Any python object stored here is not updated when it is
+    cuda-graph replayed. If you have values that need to be changed
+    dynamically, it should be stored in tensor. The tensor has to be
+    updated from `CUDAGraphRunner.forward` API.
+    """
+
+    # |---------- N-1 iteration --------|
+    # |---------------- N iteration ---------------------|
+    # |- tokenA -|......................|-- newTokens ---|
+    # |---------- context_len ----------|
+    # |-------------------- seq_len ----------------------|
+    #                                   |-- query_len ---|
+
+    # seq_lens stored as a tensor.
+    seq_lens_tensor: Optional[torch.Tensor]
+
+    # FIXME: It is for flash attn.
+    # Maximum sequence length among prefill batch. 0 if there are decoding
+    # requests only.
+    max_prefill_seq_len: int
+    # Maximum sequence length among decode batch. 0 if there are prefill
+    # requests only.
+    max_decode_seq_len: int
+
+    # Whether or not if cuda graph is enabled.
+    # Cuda-graph is currently enabled for decoding only.
+    # TODO(woosuk): Move `use_cuda_graph` out since it's unrelated to attention.
+    use_cuda_graph: bool
+
+    # (batch_size,). The sequence length per sequence. Sequence length means
+    # the computed tokens + new tokens None if it is a decoding.
+    seq_lens: Optional[List[int]] = None
+
+    # FIXME: It is for flash attn.
+    # (batch_size + 1,). The cumulative sequence lengths of the sequences in
+    # the batch, used to index into sequence. E.g., if the sequence length is
+    # [4, 6], it is [0, 4, 10].
+    seq_start_loc: Optional[torch.Tensor] = None
+
+    # (batch_size,) A tensor of context lengths (tokens that are computed
+    # so far).
+    context_lens_tensor: Optional[torch.Tensor] = None
+
+    # Maximum query length in the batch. None for decoding.
+    max_query_len: Optional[int] = None
+
+    # Max number of query tokens among request in the batch.
+    max_decode_query_len: Optional[int] = None
+
+    # (batch_size + 1,). The cumulative subquery lengths of the sequences in
+    # the batch, used to index into subquery. E.g., if the subquery length
+    # is [4, 6], it is [0, 4, 10].
+    query_start_loc: Optional[torch.Tensor] = None
+
+    # Self-attention prefill/decode metadata cache
+    _cached_prefill_metadata: Optional["XFormersMetadata"] = None
+    _cached_decode_metadata: Optional["XFormersMetadata"] = None
+
+    # Begin encoder attn & enc/dec cross-attn fields...
+
+    # Encoder sequence lengths representation
+    encoder_seq_lens: Optional[List[int]] = None
+    encoder_seq_lens_tensor: Optional[torch.Tensor] = None
+    # FIXME: It is for flash attn.
+    # (batch_size + 1,). The cumulative sequence lengths of the sequences in
+    # the batch, used to index into sequence. E.g., if the sequence length is
+    # [4, 6], it is [0, 4, 10].
+    encoder_seq_start_loc: Optional[torch.Tensor] = None
+
+    # Maximum sequence length among encoder sequences
+    max_encoder_seq_len: Optional[int] = None
+
+    # Number of tokens input to encoder
+    num_encoder_tokens: Optional[int] = None
+
+    # Cross-attention memory-mapping data structures: slot mapping
+    # and block tables
+    cross_slot_mapping: Optional[torch.Tensor] = None
+    cross_block_tables: Optional[torch.Tensor] = None
+
+    def __post_init__(self):
+        # Set during the execution of the first attention op.
+        # It is a list because it is needed to set per prompt
+        # when alibi slopes is used. It is because of the limitation
+        # from xformer API.
+        # will not appear in the __repr__ and __init__
+        self.attn_bias: Optional[List[AttentionBias]] = None
+        self.encoder_attn_bias: Optional[List[AttentionBias]] = None
+        self.cross_attn_bias: Optional[List[AttentionBias]] = None
+
+    @property
+    def is_all_encoder_attn_metadata_set(self):
+        '''
+        All attention metadata required for encoder attention is set.
+        '''
+        return is_all_encoder_attn_metadata_set(self)
+
+    @property
+    def is_all_cross_attn_metadata_set(self):
+        '''
+        All attention metadata required for enc/dec cross-attention is set.
+
+        Superset of encoder attention required metadata.
+        '''
+        return is_all_cross_attn_metadata_set(self)
+
+    @property
+    def prefill_metadata(self) -> Optional["XFormersMetadata"]:
+        if self.num_prefills == 0:
+            return None
+
+        if self._cached_prefill_metadata is not None:
+            # Recover cached prefill-phase attention
+            # metadata structure
+            return self._cached_prefill_metadata
+
+        assert ((self.seq_lens is not None)
+                or (self.encoder_seq_lens is not None))
+        assert ((self.seq_lens_tensor is not None)
+                or (self.encoder_seq_lens_tensor is not None))
+
+        # Compute some attn_metadata fields which default to None
+        query_start_loc = (None if self.query_start_loc is None else
+                           self.query_start_loc[:self.num_prefills + 1])
+        seq_start_loc = (None if self.seq_start_loc is None else
+                         self.seq_start_loc[:self.num_prefills + 1])
+        slot_mapping = (None if self.slot_mapping is None else
+                        self.slot_mapping[:self.num_prefill_tokens])
+        seq_lens = (None if self.seq_lens is None else
+                    self.seq_lens[:self.num_prefills])
+        seq_lens_tensor = (None if self.seq_lens_tensor is None else
+                           self.seq_lens_tensor[:self.num_prefills])
+        context_lens_tensor = (None if self.context_lens_tensor is None else
+                               self.context_lens_tensor[:self.num_prefills])
+        block_tables = (None if self.block_tables is None else
+                        self.block_tables[:self.num_prefills])
+
+        # Construct & cache prefill-phase attention metadata structure
+        self._cached_prefill_metadata = XFormersMetadata(
+            num_prefills=self.num_prefills,
+            num_prefill_tokens=self.num_prefill_tokens,
+            num_decode_tokens=0,
+            slot_mapping=slot_mapping,
+            multi_modal_placeholder_index_maps=self.
+            multi_modal_placeholder_index_maps,
+            enable_kv_scales_calculation=self.enable_kv_scales_calculation,
+            seq_lens=seq_lens,
+            seq_lens_tensor=seq_lens_tensor,
+            max_query_len=self.max_query_len,
+            max_prefill_seq_len=self.max_prefill_seq_len,
+            max_decode_seq_len=0,
+            query_start_loc=query_start_loc,
+            seq_start_loc=seq_start_loc,
+            context_lens_tensor=context_lens_tensor,
+            block_tables=block_tables,
+            use_cuda_graph=False,
+            # Begin encoder & cross attn fields below...
+            encoder_seq_lens=self.encoder_seq_lens,
+            encoder_seq_lens_tensor=self.encoder_seq_lens_tensor,
+            max_encoder_seq_len=self.max_encoder_seq_len,
+            cross_slot_mapping=self.cross_slot_mapping,
+            cross_block_tables=self.cross_block_tables)
+        return self._cached_prefill_metadata
+
+    @property
+    def decode_metadata(self) -> Optional["XFormersMetadata"]:
+        if self.num_decode_tokens == 0:
+            return None
+
+        if self._cached_decode_metadata is not None:
+            # Recover cached decode-phase attention
+            # metadata structure
+            return self._cached_decode_metadata
+        assert ((self.seq_lens_tensor is not None)
+                or (self.encoder_seq_lens_tensor is not None))
+
+        # Compute some attn_metadata fields which default to None
+        slot_mapping = (None if self.slot_mapping is None else
+                        self.slot_mapping[self.num_prefill_tokens:])
+        seq_lens_tensor = (None if self.seq_lens_tensor is None else
+                           self.seq_lens_tensor[self.num_prefills:])
+        block_tables = (None if self.block_tables is None else
+                        self.block_tables[self.num_prefills:])
+
+        # Construct & cache decode-phase attention metadata structure
+        self._cached_decode_metadata = XFormersMetadata(
+            num_prefills=0,
+            num_prefill_tokens=0,
+            num_decode_tokens=self.num_decode_tokens,
+            slot_mapping=slot_mapping,
+            multi_modal_placeholder_index_maps=None,
+            enable_kv_scales_calculation=True,
+            seq_lens_tensor=seq_lens_tensor,
+            max_prefill_seq_len=0,
+            max_decode_seq_len=self.max_decode_seq_len,
+            block_tables=block_tables,
+            use_cuda_graph=self.use_cuda_graph,
+            # Begin encoder & cross attn fields below...
+            encoder_seq_lens=self.encoder_seq_lens,
+            encoder_seq_lens_tensor=self.encoder_seq_lens_tensor,
+            max_encoder_seq_len=self.max_encoder_seq_len,
+            cross_slot_mapping=self.cross_slot_mapping,
+            cross_block_tables=self.cross_block_tables)
+
+        # Batch may be composed of prefill|decodes, adjust query start indices
+        # to refer to the start of decodes when the two are split apart.
+        # E.g. in tokens:[3 prefills|6 decodes], query_start_loc=[3,9] => [0,6].
+        if self._cached_decode_metadata.query_start_loc is not None:
+            qs = self._cached_decode_metadata.query_start_loc
+            self._cached_decode_metadata.query_start_loc = qs - qs[0]
+        return self._cached_decode_metadata
+
+
+def _get_attn_bias(
+    attn_metadata: XFormersMetadata,
+    attn_type: str,
+) -> Optional[AttentionBias]:
+    '''
+    Extract appropriate attention bias from attention metadata
+    according to attention type.
+
+    Arguments:
+
+    * attn_metadata: Attention metadata structure associated with attention
+    * attn_type: encoder attention, decoder self-attention,
+                 encoder/decoder cross-attention
+
+    Returns:
+    * Appropriate attention bias value given the attention type
+    '''
+
+    if (attn_type == AttentionType.DECODER
+            or attn_type == AttentionType.ENCODER_ONLY):
+        return attn_metadata.attn_bias
+    elif attn_type == AttentionType.ENCODER:
+        return attn_metadata.encoder_attn_bias
+    elif attn_type == AttentionType.ENCODER_DECODER:
+        return attn_metadata.cross_attn_bias
+    else:
+        raise AttributeError(f"Invalid attention type {str(attn_type)}")
+
+
+def _set_attn_bias(
+    attn_metadata: XFormersMetadata,
+    attn_bias: List[Optional[AttentionBias]],
+    attn_type: str,
+) -> None:
+    '''
+    Update appropriate attention bias field of attention metadata,
+    according to attention type.
+
+    Arguments:
+
+    * attn_metadata: Attention metadata structure associated with attention
+    * attn_bias: The desired attention bias value
+    * attn_type: encoder attention, decoder self-attention,
+                 encoder/decoder cross-attention
+    '''
+
+    if (attn_type == AttentionType.DECODER
+            or attn_type == AttentionType.ENCODER_ONLY):
+        attn_metadata.attn_bias = attn_bias
+    elif attn_type == AttentionType.ENCODER:
+        attn_metadata.encoder_attn_bias = attn_bias
+    elif attn_type == AttentionType.ENCODER_DECODER:
+        attn_metadata.cross_attn_bias = attn_bias
+    else:
+        raise AttributeError(f"Invalid attention type {str(attn_type)}")
+
+
+class XFormersMetadataBuilder(CommonMetadataBuilder[XFormersMetadata]):
+
+    _metadata_cls = XFormersMetadata
+
+
+class XFormersImpl(AttentionImpl[XFormersMetadata]):
+    """
+    If the input tensors contain prompt tokens, the layout is as follows:
+    |<--------------- num_prefill_tokens ----------------->|	
+    |<--prefill_0-->|<--prefill_1-->|...|<--prefill_N-1--->|
+
+    Otherwise, the layout is as follows:	
+    |<----------------- num_decode_tokens ------------------>|	
+    |<--decode_0-->|..........|<--decode_M-1-->|<--padding-->|
+
+    Generation tokens can contain padding when cuda-graph is used.
+    Currently, prompt tokens don't contain any padding.
+
+    The prompts might have different lengths, while the generation tokens
+    always have length 1.
+
+    If chunked prefill is enabled, prefill tokens and decode tokens can be
+    batched together in a flattened 1D query.
+
+    |<----- num_prefill_tokens ---->|<------- num_decode_tokens --------->|
+    |<-prefill_0->|...|<-prefill_N-1->|<--decode_0-->|...|<--decode_M-1-->|
+
+    Currently, cuda graph is disabled for chunked prefill, meaning there's no
+    padding between prefill and decode tokens.
+    """
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        num_kv_heads: int,
+        alibi_slopes: Optional[List[float]],
+        sliding_window: Optional[int],
+        kv_cache_dtype: str,
+        logits_soft_cap: Optional[float] = None,
+        attn_type: str = AttentionType.DECODER,
+        kv_sharing_target_layer_name: Optional[str] = None,
+        use_irope: bool = False,
+    ) -> None:
+        if kv_sharing_target_layer_name is not None:
+            raise NotImplementedError("KV sharing is not supported in V0 "
+                                      "XFORMERS backend.")
+        if logits_soft_cap is not None:
+            logger.warning_once("XFormers does not support logits soft cap. "
+                                "Outputs may be slightly off.")
+        if use_irope:
+            logger.warning_once(
+                "Using irope in XFormers is not supported yet, it will fall"
+                " back to global attention for long context.")
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.scale = float(scale)
+        self.num_kv_heads = num_kv_heads
+        if alibi_slopes is not None:
+            alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
+        self.alibi_slopes = alibi_slopes
+        self.sliding_window = sliding_window
+        self.kv_cache_dtype = kv_cache_dtype
+
+        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
+
+        supported_head_sizes = PagedAttention.get_supported_head_sizes()
+        if head_size not in supported_head_sizes:
+            raise ValueError(
+                f"Head size {head_size} is not supported by PagedAttention. "
+                f"Supported head sizes are: {supported_head_sizes}.")
+
+        self.attn_type = attn_type
+
+    def forward(
+        self,
+        layer: AttentionLayer,
+        query: torch.Tensor,
+        key: Optional[torch.Tensor],
+        value: Optional[torch.Tensor],
+        kv_cache: torch.Tensor,
+        attn_metadata: "XFormersMetadata",
+        output: Optional[torch.Tensor] = None,
+        output_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Forward pass with xFormers and PagedAttention.
+
+        For decoder-only models: query, key and value must be non-None.
+
+        For encoder/decoder models:
+        * XFormersImpl.forward() may be invoked for both self- and cross-
+          attention layers.
+        * For self-attention: query, key and value must be non-None.
+        * For cross-attention:
+            * Query must be non-None
+            * During prefill, key and value must be non-None; key and value
+              get cached for use during decode.
+            * During decode, key and value may be None, since:
+              (1) key and value tensors were cached during prefill, and
+              (2) cross-attention key and value tensors do not grow during
+                  decode
+        
+        A note on how the attn_type (attention type enum) argument impacts
+        attention forward() behavior:
+    
+            * DECODER: normal decoder-only behavior;
+                use decoder self-attention block table
+            * ENCODER: no KV caching; pass encoder sequence
+                attributes (encoder_seq_lens/encoder_seq_lens_tensor/
+                max_encoder_seq_len) to kernel, in lieu of decoder
+                sequence attributes (seq_lens/seq_lens_tensor/max_seq_len).
+                Used for encoder branch of encoder-decoder models.
+            * ENCODER_ONLY: no kv_caching, uses the normal attention 
+                attributes (seq_lens/seq_lens_tensor/max_seq_len).
+            * ENCODER_DECODER: cross-attention behavior;
+                use cross-attention block table for caching KVs derived
+                from encoder hidden states; since KV sequence lengths
+                will match encoder sequence lengths, pass encoder sequence
+                attributes to kernel (encoder_seq_lens/encoder_seq_lens_tensor/
+                max_encoder_seq_len)
+    
+        Args:
+            query: shape = [num_tokens, num_heads * head_size]
+            key: shape = [num_tokens, num_kv_heads * head_size]
+            value: shape = [num_tokens, num_kv_heads * head_size]
+            kv_cache = [2, num_blocks, block_size * num_kv_heads * head_size]
+                NOTE: kv_cache will be an empty tensor with shape [0]
+                for profiling run.
+            attn_metadata: Metadata for attention.
+            attn_type: Select attention type, between encoder attention,
+                       decoder self-attention, or encoder/decoder cross-
+                       attention. Defaults to decoder self-attention,
+                       which is the vLLM default generally
+        Returns:
+            shape = [num_tokens, num_heads * head_size]
+        """
+        if output_scale is not None:
+            raise NotImplementedError(
+                "fused output quantization is not yet supported"
+                " for XFormersImpl")
+
+        attn_type = self.attn_type
+        # Check that appropriate attention metadata attributes are
+        # selected for the desired attention type
+        if (attn_type == AttentionType.ENCODER
+                and (not attn_metadata.is_all_encoder_attn_metadata_set)):
+            raise AttributeError("Encoder attention requires setting "
+                                 "encoder metadata attributes.")
+
+        elif (attn_type == AttentionType.ENCODER_DECODER
+              and (not attn_metadata.is_all_cross_attn_metadata_set)):
+            raise AttributeError("Encoder/decoder cross-attention "
+                                 "requires setting cross-attention "
+                                 "metadata attributes.")
+
+        query = query.view(-1, self.num_heads, self.head_size)
+        if key is not None:
+            assert value is not None
+            key = key.view(-1, self.num_kv_heads, self.head_size)
+            value = value.view(-1, self.num_kv_heads, self.head_size)
+        else:
+            assert value is None
+
+        # Self-attention vs. cross-attention will impact
+        # which KV cache memory-mapping & which
+        # seqlen datastructures we utilize
+
+        if (attn_type != AttentionType.ENCODER and kv_cache.numel() > 0):
+            # KV-cache during decoder-self- or
+            # encoder-decoder-cross-attention, but not
+            # during encoder attention.
+            #
+            # Even if there are no new key/value pairs to cache,
+            # we still need to break out key_cache and value_cache
+            # i.e. for later use by paged attention
+            key_cache, value_cache = PagedAttention.split_kv_cache(
+                kv_cache, self.num_kv_heads, self.head_size)
+
+            if (key is not None) and (value is not None):
+
+                if attn_type == AttentionType.ENCODER_DECODER:
+                    # Update cross-attention KV cache (prefill-only)
+                    # During cross-attention decode, key & value will be None,
+                    # preventing this IF-statement branch from running
+                    updated_slot_mapping = attn_metadata.cross_slot_mapping
+                else:
+                    # Update self-attention KV cache (prefill/decode)
+                    updated_slot_mapping = attn_metadata.slot_mapping
+
+                # Reshape the input keys and values and store them in the cache.
+                # If kv_cache is not provided, the new key and value tensors are
+                # not cached. This happens during the initial memory
+                # profiling run.
+                PagedAttention.write_to_paged_cache(
+                    key, value, key_cache, value_cache, updated_slot_mapping,
+                    self.kv_cache_dtype, layer._k_scale, layer._v_scale)
+        (num_prefill_query_tokens, num_prefill_kv_tokens,
+        num_decode_query_tokens) = \
+            get_num_prefill_decode_query_kv_tokens(attn_metadata, attn_type)
+
+        output = torch.empty_like(query)
+        # Query for decode. KV is not needed because it is already cached.
+        decode_query = query[num_prefill_query_tokens:]
+        # QKV for prefill.
+        query = query[:num_prefill_query_tokens]
+        if key is not None and value is not None:
+            key = key[:num_prefill_kv_tokens]
+            value = value[:num_prefill_kv_tokens]
+
+        assert query.shape[0] == num_prefill_query_tokens
+        assert decode_query.shape[0] == num_decode_query_tokens
+
+        if prefill_meta := attn_metadata.prefill_metadata:
+            # Prompt run.
+            if kv_cache.numel() == 0 or prefill_meta.block_tables.numel() == 0:
+                # normal attention.
+                # block tables are empty if the prompt does not have a cached
+                # prefix.
+                out = self._run_memory_efficient_xformers_forward(
+                    query, key, value, prefill_meta, attn_type=attn_type)
+                assert out.shape == output[:num_prefill_query_tokens].shape
+                output[:num_prefill_query_tokens] = out
+            else:
+                assert attn_type != AttentionType.ENCODER_ONLY, (
+                    "Encoder-only models should not have prefix attention.")
+
+                assert prefill_meta.query_start_loc is not None
+                assert prefill_meta.max_query_len is not None
+
+                # prefix-enabled attention
+                # TODO(Hai) this triton kernel has regression issue (broke) to
+                # deal with different data types between KV and FP8 KV cache,
+                # to be addressed separately.
+                out = PagedAttention.forward_prefix(
+                    query,
+                    key,
+                    value,
+                    self.kv_cache_dtype,
+                    key_cache,
+                    value_cache,
+                    prefill_meta.block_tables,
+                    prefill_meta.query_start_loc,
+                    prefill_meta.seq_lens_tensor,
+                    prefill_meta.max_query_len,
+                    self.alibi_slopes,
+                    self.sliding_window,
+                    layer._k_scale,
+                    layer._v_scale,
+                )
+                assert output[:num_prefill_query_tokens].shape == out.shape
+                output[:num_prefill_query_tokens] = out
+
+        if decode_meta := attn_metadata.decode_metadata:
+            assert attn_type != AttentionType.ENCODER_ONLY, (
+                "Encoder-only models should not have decode metadata.")
+
+            (
+                seq_lens_arg,
+                max_seq_len_arg,
+                block_tables_arg,
+            ) = get_seq_len_block_table_args(decode_meta, False, attn_type)
+
+            output[num_prefill_query_tokens:] = PagedAttention.forward_decode(
+                decode_query,
+                key_cache,
+                value_cache,
+                block_tables_arg,
+                seq_lens_arg,
+                max_seq_len_arg,
+                self.kv_cache_dtype,
+                self.num_kv_heads,
+                self.scale,
+                self.alibi_slopes,
+                layer._k_scale,
+                layer._v_scale,
+            )
+
+        # Reshape the output tensor.
+        return output.view(-1, self.num_heads * self.head_size)
+
+    def _run_memory_efficient_xformers_forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_metadata: XFormersMetadata,
+        attn_type: str = AttentionType.DECODER,
+    ) -> torch.Tensor:
+        """Attention for 1D query of multiple prompts. Multiple prompt
+        tokens are flattened in to `query` input.
+
+        See https://facebookresearch.github.io/xformers/components/ops.html
+        for API spec.
+
+        Args:
+            output: shape = [num_prefill_tokens, num_heads, head_size]
+            query: shape = [num_prefill_tokens, num_heads, head_size]
+            key: shape = [num_prefill_tokens, num_kv_heads, head_size]
+            value: shape = [num_prefill_tokens, num_kv_heads, head_size]
+            attn_metadata: Metadata for attention.
+            attn_type: Select attention type, between encoder attention,
+                       decoder self-attention, or encoder/decoder cross-
+                       attention. Defaults to decoder self-attention,
+                       which is the vLLM default generally
+        """
+
+        original_query = query
+        if self.num_kv_heads != self.num_heads:
+            # GQA/MQA requires the shape [B, M, G, H, K].
+            # Note that the output also has the same shape (which is different
+            # from a spec from the doc).
+            query = query.view(query.shape[0], self.num_kv_heads,
+                               self.num_queries_per_kv, query.shape[-1])
+            key = key[:, :,
+                      None, :].expand(key.shape[0], self.num_kv_heads,
+                                      self.num_queries_per_kv, key.shape[-1])
+            value = value[:, :,
+                          None, :].expand(value.shape[0], self.num_kv_heads,
+                                          self.num_queries_per_kv,
+                                          value.shape[-1])
+
+        # Set attention bias if not provided. This typically happens at
+        # the very attention layer of every iteration.
+        # FIXME(woosuk): This is a hack.
+        attn_bias = _get_attn_bias(attn_metadata, attn_type)
+        if attn_bias is None:
+            if self.alibi_slopes is None:
+
+                # Cross attention block of decoder branch of encoder-decoder
+                # model uses seq_lens for dec / encoder_seq_lens for enc
+                if (attn_type == AttentionType.ENCODER_DECODER):
+                    assert attn_metadata.seq_lens is not None
+                    assert attn_metadata.encoder_seq_lens is not None
+
+                    # Cross-attention mask is non-causal
+                    attn_bias = BlockDiagonalMask.from_seqlens(
+                        attn_metadata.seq_lens,
+                        attn_metadata.encoder_seq_lens,
+                        device=query.device)
+
+                # Encoder branch of encoder-decoder model uses
+                # attn_metadata.encoder_seq_lens
+                elif attn_type == AttentionType.ENCODER:
+
+                    assert attn_metadata.encoder_seq_lens is not None
+
+                    # Encoder self-attention mask is non-causal
+                    attn_bias = BlockDiagonalMask.from_seqlens(
+                        attn_metadata.encoder_seq_lens, device=query.device)
+
+                # Self-attention block of encoder-only model just
+                # uses the seq_lens directly.
+                elif attn_type == AttentionType.ENCODER_ONLY:
+                    assert attn_metadata.seq_lens is not None
+
+                    # Encoder self-attention mask is non-causal
+                    attn_bias = BlockDiagonalMask.from_seqlens(
+                        attn_metadata.seq_lens, device=query.device)
+
+                # Self-attention block of decoder branch just
+                # uses the seq_lens directly
+                elif attn_type == AttentionType.DECODER:
+                    assert attn_metadata.seq_lens is not None
+
+                    # Decoder self-attention mask is causal
+                    attn_bias = BlockDiagonalCausalMask.from_seqlens(
+                        attn_metadata.seq_lens, device=query.device)
+                else:
+                    raise ValueError("Unknown AttentionType: %s", attn_type)
+
+                if self.sliding_window is not None:
+                    attn_bias = attn_bias.make_local_attention(
+                        self.sliding_window)
+                attn_bias = [attn_bias]
+            else:
+                assert attn_type == AttentionType.DECODER
+                assert attn_metadata.seq_lens is not None
+                attn_bias = _make_alibi_bias(self.alibi_slopes,
+                                             self.num_kv_heads, query.dtype,
+                                             attn_metadata.seq_lens)
+
+            _set_attn_bias(attn_metadata, attn_bias, attn_type)
+
+        # No alibi slopes.
+        # TODO(woosuk): Too many view operations. Let's try to reduce
+        # them in the future for code readability.
+        if self.alibi_slopes is None:
+            # Add the batch dimension.
+            query = query.unsqueeze(0)
+            key = key.unsqueeze(0)
+            value = value.unsqueeze(0)
+            out = xops.memory_efficient_attention_forward(
+                query,
+                key,
+                value,
+                attn_bias=attn_bias[0],
+                p=0.0,
+                scale=self.scale)
+            return out.view_as(original_query)
+
+        # Attention with alibi slopes.
+        # FIXME(woosuk): Because xformers does not support dynamic sequence
+        # lengths with custom attention bias, we process each prompt one by
+        # one. This is inefficient, especially when we have many short prompts.
+        assert attn_metadata.seq_lens is not None
+        output = torch.empty_like(original_query)
+        start = 0
+        for i, seq_len in enumerate(attn_metadata.seq_lens):
+            end = start + seq_len
+            out = xops.memory_efficient_attention_forward(
+                query[None, start:end],
+                key[None, start:end],
+                value[None, start:end],
+                attn_bias=attn_bias[i],
+                p=0.0,
+                scale=self.scale)
+            # TODO(woosuk): Unnecessary copy. Optimize.
+            output[start:end].copy_(out.view_as(original_query[start:end]))
+            start += seq_len
+        return output
+
+
+def _make_alibi_bias(
+    alibi_slopes: torch.Tensor,
+    num_kv_heads: int,
+    dtype: torch.dtype,
+    seq_lens: List[int],
+) -> List[AttentionBias]:
+    attn_biases: List[AttentionBias] = []
+    for seq_len in seq_lens:
+        bias = torch.arange(seq_len, dtype=dtype)
+        # NOTE(zhuohan): HF uses
+        #     `bias = bias[None, :].repeat(seq_len, 1)`
+        # here. We find that both biases give the same results, but
+        # the bias below more accurately follows the original ALiBi
+        # paper.
+        # Calculate a matrix where each element represents ith element- jth
+        # element.
+        bias = bias[None, :] - bias[:, None]
+
+        padded_len = (seq_len + 7) // 8 * 8
+        num_heads = alibi_slopes.shape[0]
+        bias = torch.empty(
+            1,  # batch size
+            num_heads,
+            seq_len,
+            padded_len,
+            device=alibi_slopes.device,
+            dtype=dtype,
+        )[:, :, :, :seq_len].copy_(bias)
+        bias.mul_(alibi_slopes[:, None, None])
+        attn_biases.append(LowerTriangularMaskWithTensorBias(bias))
+
+    return attn_biases
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/ops/flashmla.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/ops/flashmla.py
new file mode 100644
index 0000000000000000000000000000000000000000..1af26dfc3daa3dbc84cea249b4a1ddae04b79e51
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/ops/flashmla.py
@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# adapted from: https://github.com/deepseek-ai/FlashMLA/blob/main/flash_mla/flash_mla_interface.py
+from typing import Optional, Tuple
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+
+logger = init_logger(__name__)
+
+if current_platform.is_cuda():
+    try:
+        import vllm._flashmla_C  # noqa: F401
+        _flashmla_C_AVAILABLE = True
+    except ImportError:
+        _flashmla_C_AVAILABLE = False
+else:
+    _flashmla_C_AVAILABLE = False
+
+
+def is_flashmla_supported() -> Tuple[bool, Optional[str]]:
+    """
+    Return: is_supported_flag, unsupported_reason (optional).
+    """
+    if not current_platform.is_cuda():
+        return False, "FlashMLA is only supported on CUDA devices."
+    if current_platform.get_device_capability()[0] != 9:
+        return False, "FlashMLA is only supported on Hopper devices."
+    if not _flashmla_C_AVAILABLE:
+        return False, "vllm._flashmla_C is not available, likely was not "\
+            "compiled due to insufficient nvcc version or a supported arch "\
+            "(only sm90a currently) was not in the list of target arches to "\
+            "compile for."
+    return True, None
+
+
+def get_mla_metadata(
+    cache_seqlens: torch.Tensor,
+    num_heads_per_head_k: int,
+    num_heads_k: int,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Arguments:
+        cache_seqlens: (batch_size), dtype torch.int32.
+        num_heads_per_head_k: Equals to seq_len_q * num_heads_q // num_heads_k.
+        num_heads_k: num_heads_k.
+
+    Return:
+        tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), 
+                                 dtype torch.int32.
+        num_splits: (batch_size + 1), dtype torch.int32.
+    """
+    return torch.ops._flashmla_C.get_mla_metadata(cache_seqlens,
+                                                  num_heads_per_head_k,
+                                                  num_heads_k)
+
+
+def flash_mla_with_kvcache(
+    q: torch.Tensor,
+    k_cache: torch.Tensor,
+    block_table: torch.Tensor,
+    cache_seqlens: torch.Tensor,
+    head_dim_v: int,
+    tile_scheduler_metadata: torch.Tensor,
+    num_splits: torch.Tensor,
+    softmax_scale: Optional[float] = None,
+    causal: bool = False,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Arguments:
+        q: (batch_size, seq_len_q, num_heads_q, head_dim).
+        k_cache: (num_blocks, page_block_size, num_heads_k, head_dim).
+        block_table: (batch_size, max_num_blocks_per_seq), torch.int32.
+        cache_seqlens: (batch_size), torch.int32.
+        head_dim_v: Head_dim of v.
+        tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), 
+                                 torch.int32, return by get_mla_metadata.
+        num_splits: (batch_size + 1), torch.int32, return by get_mla_metadata.
+        softmax_scale: float. The scaling of QK^T before applying softmax. 
+                       Default to 1 / sqrt(head_dim).
+        causal: bool. Whether to apply causal attention mask.
+
+    Return:
+        out: (batch_size, seq_len_q, num_heads_q, head_dim_v).
+        softmax_lse: (batch_size, num_heads_q, seq_len_q), torch.float32.
+    """
+    if softmax_scale is None:
+        softmax_scale = q.shape[-1]**(-0.5)
+    out, softmax_lse = torch.ops._flashmla_C.fwd_kvcache_mla(
+        q,
+        k_cache,
+        head_dim_v,
+        cache_seqlens,
+        block_table,
+        softmax_scale,
+        causal,
+        tile_scheduler_metadata,
+        num_splits,
+    )
+    return out, softmax_lse
+
+
+#
+# TODO: Add fake functions
+#
+# @register_fake("_flashmla_C::get_mla_metadata")
+# def _get_mla_metadata_fake(....) -> Tuple[torch.Tensor, torch.Tensor]:
+#     return ....
+#
+# @register_fake("_flashmla_C::fwd_kvcache_mla")
+# def _fwd_kvcache_mla_fake(....) -> Tuple[torch.Tensor, torch.Tensor]:
+#     return ....
+#
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/ops/merge_attn_states.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/ops/merge_attn_states.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cb1a47394cf6bb04ec13a8810e54143ba167c53
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/ops/merge_attn_states.py
@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Optional
+
+import torch
+
+from vllm.platforms import current_platform
+
+
+def merge_attn_states(
+    output: torch.Tensor,
+    prefix_output: torch.Tensor,
+    prefix_lse: torch.Tensor,
+    suffix_output: torch.Tensor,
+    suffix_lse: torch.Tensor,
+    output_lse: Optional[torch.Tensor] = None,
+) -> None:
+
+    # NOTE(DefTruth): Currently, custom merge_attn_states CUDA kernel
+    # is not support for FP8 dtype, fallback to use Triton kernel.
+    def supported_dtypes(o: torch.Tensor) -> bool:
+        return o.dtype in [torch.float32, torch.half, torch.bfloat16]
+
+    # NOTE(DefTruth): Currently, custom merge_attn_states CUDA
+    # kernel load/store 128b(16 bytes) per memory issue within
+    # thread. Namely, the headsize(headdim) must be multiple of
+    # pack_size (float32 -> 4, half/bfloat16 -> 8).
+    def supported_headdim(o: torch.Tensor) -> bool:
+        headdim = o.shape[2]  # [NUM_TOKENS, NUM_HEADS, HEAD_SIZE]
+        if o.dtype == torch.float32:
+            return headdim % 4 == 0
+        return headdim % 8 == 0
+
+    if (current_platform.is_cuda() and supported_dtypes(output)
+            and supported_headdim(output)):
+        from vllm._custom_ops import merge_attn_states
+        return merge_attn_states(output, prefix_output, prefix_lse,
+                                 suffix_output, suffix_lse, output_lse)
+    else:
+        from vllm.attention.ops.triton_merge_attn_states import (
+            merge_attn_states)
+        return merge_attn_states(output, prefix_output, prefix_lse,
+                                 suffix_output, suffix_lse, output_lse)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/ops/prefix_prefill.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/ops/prefix_prefill.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1d41930f6231a51c6bb29d5f308bdf3f6e33bf0
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/attention/ops/prefix_prefill.py
@@ -0,0 +1,916 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# The kernels in this file are adapted from LightLLM's context_attention_fwd:
+# https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/context_flashattention_nopad.py
+
+import torch
+
+from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
+
+# Static kernels parameters
+BASE_BLOCK = 128 if current_platform.has_device_capability(80) else 64
+NUM_WARPS = 4 if current_platform.is_rocm() else 8
+
+# To check compatibility
+IS_TURING = current_platform.get_device_capability() == (7, 5)
+
+
+# Here's an example autotuner config for this kernel. This config does provide
+# a performance improvement, but dramatically increases first call latency in
+# triton 3.2. Because of this tradeoff, it's currently commented out.
+# @triton.autotune(
+#     configs=[
+#         triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, \
+#                         "num_unroll_cache": 4, \
+#                         "num_unroll_request": 1 } | \
+#                         ({"kpack": 2, "waves_per_eu": 2} \
+#                             if current_platform.is_rocm() else {}), \
+#                         num_warps=4, \
+#                         num_stages=1)
+#     ],
+#     key=["BLOCK_SIZE", "MAX_Q_LEN", "MAX_CTX_LEN"]
+# )
+@triton.jit
+def _fwd_kernel(Q,
+                K,
+                V,
+                K_cache,
+                V_cache,
+                sink_ptr,
+                B_Loc,
+                sm_scale,
+                k_scale,
+                v_scale,
+                B_Start_Loc,
+                B_Seqlen,
+                x: tl.constexpr,
+                Out,
+                stride_b_loc_b,
+                stride_b_loc_s,
+                stride_qbs,
+                stride_qh,
+                stride_qd,
+                stride_kbs,
+                stride_kh,
+                stride_kd,
+                stride_vbs,
+                stride_vh,
+                stride_vd,
+                stride_obs,
+                stride_oh,
+                stride_od,
+                stride_k_cache_bs,
+                stride_k_cache_h,
+                stride_k_cache_d,
+                stride_k_cache_bl: tl.constexpr,
+                stride_k_cache_x,
+                stride_v_cache_bs,
+                stride_v_cache_h,
+                stride_v_cache_d,
+                stride_v_cache_bl,
+                num_queries_per_kv: tl.constexpr,
+                IN_PRECISION: tl.constexpr,
+                BLOCK_M: tl.constexpr,
+                BLOCK_DMODEL: tl.constexpr,
+                BLOCK_DMODEL_PADDED: tl.constexpr,
+                BLOCK_SIZE: tl.constexpr,
+                BLOCK_N: tl.constexpr,
+                SLIDING_WINDOW: tl.constexpr,
+                num_unroll_cache: tl.constexpr,
+                num_unroll_request: tl.constexpr,
+                SKIP_DECODE: tl.constexpr,
+                USE_SINKS: tl.constexpr,
+                MAX_Q_LEN: tl.constexpr = 0,
+                MAX_CTX_LEN: tl.constexpr = 0):
+
+    cur_batch = tl.program_id(0)
+    cur_head = tl.program_id(1)
+    start_m = tl.program_id(2)
+
+    cur_kv_head = cur_head // num_queries_per_kv
+
+    cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
+    cur_batch_in_all_start_index = tl.load(B_Start_Loc + cur_batch)
+    cur_batch_in_all_stop_index = tl.load(B_Start_Loc + cur_batch + 1)
+    cur_batch_query_len = (cur_batch_in_all_stop_index -
+                           cur_batch_in_all_start_index)
+    cur_batch_ctx_len = cur_batch_seq_len - cur_batch_query_len
+
+    if SKIP_DECODE and cur_batch_query_len == 1:
+        return
+
+    # start position inside of the query
+    # generally, N goes over kv, while M goes over query_len
+    block_start_loc = BLOCK_M * start_m
+
+    # initialize offsets
+    # [BLOCK_SIZE]; starts at 0
+    offs_bs_n = tl.arange(0, BLOCK_SIZE)
+    # [N]; starts at 0
+    offs_n = tl.arange(0, BLOCK_N)
+    # [D]; starts at 0
+    offs_d = tl.arange(0, BLOCK_DMODEL_PADDED)
+    # [M]; starts at current position in query
+    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    # [M,D]
+    off_q = ((cur_batch_in_all_start_index + offs_m[:, None]) * stride_qbs +
+             cur_head * stride_qh + offs_d[None, :] * stride_qd)
+
+    dim_mask = tl.where(
+        tl.arange(0, BLOCK_DMODEL_PADDED) < BLOCK_DMODEL, 1,
+        0).to(tl.int1)  # [D]
+
+    q = tl.load(Q + off_q,
+                mask=dim_mask[None, :] &
+                (offs_m[:, None] < cur_batch_query_len),
+                other=0.0)  # [M,D]
+
+    # initialize pointer to m and l
+    if not USE_SINKS:
+        m_i = tl.full([BLOCK_M], float("-inf"), dtype=tl.float32)
+    else:
+        m_i = tl.load(
+            sink_ptr + tl.full([BLOCK_M], cur_head, dtype=tl.int64),
+            mask=(offs_m < cur_batch_query_len),
+            other=float("-inf"),
+        ).to(dtype=tl.float32)
+
+    l_i = tl.full([BLOCK_M], 1.0, dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, BLOCK_DMODEL_PADDED], dtype=tl.float32)  # [M,D]
+
+    # compute query against context (no causal mask here)
+    for start_n in tl.range(0, cur_batch_ctx_len, BLOCK_SIZE, \
+                            loop_unroll_factor=num_unroll_cache):
+        start_n = tl.multiple_of(start_n, BLOCK_SIZE)
+        # -- compute qk ----
+        bn = tl.load(B_Loc + cur_batch * stride_b_loc_b +
+                     (start_n // BLOCK_SIZE) * stride_b_loc_s)
+        # [D,BLOCK_SIZE]
+        off_k = (
+            bn[None, :] * stride_k_cache_bs + cur_kv_head * stride_k_cache_h +
+            (offs_d[:, None] // x) * stride_k_cache_d +
+            ((start_n + offs_bs_n[None, :]) % BLOCK_SIZE) * stride_k_cache_bl +
+            (offs_d[:, None] % x) * stride_k_cache_x)
+
+        # [BLOCK_SIZE,D]
+        off_v = (bn[:, None] * stride_v_cache_bs +
+                 cur_kv_head * stride_v_cache_h +
+                 offs_d[None, :] * stride_v_cache_d +
+                 offs_bs_n[:, None] * stride_v_cache_bl)
+
+        if start_n + BLOCK_SIZE > cur_batch_ctx_len or \
+            BLOCK_DMODEL != BLOCK_DMODEL_PADDED:
+            k_load = tl.load(
+                K_cache + off_k,
+                mask=dim_mask[:, None] &
+                ((start_n + offs_bs_n[None, :]) < cur_batch_ctx_len),
+                other=0.0)  # [D,N]
+        else:
+            k_load = tl.load(K_cache + off_k)
+
+        if k_load.dtype.is_fp8():
+            k = (k_load.to(tl.float32) * tl.load(k_scale)).to(q.dtype)
+        else:
+            k = k_load
+
+        qk = tl.zeros([BLOCK_M, BLOCK_SIZE], dtype=tl.float32)  # [M,N]
+        qk = tl.dot(q, k, acc=qk, input_precision=IN_PRECISION)
+        qk = tl.where((start_n + offs_bs_n[None, :]) < cur_batch_ctx_len, qk,
+                      float("-inf"))
+        qk *= sm_scale
+        if SLIDING_WINDOW > 0:
+            # (cur_batch_ctx_len + offs_m[:, None]) are the positions of
+            # Q entries in sequence
+            # (start_n + offs_bs_n[None, :]) are the positions of
+            # KV entries in sequence
+            # So the condition makes sure each entry in Q only attends
+            # to KV entries not more than SLIDING_WINDOW away.
+            #
+            # We can't use -inf here, because the
+            # sliding window may lead to the entire row being masked.
+            # This then makes m_ij contain -inf, which causes NaNs in
+            # exp().
+            qk = tl.where((cur_batch_ctx_len + offs_m[:, None]) -
+                          (start_n + offs_bs_n[None, :]) < SLIDING_WINDOW, qk,
+                          -10000)
+
+        # compute running maximum
+        m_ij = tl.maximum(m_i, tl.max(qk, axis=1))
+        p = tl.exp(qk - m_ij[:, None])
+        l_ij = tl.sum(p, axis=1)
+        alpha = tl.exp(m_i - m_ij)
+        acc = acc * alpha[:, None]
+
+        # update acc
+        if start_n + BLOCK_SIZE > cur_batch_ctx_len or \
+            BLOCK_DMODEL != BLOCK_DMODEL_PADDED:
+            v_load = tl.load(
+                V_cache + off_v,
+                mask=dim_mask[None, :] &
+                ((start_n + offs_bs_n[:, None]) < cur_batch_ctx_len),
+                other=0.0)  # [N,D]
+        else:
+            v_load = tl.load(V_cache + off_v)
+
+        if v_load.dtype.is_fp8():
+            v = (v_load.to(tl.float32) * tl.load(v_scale)).to(q.dtype)
+        else:
+            v = v_load
+        p = p.to(v.dtype)
+
+        acc = tl.dot(p, v, acc=acc, input_precision=IN_PRECISION)
+        # # update m_i and l_i
+        l_i = l_i * alpha + l_ij
+        m_i = m_ij
+
+    off_k = (offs_n[None, :] * stride_kbs + cur_kv_head * stride_kh +
+             offs_d[:, None] * stride_kd)
+    off_v = (offs_n[:, None] * stride_vbs + cur_kv_head * stride_vh +
+             offs_d[None, :] * stride_vd)
+    k_ptrs = K + off_k
+    v_ptrs = V + off_v
+
+    # block_mask is 0 when we're already past the current query length
+    block_mask = tl.where(block_start_loc < cur_batch_query_len, 1, 0)
+
+    # compute query against itself (with causal mask)
+    for start_n in tl.range(0, \
+                        block_mask * (start_m + 1) * BLOCK_M, BLOCK_N, \
+                        loop_unroll_factor=num_unroll_request):
+        start_n = tl.multiple_of(start_n, BLOCK_N)
+        # -- compute qk ----
+        k = tl.load(k_ptrs +
+                    (cur_batch_in_all_start_index + start_n) * stride_kbs,
+                    mask=dim_mask[:, None] &
+                    ((start_n + offs_n[None, :]) < cur_batch_query_len),
+                    other=0.0)
+
+        qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        qk = tl.dot(q, k, acc=qk, input_precision=IN_PRECISION)
+        qk *= sm_scale
+        # apply causal mask
+        qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk,
+                      float("-inf"))
+        if SLIDING_WINDOW > 0:
+            qk = tl.where(
+                offs_m[:, None] - (start_n + offs_n[None, :]) < SLIDING_WINDOW,
+                qk, -10000)
+
+        # compute running maximum
+        m_ij = tl.maximum(m_i, tl.max(qk, axis=1))
+        p = tl.exp(qk - m_ij[:, None])
+        l_ij = tl.sum(p, axis=1)
+        alpha = tl.exp(m_i - m_ij)
+        acc = acc * alpha[:, None]
+
+        # update acc
+        v = tl.load(v_ptrs +
+                    (cur_batch_in_all_start_index + start_n) * stride_vbs,
+                    mask=dim_mask[None, :] &
+                    ((start_n + offs_n[:, None]) < cur_batch_query_len),
+                    other=0.0)
+        p = p.to(v.dtype)
+
+        acc = tl.dot(p, v, acc=acc, input_precision=IN_PRECISION)
+        # update m_i and l_i
+        l_i = l_i * alpha + l_ij
+        m_i = m_ij
+
+    acc = acc / l_i[:, None]
+
+    # initialize pointers to output
+    off_o = ((cur_batch_in_all_start_index + offs_m[:, None]) * stride_obs +
+             cur_head * stride_oh + offs_d[None, :] * stride_od)
+    out_ptrs = Out + off_o
+    tl.store(out_ptrs,
+             acc,
+             mask=dim_mask[None, :] & (offs_m[:, None] < cur_batch_query_len))
+    return
+
+
+@triton.jit
+def _fwd_kernel_flash_attn_v2(
+    Q,
+    K,
+    V,
+    K_cache,
+    V_cache,
+    B_Loc,
+    sm_scale,
+    B_Start_Loc,
+    B_Seqlen,
+    B_Ctxlen,
+    block_size,
+    x,
+    Out,
+    stride_b_loc_b,
+    stride_b_loc_s,
+    stride_qbs,
+    stride_qh,
+    stride_qd,
+    stride_kbs,
+    stride_kh,
+    stride_kd,
+    stride_vbs,
+    stride_vh,
+    stride_vd,
+    stride_obs,
+    stride_oh,
+    stride_od,
+    stride_k_cache_bs,
+    stride_k_cache_h,
+    stride_k_cache_d,
+    stride_k_cache_bl,
+    stride_k_cache_x,
+    stride_v_cache_bs,
+    stride_v_cache_h,
+    stride_v_cache_d,
+    stride_v_cache_bl,
+    num_queries_per_kv: int,
+    BLOCK_M: tl.constexpr,
+    BLOCK_DMODEL: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    cur_batch = tl.program_id(0)
+    cur_head = tl.program_id(1)
+    start_m = tl.program_id(2)
+
+    cur_kv_head = cur_head // num_queries_per_kv
+
+    cur_batch_ctx_len = tl.load(B_Ctxlen + cur_batch)
+    cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
+    cur_batch_in_all_start_index = tl.load(B_Start_Loc + cur_batch)
+
+    block_start_loc = BLOCK_M * start_m
+
+    # initialize offsets
+    offs_n = tl.arange(0, BLOCK_N)
+    offs_d = tl.arange(0, BLOCK_DMODEL)
+    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    off_q = ((cur_batch_in_all_start_index + offs_m[:, None]) * stride_qbs +
+             cur_head * stride_qh + offs_d[None, :] * stride_qd)
+
+    q = tl.load(Q + off_q,
+                mask=offs_m[:, None] < cur_batch_seq_len - cur_batch_ctx_len,
+                other=0.0)
+
+    # # initialize pointer to m and l
+    m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
+    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
+
+    for start_n in range(0, cur_batch_ctx_len, BLOCK_N):
+        start_n = tl.multiple_of(start_n, BLOCK_N)
+        # -- compute qk ----
+        bn = tl.load(B_Loc + cur_batch * stride_b_loc_b +
+                     ((start_n + offs_n) // block_size) * stride_b_loc_s,
+                     mask=(start_n + offs_n) < cur_batch_ctx_len,
+                     other=0)
+        off_k = (
+            bn[None, :] * stride_k_cache_bs + cur_kv_head * stride_k_cache_h +
+            (offs_d[:, None] // x) * stride_k_cache_d +
+            ((start_n + offs_n[None, :]) % block_size) * stride_k_cache_bl +
+            (offs_d[:, None] % x) * stride_k_cache_x)
+        off_v = (bn[:, None] * stride_v_cache_bs +
+                 cur_kv_head * stride_v_cache_h +
+                 offs_d[None, :] * stride_v_cache_d +
+                 (start_n + offs_n[:, None]) % block_size * stride_v_cache_bl)
+        k = tl.load(K_cache + off_k,
+                    mask=(start_n + offs_n[None, :]) < cur_batch_ctx_len,
+                    other=0.0)
+        qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        qk += tl.dot(q, k)
+        qk = tl.where((start_n + offs_n[None, :]) < cur_batch_ctx_len, qk,
+                      float("-inf"))
+        qk *= sm_scale
+
+        # -- compute m_ij, p, l_ij
+        m_ij = tl.max(qk, 1)
+        m_i_new = tl.maximum(m_i, m_ij)
+        p = tl.math.exp(qk - m_i_new[:, None])
+        l_ij = tl.sum(p, 1)
+        # -- update m_i and l_i
+
+        alpha = tl.math.exp(m_i - m_i_new)
+        l_i_new = alpha * l_i + l_ij
+        # -- update output accumulator --
+        # scale p
+        # scale acc
+        acc_scale = alpha
+        # acc_scale = l_i / l_i_new * alpha
+        acc = acc * acc_scale[:, None]
+        # update acc
+        v = tl.load(V_cache + off_v,
+                    mask=(start_n + offs_n[:, None]) < cur_batch_ctx_len,
+                    other=0.0)
+
+        p = p.to(v.dtype)
+        acc += tl.dot(p, v)
+        # update m_i and l_i
+        l_i = l_i_new
+        m_i = m_i_new
+
+    off_k = (offs_n[None, :] * stride_kbs + cur_kv_head * stride_kh +
+             offs_d[:, None] * stride_kd)
+    off_v = (offs_n[:, None] * stride_vbs + cur_kv_head * stride_vh +
+             offs_d[None, :] * stride_vd)
+    k_ptrs = K + off_k
+    v_ptrs = V + off_v
+
+    block_mask = tl.where(
+        block_start_loc < cur_batch_seq_len - cur_batch_ctx_len, 1, 0)
+
+    for start_n in range(0, block_mask * (start_m + 1) * BLOCK_M, BLOCK_N):
+        start_n = tl.multiple_of(start_n, BLOCK_N)
+        # -- compute qk ----
+        k = tl.load(k_ptrs +
+                    (cur_batch_in_all_start_index + start_n) * stride_kbs,
+                    mask=(start_n + offs_n[None, :])
+                    < cur_batch_seq_len - cur_batch_ctx_len,
+                    other=0.0)
+
+        qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        qk += tl.dot(q, k)
+        qk *= sm_scale
+        qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk,
+                      float("-inf"))
+
+        # -- compute m_ij, p, l_ij
+        m_ij = tl.max(qk, 1)
+        m_i_new = tl.maximum(m_i, m_ij)
+        p = tl.math.exp(qk - m_i_new[:, None])
+        l_ij = tl.sum(p, 1)
+        # -- update m_i and l_i
+
+        alpha = tl.math.exp(m_i - m_i_new)
+        l_i_new = alpha * l_i + l_ij
+        # -- update output accumulator --
+        # scale p
+        # scale acc
+        acc_scale = alpha
+        # acc_scale = l_i / l_i_new * alpha
+        acc = acc * acc_scale[:, None]
+        # update acc
+        v = tl.load(v_ptrs +
+                    (cur_batch_in_all_start_index + start_n) * stride_vbs,
+                    mask=(start_n + offs_n[:, None])
+                    < cur_batch_seq_len - cur_batch_ctx_len,
+                    other=0.0)
+
+        p = p.to(v.dtype)
+        acc += tl.dot(p, v)
+        # update m_i and l_i
+        l_i = l_i_new
+        m_i = m_i_new
+
+    # acc /= l_i[:, None]
+    # initialize pointers to output
+    off_o = ((cur_batch_in_all_start_index + offs_m[:, None]) * stride_obs +
+             cur_head * stride_oh + offs_d[None, :] * stride_od)
+    out_ptrs = Out + off_o
+    tl.store(out_ptrs,
+             acc,
+             mask=offs_m[:, None] < cur_batch_seq_len - cur_batch_ctx_len)
+    return
+
+
+@triton.jit
+def _fwd_kernel_alibi(
+    Q,
+    K,
+    V,
+    K_cache,
+    V_cache,
+    B_Loc,
+    sm_scale,
+    k_scale,
+    v_scale,
+    B_Start_Loc,
+    B_Seqlen,
+    Alibi_slopes,
+    block_size,
+    x,
+    Out,
+    stride_b_loc_b,
+    stride_b_loc_s,
+    stride_qbs,
+    stride_qh,
+    stride_qd,
+    stride_kbs,
+    stride_kh,
+    stride_kd,
+    stride_vbs,
+    stride_vh,
+    stride_vd,
+    stride_obs,
+    stride_oh,
+    stride_od,
+    stride_k_cache_bs,
+    stride_k_cache_h,
+    stride_k_cache_d,
+    stride_k_cache_bl,
+    stride_k_cache_x,
+    stride_v_cache_bs,
+    stride_v_cache_h,
+    stride_v_cache_d,
+    stride_v_cache_bl,
+    num_queries_per_kv: int,
+    IN_PRECISION: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_DMODEL: tl.constexpr,  # head size
+    BLOCK_DMODEL_PADDED: tl.constexpr,  # head size padded to a power of 2
+    BLOCK_N: tl.constexpr,
+    SKIP_DECODE: tl.constexpr,
+):
+    # attn_bias[]
+    cur_batch = tl.program_id(0)
+    cur_head = tl.program_id(1)
+    start_m = tl.program_id(2)
+
+    cur_kv_head = cur_head // num_queries_per_kv
+
+    # cur_batch_seq_len: the length of prompts
+    # cur_batch_ctx_len: the length of prefix
+    # cur_batch_in_all_start_index: the start id of the dim=0
+    cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
+    cur_batch_in_all_start_index = tl.load(B_Start_Loc + cur_batch)
+    cur_batch_in_all_stop_index = tl.load(B_Start_Loc + cur_batch + 1)
+    cur_batch_query_len = (cur_batch_in_all_stop_index -
+                           cur_batch_in_all_start_index)
+    cur_batch_ctx_len = cur_batch_seq_len - cur_batch_query_len
+
+    if SKIP_DECODE and cur_batch_query_len == 1:
+        return
+
+    block_start_loc = BLOCK_M * start_m
+
+    # initialize offsets
+    offs_n = tl.arange(0, BLOCK_N)
+    offs_d = tl.arange(0, BLOCK_DMODEL_PADDED)
+    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    off_q = ((cur_batch_in_all_start_index + offs_m[:, None]) * stride_qbs +
+             cur_head * stride_qh + offs_d[None, :] * stride_qd)
+
+    dim_mask = tl.where(
+        tl.arange(0, BLOCK_DMODEL_PADDED) < BLOCK_DMODEL, 1, 0).to(tl.int1)
+
+    q = tl.load(Q + off_q,
+                mask=dim_mask[None, :] &
+                (offs_m[:, None] < cur_batch_seq_len - cur_batch_ctx_len),
+                other=0.0)
+
+    # # initialize pointer to m and l
+    m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
+    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, BLOCK_DMODEL_PADDED], dtype=tl.float32)
+
+    alibi_slope = tl.load(Alibi_slopes + cur_head)
+    alibi_start_q = tl.arange(0, BLOCK_M) + block_start_loc + cur_batch_ctx_len
+    alibi_start_k = 0
+    for start_n in range(0, cur_batch_ctx_len, BLOCK_N):
+        start_n = tl.multiple_of(start_n, BLOCK_N)
+        # -- compute qk ----
+        bn = tl.load(B_Loc + cur_batch * stride_b_loc_b +
+                     ((start_n + offs_n) // block_size) * stride_b_loc_s,
+                     mask=(start_n + offs_n) < cur_batch_ctx_len,
+                     other=0)
+        off_k = (
+            bn[None, :] * stride_k_cache_bs + cur_kv_head * stride_k_cache_h +
+            (offs_d[:, None] // x) * stride_k_cache_d +
+            ((start_n + offs_n[None, :]) % block_size) * stride_k_cache_bl +
+            (offs_d[:, None] % x) * stride_k_cache_x)
+        off_v = (bn[:, None] * stride_v_cache_bs +
+                 cur_kv_head * stride_v_cache_h +
+                 offs_d[None, :] * stride_v_cache_d +
+                 (start_n + offs_n[:, None]) % block_size * stride_v_cache_bl)
+        k_load = tl.load(K_cache + off_k,
+                         mask=dim_mask[:, None] &
+                         ((start_n + offs_n[None, :]) < cur_batch_ctx_len),
+                         other=0.0)  # [D,N]
+
+        if k_load.dtype.is_fp8():
+            k = (k_load.to(tl.float32) * tl.load(k_scale)).to(q.dtype)
+        else:
+            k = k_load
+
+        qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        qk = tl.dot(q, k, acc=qk, input_precision=IN_PRECISION)
+        qk = tl.where((start_n + offs_n[None, :]) < cur_batch_ctx_len, qk,
+                      float("-inf"))
+        qk *= sm_scale
+
+        # load alibi
+        alibi = (tl.arange(0, BLOCK_N)[None, :] + alibi_start_k -
+                 alibi_start_q[:, None]) * alibi_slope
+        alibi = tl.where(
+            (alibi <= 0) & (alibi_start_q[:, None] < cur_batch_seq_len), alibi,
+            float("-inf"))
+        qk += alibi
+        alibi_start_k += BLOCK_N
+
+        # -- compute m_ij, p, l_ij
+        m_ij = tl.max(qk, 1)
+        m_i_new = tl.maximum(m_i, m_ij)
+        p = tl.math.exp(qk - m_i_new[:, None])
+        l_ij = tl.sum(p, 1)
+        # -- update m_i and l_i
+
+        alpha = tl.math.exp(m_i - m_i_new)
+        l_i_new = alpha * l_i + l_ij
+        # -- update output accumulator --
+        # scale p
+        # scale acc
+        acc_scale = alpha
+        # acc_scale = l_i / l_i_new * alpha
+        acc = acc * acc_scale[:, None]
+        # update acc
+        v_load = tl.load(V_cache + off_v,
+                         mask=dim_mask[None, :] &
+                         ((start_n + offs_n[:, None]) < cur_batch_ctx_len),
+                         other=0.0)
+        if v_load.dtype.is_fp8():
+            v = (v_load.to(tl.float32) * tl.load(v_scale)).to(q.dtype)
+        else:
+            v = v_load
+        p = p.to(v.dtype)
+
+        acc = tl.dot(p, v, acc=acc, input_precision='ieee')
+        # update m_i and l_i
+        l_i = l_i_new
+        m_i = m_i_new
+
+    off_k = (offs_n[None, :] * stride_kbs + cur_kv_head * stride_kh +
+             offs_d[:, None] * stride_kd)
+    off_v = (offs_n[:, None] * stride_vbs + cur_kv_head * stride_vh +
+             offs_d[None, :] * stride_vd)
+    k_ptrs = K + off_k
+    v_ptrs = V + off_v
+
+    block_mask = tl.where(
+        block_start_loc < cur_batch_seq_len - cur_batch_ctx_len, 1, 0)
+
+    # init alibi
+    alibi_slope = tl.load(Alibi_slopes + cur_head)
+    alibi_start_q = tl.arange(0, BLOCK_M) + block_start_loc + cur_batch_ctx_len
+    alibi_start_k = cur_batch_ctx_len
+    # # init debugger
+    # offset_db_q = tl.arange(0, BLOCK_M) + block_start_loc
+    # offset_db_k = tl.arange(0, BLOCK_N)
+    # calc q[BLOCK_M, BLOCK_MODEL] mul k[prefix_len: , BLOCK_DMODEL]
+    for start_n in range(0, block_mask * (start_m + 1) * BLOCK_M, BLOCK_N):
+        start_n = tl.multiple_of(start_n, BLOCK_N)
+        # -- compute qk ----
+        k = tl.load(
+            k_ptrs + (cur_batch_in_all_start_index + start_n) * stride_kbs,
+            mask=dim_mask[:, None] & ((start_n + offs_n[None, :])
+                                      < cur_batch_seq_len - cur_batch_ctx_len),
+            other=0.0)
+
+        qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        qk = tl.dot(q, k, acc=qk, input_precision='ieee')
+        qk *= sm_scale
+        qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk,
+                      float("-inf"))
+
+        # load alibi
+        alibi = (tl.arange(0, BLOCK_N)[None, :] + alibi_start_k -
+                 alibi_start_q[:, None]) * alibi_slope
+        alibi = tl.where(
+            (alibi <= 0) & (alibi_start_q[:, None] < cur_batch_seq_len), alibi,
+            float("-inf"))
+        qk += alibi
+        alibi_start_k += BLOCK_N
+
+        # -- compute m_ij, p, l_ij
+        m_ij = tl.max(qk, 1)
+        m_i_new = tl.maximum(m_i, m_ij)
+        p = tl.math.exp(qk - m_i_new[:, None])
+        l_ij = tl.sum(p, 1)
+        # -- update m_i and l_i
+
+        alpha = tl.math.exp(m_i - m_i_new)
+        l_i_new = alpha * l_i + l_ij
+        # -- update output accumulator --
+        # scale p
+        # scale acc
+        acc_scale = alpha
+        # acc_scale = l_i / l_i_new * alpha
+        acc = acc * acc_scale[:, None]
+        # update acc
+        v = tl.load(
+            v_ptrs + (cur_batch_in_all_start_index + start_n) * stride_vbs,
+            mask=dim_mask[None, :] & ((start_n + offs_n[:, None])
+                                      < cur_batch_seq_len - cur_batch_ctx_len),
+            other=0.0)
+        p = p.to(v.dtype)
+
+        acc = tl.dot(p, v, acc=acc, input_precision='ieee')
+        # update m_i and l_i
+        l_i = l_i_new
+        m_i = m_i_new
+
+    acc = acc / l_i[:, None]
+
+    # initialize pointers to output
+    off_o = ((cur_batch_in_all_start_index + offs_m[:, None]) * stride_obs +
+             cur_head * stride_oh + offs_d[None, :] * stride_od)
+    out_ptrs = Out + off_o
+    tl.store(out_ptrs,
+             acc,
+             mask=dim_mask[None, :] &
+             (offs_m[:, None] < cur_batch_seq_len - cur_batch_ctx_len))
+    return
+
+
+@torch.inference_mode()
+def context_attention_fwd(q,
+                          k,
+                          v,
+                          o,
+                          kv_cache_dtype: str,
+                          k_cache,
+                          v_cache,
+                          b_loc,
+                          b_start_loc,
+                          b_seq_len,
+                          max_seq_len,
+                          max_input_len,
+                          k_scale: torch.Tensor,
+                          v_scale: torch.Tensor,
+                          alibi_slopes=None,
+                          sliding_window=None,
+                          sm_scale=None,
+                          skip_decode=False,
+                          sinks=None):
+
+    q_dtype_is_f32 = q.dtype is torch.float32
+
+    # Turing does have tensor core for float32 multiplication
+    # use ieee as fallback for triton kernels work. There is also
+    # warning on vllm/config.py to inform users this fallback
+    # implementation
+    IN_PRECISION = 'ieee' if IS_TURING and q_dtype_is_f32 else None
+
+    # Conversion of FP8 Tensor from uint8 storage to
+    # appropriate torch.dtype for interpretation by Triton
+    if "fp8" in kv_cache_dtype:
+        assert k_cache.dtype in [torch.uint8, current_platform.fp8_dtype()]
+        assert v_cache.dtype in [torch.uint8, current_platform.fp8_dtype()]
+
+        if kv_cache_dtype in ("fp8", "fp8_e4m3"):
+            target_dtype = current_platform.fp8_dtype()
+        elif kv_cache_dtype == "fp8_e5m2":
+            target_dtype = torch.float8_e5m2
+        else:
+            raise ValueError("Unsupported FP8 dtype:", kv_cache_dtype)
+
+        k_cache = k_cache.view(target_dtype)
+        v_cache = v_cache.view(target_dtype)
+
+    if (k_cache.dtype == torch.uint8
+            or v_cache.dtype == torch.uint8 and kv_cache_dtype == "auto"):
+        raise ValueError("kv_cache_dtype='auto' unsupported for\
+            FP8 KV Cache prefill kernel")
+
+    # shape constraints
+    Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
+    assert Lq == Lk and Lk == Lv
+    # round up Lk to a power of 2 - this is required for Triton block size
+    Lk_padded = triton.next_power_of_2(Lk)
+
+    if sm_scale is None:
+        sm_scale = 1.0 / (Lq**0.5)
+    batch, head = b_seq_len.shape[0], q.shape[1]
+    num_queries_per_kv = q.shape[1] // k.shape[1]
+
+    assert batch + 1 == len(b_start_loc)
+
+    # 0 means "disable"
+    if sliding_window is None or sliding_window <= 0:
+        sliding_window = 0
+
+    if alibi_slopes is not None:
+        assert sinks is None, "Sinks arg is not supported with alibi"
+        # need to reduce num. blocks when using fp32
+        # due to increased use of GPU shared memory
+        # if q.dtype is torch.float32:
+        BLOCK = BASE_BLOCK // 2 if q_dtype_is_f32 else BASE_BLOCK
+        # batch, head,
+        grid = (batch, head, triton.cdiv(max_input_len, BLOCK))
+        _fwd_kernel_alibi[grid](
+            q,
+            k,
+            v,
+            k_cache,
+            v_cache,
+            b_loc,
+            sm_scale,
+            k_scale,
+            v_scale,
+            b_start_loc,
+            b_seq_len,
+            alibi_slopes,
+            v_cache.shape[3],
+            k_cache.shape[4],
+            o,
+            b_loc.stride(0),
+            b_loc.stride(1),
+            q.stride(0),
+            q.stride(1),
+            q.stride(2),
+            k.stride(0),
+            k.stride(1),
+            k.stride(2),
+            v.stride(0),
+            v.stride(1),
+            v.stride(2),
+            o.stride(0),
+            o.stride(1),
+            o.stride(2),
+            k_cache.stride(0),
+            k_cache.stride(1),
+            k_cache.stride(2),
+            k_cache.stride(3),
+            k_cache.stride(
+                4),  #[num_blocks, num_kv_heads, head_size/x, block_size, x]
+            v_cache.stride(0),
+            v_cache.stride(1),
+            v_cache.stride(2),
+            v_cache.stride(
+                3),  #[num_blocks, num_kv_heads, head_size, block_size]
+            num_queries_per_kv=num_queries_per_kv,
+            IN_PRECISION=IN_PRECISION,
+            BLOCK_M=BLOCK,
+            BLOCK_DMODEL=Lk,
+            BLOCK_DMODEL_PADDED=Lk_padded,
+            BLOCK_N=BLOCK,
+            SKIP_DECODE=skip_decode,
+            num_warps=NUM_WARPS,
+            num_stages=1,
+        )
+        return
+
+    max_seq_len = 0 if max_seq_len is None else max_seq_len
+    extra_kargs = {}
+    if current_platform.is_rocm():
+        extra_kargs = {"kpack": 1, "waves_per_eu": 2}
+
+    grid = lambda META: (batch, head,
+                         triton.cdiv(max_input_len, META["BLOCK_M"]))
+    _fwd_kernel[grid](
+        q,
+        k,
+        v,
+        k_cache,
+        v_cache,
+        sinks,
+        b_loc,
+        sm_scale,
+        k_scale,
+        v_scale,
+        b_start_loc,
+        b_seq_len,
+        k_cache.shape[4],
+        o,
+        b_loc.stride(0),
+        b_loc.stride(1),
+        q.stride(0),
+        q.stride(1),
+        q.stride(2),
+        k.stride(0),
+        k.stride(1),
+        k.stride(2),
+        v.stride(0),
+        v.stride(1),
+        v.stride(2),
+        o.stride(0),
+        o.stride(1),
+        o.stride(2),
+        k_cache.stride(0),
+        k_cache.stride(1),
+        k_cache.stride(2),
+        k_cache.stride(3),
+        k_cache.stride(
+            4),  #[num_blocks, num_kv_heads, head_size/x, block_size, x]
+        v_cache.stride(0),
+        v_cache.stride(1),
+        v_cache.stride(2),
+        v_cache.stride(3),  #[num_blocks, num_kv_heads, head_size, block_size]
+        BLOCK_SIZE=v_cache.shape[3],
+        num_queries_per_kv=num_queries_per_kv,
+        IN_PRECISION=IN_PRECISION,
+        BLOCK_DMODEL=Lk,
+        BLOCK_DMODEL_PADDED=Lk_padded,
+        SLIDING_WINDOW=sliding_window,
+        SKIP_DECODE=skip_decode,
+        BLOCK_M=128,
+        BLOCK_N=64,
+        num_unroll_cache=4,
+        num_unroll_request=1,
+        num_warps=4,
+        num_stages=1,
+        USE_SINKS=sinks is not None,
+        **extra_kargs)
+    return
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/core/block/common.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/core/block/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..a337007a9eaa6f3bfc46cb9b9fa0f5585590cfc9
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/core/block/common.py
@@ -0,0 +1,371 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections import deque
+from dataclasses import dataclass
+from typing import Deque, Dict, Iterable, List, Optional, Protocol, Tuple
+
+from vllm.core.block.interfaces import Block, BlockAllocator
+
+BlockId = int
+RefCount = int
+
+
+class RefCounterProtocol(Protocol):
+
+    def incr(self, block_id: BlockId) -> RefCount:
+        raise NotImplementedError
+
+    def decr(self, block_id: BlockId) -> RefCount:
+        raise NotImplementedError
+
+    def get(self, block_id: BlockId) -> RefCount:
+        raise NotImplementedError
+
+
+class RefCounter(RefCounterProtocol):
+    """A class for managing reference counts for a set of block indices.
+
+    The RefCounter class maintains a dictionary that maps block indices to their
+    corresponding reference counts. It provides methods to increment, decrement,
+    and retrieve the reference count for a given block index.
+
+    Args:
+        all_block_indices (Iterable[BlockId]): An iterable of block indices
+            to initialize the reference counter with.
+    """
+
+    def __init__(self, all_block_indices: Iterable[BlockId]):
+        deduped = set(all_block_indices)
+        self._refcounts: Dict[BlockId, RefCount] = {
+            index: 0
+            for index in deduped
+        }
+
+    def incr(self, block_id: BlockId) -> RefCount:
+        assert block_id in self._refcounts
+        pre_incr_refcount = self._refcounts[block_id]
+
+        assert pre_incr_refcount >= 0
+
+        post_incr_refcount = pre_incr_refcount + 1
+        self._refcounts[block_id] = post_incr_refcount
+        return post_incr_refcount
+
+    def decr(self, block_id: BlockId) -> RefCount:
+        assert block_id in self._refcounts
+        refcount = self._refcounts[block_id]
+
+        assert refcount > 0
+        refcount -= 1
+
+        self._refcounts[block_id] = refcount
+
+        return refcount
+
+    def get(self, block_id: BlockId) -> RefCount:
+        assert block_id in self._refcounts
+        return self._refcounts[block_id]
+
+    def as_readonly(self) -> "ReadOnlyRefCounter":
+        return ReadOnlyRefCounter(self)
+
+
+class ReadOnlyRefCounter(RefCounterProtocol):
+    """A read-only view of the RefCounter class.
+
+    The ReadOnlyRefCounter class provides a read-only interface to access the
+    reference counts maintained by a RefCounter instance. It does not allow
+    modifications to the reference counts.
+
+    Args:
+        refcounter (RefCounter): The RefCounter instance to create a read-only
+            view for.
+    """
+
+    def __init__(self, refcounter: RefCounter):
+        self._refcounter = refcounter
+
+    def incr(self, block_id: BlockId) -> RefCount:
+        raise ValueError("Incr not allowed")
+
+    def decr(self, block_id: BlockId) -> RefCount:
+        raise ValueError("Decr not allowed")
+
+    def get(self, block_id: BlockId) -> RefCount:
+        return self._refcounter.get(block_id)
+
+
+class CopyOnWriteTracker:
+    """A class for tracking and managing copy-on-write operations for blocks.
+
+    The CopyOnWriteTracker class maintains a mapping of source block indices to
+        their corresponding copy-on-write destination block indices. It works in
+        conjunction with a RefCounter.
+
+    Args:
+        refcounter (RefCounter): The reference counter used to track block
+            reference counts.
+    """
+
+    def __init__(self, refcounter: RefCounterProtocol):
+        self._copy_on_writes: List[Tuple[BlockId, BlockId]] = []
+        self._refcounter = refcounter
+
+    def is_appendable(self, block: Block) -> bool:
+        """Checks if the block is shared or not. If shared, then it cannot
+        be appended and needs to be duplicated via copy-on-write
+        """
+        block_id = block.block_id
+        if block_id is None:
+            return True
+
+        refcount = self._refcounter.get(block_id)
+        return refcount <= 1
+
+    def record_cow(self, src_block_id: Optional[BlockId],
+                   trg_block_id: Optional[BlockId]) -> None:
+        """Records a copy-on-write operation from source to target block id
+        Args:
+            src_block_id (BlockId): The source block id from which to copy 
+                the data
+            trg_block_id (BlockId): The target block id to which the data
+                is copied
+        """
+        assert src_block_id is not None
+        assert trg_block_id is not None
+        self._copy_on_writes.append((src_block_id, trg_block_id))
+
+    def clear_cows(self) -> List[Tuple[BlockId, BlockId]]:
+        """Clears the copy-on-write tracking information and returns the current
+        state.
+
+        This method returns a list mapping source block indices to
+         destination block indices for the current copy-on-write operations.
+        It then clears the internal tracking information.
+
+        Returns:
+            List[Tuple[BlockId, BlockId]]: A list mapping source
+                block indices to destination block indices for the
+                current copy-on-write operations.
+        """
+        cows = self._copy_on_writes
+        self._copy_on_writes = []
+        return cows
+
+
+class BlockPool:
+    """Used to pre-allocate block objects, in order to avoid excessive python
+    object allocations/deallocations.
+    The pool starts from "pool_size" objects and will increase to more objects
+    if necessary
+
+    Note that multiple block objects may point to the same physical block id,
+    which is why this pool is needed, so that it will be easier to support
+    prefix caching and more complicated sharing of physical blocks.
+    """
+
+    def __init__(self, block_size: int, create_block: Block.Factory,
+                 allocator: BlockAllocator, pool_size: int):
+        self._block_size = block_size
+        self._create_block = create_block
+        self._allocator = allocator
+        self._pool_size = pool_size
+        assert self._pool_size >= 0
+
+        self._free_ids: Deque[int] = deque(range(self._pool_size))
+        self._pool = []
+        for i in range(self._pool_size):
+            self._pool.append(
+                self._create_block(prev_block=None,
+                                   token_ids=[],
+                                   block_size=self._block_size,
+                                   allocator=self._allocator,
+                                   block_id=None,
+                                   extra_hash=None))
+
+    def increase_pool(self):
+        """Doubles the internal pool size
+        """
+        cur_pool_size = self._pool_size
+        new_pool_size = cur_pool_size * 2
+        self._pool_size = new_pool_size
+
+        self._free_ids += deque(range(cur_pool_size, new_pool_size))
+
+        for i in range(cur_pool_size, new_pool_size):
+            self._pool.append(
+                self._create_block(prev_block=None,
+                                   token_ids=[],
+                                   block_size=self._block_size,
+                                   allocator=self._allocator,
+                                   block_id=None,
+                                   extra_hash=None))
+
+    def init_block(self,
+                   prev_block: Optional[Block],
+                   token_ids: List[int],
+                   block_size: int,
+                   physical_block_id: Optional[int],
+                   extra_hash: Optional[int] = None) -> Block:
+        if len(self._free_ids) == 0:
+            self.increase_pool()
+            assert len(self._free_ids) > 0
+
+        pool_id = self._free_ids.popleft()
+
+        block = self._pool[pool_id]
+        block.__init__(  # type: ignore[misc]
+            prev_block=prev_block,
+            token_ids=token_ids,
+            block_size=block_size,
+            allocator=block._allocator,  # type: ignore[attr-defined] 
+            block_id=physical_block_id,
+            extra_hash=extra_hash)
+        block.pool_id = pool_id  # type: ignore[attr-defined]
+        return block
+
+    def free_block(self, block: Block) -> None:
+        self._free_ids.appendleft(block.pool_id)  # type: ignore[attr-defined]
+
+
+class BlockList:
+    """This class is an optimization to allow fast-access to physical 
+    block ids. It maintains a block id list that is updated with the 
+    block list and this avoids the need to reconstruct the block id 
+    list on every iteration of the block manager
+    """
+
+    def __init__(self, blocks: List[Block]):
+        self._blocks: List[Block] = []
+        self._block_ids: List[int] = []
+
+        self.update(blocks)
+
+    def _add_block_id(self, block_id: Optional[BlockId]) -> None:
+        assert block_id is not None
+        self._block_ids.append(block_id)
+
+    def _update_block_id(self, block_index: int,
+                         new_block_id: Optional[BlockId]) -> None:
+        assert new_block_id is not None
+        self._block_ids[block_index] = new_block_id
+
+    def update(self, blocks: List[Block]):
+        self._blocks = blocks
+
+        # Cache block ids for fast query
+        self._block_ids = []
+        for block in self._blocks:
+            self._add_block_id(block.block_id)
+
+    def append_token_ids(self, block_index: int, token_ids: List[int]) -> None:
+        block = self._blocks[block_index]
+        prev_block_id = block.block_id
+
+        block.append_token_ids(token_ids)
+
+        # CoW or promotion may update the internal block_id
+        if prev_block_id != block.block_id:
+            self._update_block_id(block_index, block.block_id)
+
+    def append(self, new_block: Block):
+        self._blocks.append(new_block)
+        self._add_block_id(new_block.block_id)
+
+    def __len__(self) -> int:
+        return len(self._blocks)
+
+    def __getitem__(self, block_index: int) -> Block:
+        return self._blocks[block_index]
+
+    def __setitem__(self, block_index: int, new_block: Block) -> None:
+        self._blocks[block_index] = new_block
+        self._update_block_id(block_index, new_block.block_id)
+
+    def reset(self):
+        self._blocks = []
+        self._block_ids = []
+
+    def list(self) -> List[Block]:
+        return self._blocks
+
+    def ids(self) -> List[int]:
+        return self._block_ids
+
+
+@dataclass
+class CacheMetricData:
+    """A utility dataclass to maintain cache metric.
+    To avoid overflow, we maintain the hit rate in block granularity, so that
+    we can maintain a single hit rate for n_completed_block x block_size,
+    and calculate the real time hit rate by the following:
+    BS = The number of queries per block.
+    nB = The number of completed blocks.
+    HR = hit rate of (nB x BS) queries.
+    Q = current number of queries (< BS).
+    H = current number of hits (< BS).
+    hit rate = ((HR x nB) + (H / Q) x (Q / BS)) / (nB + Q / BS)
+    """
+    num_completed_blocks: int = 0
+    completed_block_cache_hit_rate: float = 0.0
+    num_incompleted_block_queries: int = 0
+    num_incompleted_block_hit: int = 0
+    block_size: int = 1000
+
+    def query(self, hit: bool):
+        self.num_incompleted_block_queries += 1
+        self.num_incompleted_block_hit += 1 if hit else 0
+
+        # When a block is completed, update the cache hit rate
+        # and reset the incomplete numbers.
+        if self.num_incompleted_block_queries == self.block_size:
+            hit_rate = (self.num_incompleted_block_hit /
+                        self.num_incompleted_block_queries)
+            self.completed_block_cache_hit_rate = (
+                self.completed_block_cache_hit_rate * self.num_completed_blocks
+                + hit_rate) / (self.num_completed_blocks + 1)
+            self.num_incompleted_block_queries = 0
+            self.num_incompleted_block_hit = 0
+            self.num_completed_blocks += 1
+
+    def get_hit_rate(self):
+        incomplete_ratio = self.num_incompleted_block_queries / self.block_size
+        total_blocks = self.num_completed_blocks + incomplete_ratio
+        if total_blocks == 0:
+            return 0.0
+
+        completed_block_hit, incompleted_block_hit = 0.0, 0.0
+        if self.num_completed_blocks > 0:
+            completed_block_hit = (self.completed_block_cache_hit_rate *
+                                   self.num_completed_blocks)
+        if self.num_incompleted_block_queries > 0:
+            incompleted_hit_rate = (self.num_incompleted_block_hit /
+                                    self.num_incompleted_block_queries)
+            incompleted_block_hit = (incompleted_hit_rate * incomplete_ratio)
+        return (completed_block_hit + incompleted_block_hit) / total_blocks
+
+
+def get_all_blocks_recursively(last_block: Block) -> List[Block]:
+    """Retrieves all the blocks in a sequence starting from the last block.
+
+    This function recursively traverses the sequence of blocks in reverse order,
+    starting from the given last block, and returns a list of all the blocks in
+    the sequence.
+
+    Args:
+        last_block (Block): The last block in the sequence.
+
+    Returns:
+        List[Block]: A list of all the blocks in the sequence, in the order they
+            appear.
+    """
+
+    def recurse(block: Block, lst: List[Block]) -> None:
+        if block.prev_block is not None:
+            recurse(block.prev_block, lst)
+        lst.append(block)
+
+    all_blocks: List[Block] = []
+    recurse(last_block, all_blocks)
+    return all_blocks
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/lora/ops/xla_ops/lora_ops.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/lora/ops/xla_ops/lora_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..9118f3351ef0a55f0adcf153355f609be96d2404
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/lora/ops/xla_ops/lora_ops.py
@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import jax
+import jax.numpy as jnp
+import torch
+import torch.nn.functional as F
+import torch_xla.core.xla_builder as xb
+from torch.library import impl
+from torch_xla.experimental.custom_kernel import XLA_LIB, jax_import_guard
+
+
+@jax.jit
+def bgmv_jax(inputs, loras, idxs):
+    return jnp.einsum(
+        "td,tX,Xld->tl",
+        inputs,
+        jax.nn.one_hot(idxs, loras.shape[0], dtype=inputs.dtype),
+        loras,
+    )
+
+
+XLA_LIB.define("bgmv(Tensor inputs, Tensor loras, Tensor idxs) -> Tensor")
+
+
+@impl(XLA_LIB, "bgmv", "XLA")
+def bgmv_xla(inputs: torch.Tensor, loras: torch.Tensor, idxs: torch.IntTensor):
+    if len(loras.shape) == 4:
+        loras = loras.squeeze(axis=1)
+
+    jax_import_guard()
+    return xb.call_jax(bgmv_jax, (inputs, loras, idxs))
+
+
+@impl(XLA_LIB, "bgmv", "CompositeExplicitAutograd")
+def bgmv_non_xla(inputs: torch.Tensor, loras: torch.Tensor,
+                 idxs: torch.IntTensor):
+    T, _ = inputs.shape
+    if len(loras.shape) == 4:
+        loras = loras.squeeze(axis=1)
+    _, L, _ = loras.shape
+
+    return torch.empty((T, L), device=inputs.device)
+
+
+def bgmv_expand(
+    inputs: torch.Tensor,
+    lora_b_weights: torch.Tensor,
+    output_tensor: torch.Tensor,
+    lora_indices_tensor: torch.Tensor,
+    add_inputs: bool = True,
+):
+    """
+    Args:
+        inputs (torch.Tensor): Input tensor of shape [num_tokens, hidden_size].
+
+        lora_b_weights (torch.Tensor): LoRA weights of shape
+            [num_loras, lora_rank, hidden_size].
+
+        output_tensor (torch.Tensor): output tensor of shape
+            [num_tokens, hidden_size * num_slices].
+
+        lora_indices_tensor (torch.Tensor): Tensor of shape [num_tokens]
+            indicating which LoRA matrix to use for each token.
+        add_inputs (bool): Whether or not to add the input tensor to the output
+            tensor.
+    """
+
+    outputs = torch.ops.xla.bgmv(inputs, lora_b_weights, lora_indices_tensor)
+
+    limit = output_tensor.shape[0]
+    if outputs.shape[0] == 1 and output_tensor.shape[0] != 1:
+        limit = 1
+
+    if output_tensor.shape[1] > outputs.shape[1]:
+        outputs = F.pad(outputs,
+                        (0, output_tensor.shape[1] - outputs.shape[1], 0, 0))
+
+    if add_inputs:
+        return output_tensor + outputs[:limit, :output_tensor.shape[1]]
+    else:
+        return outputs[:limit, :output_tensor.shape[1]]
+
+
+def bgmv_shrink(
+    inputs: torch.Tensor,
+    lora_b_weights: torch.Tensor,
+    lora_indices_tensor: torch.Tensor,
+    scaling: float = 1.0,
+):
+    """
+    Args:
+        inputs (torch.Tensor): Input tensor of shape [num_tokens, hidden_size].
+        lora_b_weights (torch.Tensor): LoRA weights of shape
+            [num_loras, lora_rank, hidden_size].
+        output_tensor (torch.Tensor): (Unused) output tensor (placeholder).
+        lora_indices_tensor (torch.Tensor): Tensor of shape [num_tokens]
+            indicating which LoRA matrix to use for each token.
+        scaling (float, optional): Scalar multiplier applied to the output.
+    """
+
+    return scaling * torch.ops.xla.bgmv(inputs, lora_b_weights,
+                                        lora_indices_tensor)
+
+
+def bgmv_expand_slice(
+    inputs: torch.Tensor,
+    lora_b_weights: torch.Tensor,
+    output_tensor: torch.Tensor,
+    lora_indices_tensor: torch.Tensor,
+    slice_offset: int,
+    slice_size: int,
+    add_inputs: bool = True,
+):
+    """
+    Args:
+        inputs (torch.Tensor): Input tensor of shape [num_tokens, hidden_size].
+
+        lora_b_weights (torch.Tensor): LoRA weights of shape
+            [num_loras, lora_rank, hidden_size].
+
+        output_tensor (torch.Tensor): output tensor of shape
+            [num_tokens, hidden_size * num_slices].
+
+        lora_indices_tensor (torch.Tensor): Tensor of shape [num_tokens]
+            indicating which LoRA matrix to use for each token.
+        add_inputs (bool): Whether or not to add the input tensor to the output
+            tensor.
+    """
+    outputs = torch.ops.xla.bgmv(inputs, lora_b_weights, lora_indices_tensor)
+
+    outputs = F.pad(
+        outputs,
+        (
+            slice_offset,
+            output_tensor.shape[1] - (slice_offset + slice_size),
+            0,
+            0,
+        ),
+    )
+
+    if add_inputs:
+        return output_tensor + outputs
+    else:
+        return outputs
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diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/fused_moe/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/fused_moe/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3007643d7a28852302341db381c531bc19012f57
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/fused_moe/__init__.py
@@ -0,0 +1,80 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from contextlib import contextmanager
+from typing import Any, Optional
+
+from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.fused_moe.modular_kernel import (
+    FusedMoEActivationFormat, FusedMoEPermuteExpertsUnpermute,
+    FusedMoEPrepareAndFinalize)
+from vllm.triton_utils import HAS_TRITON
+
+_config: Optional[dict[str, Any]] = None
+
+
+@contextmanager
+def override_config(config):
+    global _config
+    old_config = _config
+    _config = config
+    yield
+    _config = old_config
+
+
+def get_config() -> Optional[dict[str, Any]]:
+    return _config
+
+
+__all__ = [
+    "FusedMoE",
+    "FusedMoEConfig",
+    "FusedMoEMethodBase",
+    "FusedMoeWeightScaleSupported",
+    "FusedMoEPermuteExpertsUnpermute",
+    "FusedMoEActivationFormat",
+    "FusedMoEPrepareAndFinalize",
+    "override_config",
+    "get_config",
+]
+
+if HAS_TRITON:
+    # import to register the custom ops
+    import vllm.model_executor.layers.fused_moe.fused_marlin_moe  # noqa
+    import vllm.model_executor.layers.fused_moe.fused_moe  # noqa
+    from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
+        BatchedDeepGemmExperts)
+    from vllm.model_executor.layers.fused_moe.batched_triton_or_deep_gemm_moe import (  # noqa: E501
+        BatchedTritonOrDeepGemmExperts)
+    from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+        CutlassBatchedExpertsFp8, CutlassExpertsFp8, cutlass_moe_fp4,
+        cutlass_moe_fp8)
+    from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
+        DeepGemmExperts)
+    from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
+        BatchedTritonExperts)
+    from vllm.model_executor.layers.fused_moe.fused_moe import (
+        TritonExperts, fused_experts, fused_moe, fused_topk,
+        get_config_file_name, grouped_topk)
+    from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
+        TritonOrDeepGemmExperts)
+
+    __all__ += [
+        "fused_moe",
+        "fused_topk",
+        "fused_experts",
+        "get_config_file_name",
+        "grouped_topk",
+        "cutlass_moe_fp8",
+        "cutlass_moe_fp4",
+        "CutlassExpertsFp8",
+        "CutlassBatchedExpertsFp8",
+        "TritonExperts",
+        "BatchedTritonExperts",
+        "DeepGemmExperts",
+        "BatchedDeepGemmExperts",
+        "TritonOrDeepGemmExperts",
+        "BatchedTritonOrDeepGemmExperts",
+    ]
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..061b02172c4465155902e29a666abe9d3912f5f3
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Optional
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.distributed import get_dp_group
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+from vllm.model_executor.layers.fused_moe.utils import (
+    moe_kernel_quantize_input)
+from vllm.utils.flashinfer import nvfp4_block_scale_interleave
+
+
+def get_local_sizes():
+    return get_forward_context().dp_metadata.get_chunk_sizes_across_dp_rank()
+
+
+class FlashInferCutlassMoEPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
+
+    def __init__(
+        self,
+        use_dp: bool,
+        a1_gscale: Optional[torch.Tensor],
+        num_dispatchers: int = 1,
+    ):
+        super().__init__()
+        self.num_dispatchers_ = num_dispatchers
+        self.use_dp = use_dp
+        self.a1_gscale = a1_gscale
+        self.local_tokens = None
+
+    @property
+    def activation_format(self) -> mk.FusedMoEActivationFormat:
+        return mk.FusedMoEActivationFormat.Standard
+
+    def max_num_tokens_per_rank(self) -> Optional[int]:
+        return None
+
+    def topk_indices_dtype(self) -> Optional[torch.dtype]:
+        return None
+
+    def num_dispatchers(self) -> int:
+        return self.num_dispatchers_
+
+    def prepare(
+        self,
+        a1: torch.Tensor,
+        a1_scale: Optional[torch.Tensor],  # Not used
+        a2_scale: Optional[torch.Tensor],  # Not used
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        num_experts: int,
+        expert_map: Optional[torch.Tensor],
+        apply_router_weight_on_input: bool,
+        # TODO(bnell): use quant_config + scales instead of ctor args
+        quant_config: FusedMoEQuantConfig,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor],
+               Optional[mk.ExpertTokensMetadata], Optional[torch.Tensor],
+               Optional[torch.Tensor]]:
+
+        if apply_router_weight_on_input:
+            topk = topk_ids.size(1)
+            # TODO: this only works for topK=1, will need to update for topK>1
+            assert topk == 1, \
+                "apply_router_weight_on_input is only implemented for topk=1"
+            a1.mul_(topk_weights.to(a1.dtype))
+
+        a1q, a1q_scale = moe_kernel_quantize_input(
+            a1,
+            self.a1_gscale,
+            quant_config.quant_dtype,
+            quant_config.per_act_token_quant,
+            quant_config.block_shape,
+            # Swizzling after communication
+            is_fp4_scale_swizzled=not self.use_dp,
+        )
+        if self.use_dp:
+            topk_weights, topk_ids, a1q, a1q_scale = \
+                get_dp_group().all_gatherv(
+                    [topk_weights, topk_ids, a1q, a1q_scale],
+                    dim=0,
+                    sizes=get_local_sizes(),
+                )
+            a1_m, a1_n = a1q.shape
+            a1q_scale = nvfp4_block_scale_interleave(a1q_scale)
+
+        return a1q, a1q_scale, None, topk_ids, topk_weights
+
+    def finalize(self, output: torch.Tensor, fused_expert_output: torch.Tensor,
+                 topk_weights: torch.Tensor, topk_ids: torch.Tensor,
+                 apply_router_weight_on_input: bool,
+                 weight_and_reduce_impl: mk.TopKWeightAndReduce) -> None:
+
+        if self.use_dp:
+            fused_expert_output = get_dp_group().reduce_scatterv(
+                fused_expert_output, dim=0, sizes=get_local_sizes())
+        output.copy_(fused_expert_output)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/lightning_attn.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/lightning_attn.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ffc700ca5cde83e2a365ac80c52aa28eb818365
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/lightning_attn.py
@@ -0,0 +1,652 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+from einops import rearrange
+
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def _fwd_diag_kernel(Q, K, V, Out, S, b: tl.constexpr, h: tl.constexpr, n,
+                     d: tl.constexpr, e: tl.constexpr, BLOCK: tl.constexpr,
+                     NUM_BLOCK, CBLOCK: tl.constexpr):
+    # This kernel computes the diagonal blocks of the attention matrix
+    # Each diagonal block represents attention
+    # where queries attend to keys in the same block
+    off = tl.program_id(0)
+    off_bh = off // NUM_BLOCK  # batch-head index
+    off_block = off % NUM_BLOCK  # block index within the sequence
+    off_cblock = tl.program_id(1)  # sub-block index within a block
+
+    off_h = off_bh % h  # head index
+
+    # Calculate base offsets for the current batch and head
+    qk_offset = off_bh * n * d
+    v_offset = off_bh * n * e
+    o_offset = off_bh * n * e
+
+    # Calculate offsets for the current block
+    block_offset = off_block * BLOCK
+    qk_block_offset = block_offset * d
+    v_block_offset = block_offset * e
+    o_block_offset = block_offset * e
+
+    # Calculate offsets for the current sub-block
+    cblock_offset = off_cblock * CBLOCK
+    q_cblock_offset = cblock_offset * d
+    o_cblock_offset = cblock_offset * e
+
+    # Calculate pointers to the query, key, value, and output tensors
+    Q_block_ptr = (Q + qk_offset + qk_block_offset + q_cblock_offset +
+                   tl.arange(0, CBLOCK)[:, None] * d +
+                   tl.arange(0, d)[None, :])
+    K_trans_block_ptr = (K + qk_offset + qk_block_offset +
+                         tl.arange(0, CBLOCK)[None, :] * d +
+                         tl.arange(0, d)[:, None])
+    V_block_ptr = (V + v_offset + v_block_offset +
+                   tl.arange(0, CBLOCK)[:, None] * e +
+                   tl.arange(0, e)[None, :])
+    O_block_ptr = (Out + o_offset + o_block_offset + o_cblock_offset +
+                   tl.arange(0, CBLOCK)[:, None] * e +
+                   tl.arange(0, e)[None, :])
+
+    # Load the decay rate for the current head
+    S_block_ptr = S + off_h
+    s = tl.load(S_block_ptr)
+
+    i = off_cblock
+    q_index = tl.arange(0, CBLOCK) + i * CBLOCK
+
+    # Load query values
+    q = tl.load(Q_block_ptr,
+                mask=block_offset + q_index[:, None] < n,
+                other=0.0).to(tl.float32)
+
+    # Initialize output accumulator
+    qkv = tl.zeros([CBLOCK, e], dtype=tl.float32)
+
+    # Process all sub-blocks up to and
+    # including the current one (causal attention)
+    for j in range(i + 1):
+        kv_index = tl.arange(0, CBLOCK) + j * CBLOCK
+        diff = q_index[:, None] - kv_index[None, :]
+        s_index = s * diff
+        # Apply causal mask: only attend to positions before the current one
+        s_index = tl.where(diff >= 0, -s_index, float("-inf"))
+        decay = tl.exp(s_index)
+
+        # Load key and value
+        k_trans = tl.load(
+            K_trans_block_ptr,
+            mask=block_offset + kv_index[None, :] < n,
+            other=0.0,
+        ).to(tl.float32)
+        v = tl.load(
+            V_block_ptr,
+            mask=block_offset + kv_index[:, None] < n,
+            other=0.0,
+        ).to(tl.float32)
+
+        # Compute attention scores and apply decay
+        qk = tl.dot(q, k_trans) * decay
+
+        # Compute weighted values and accumulate
+        qkv += tl.dot(qk, v)
+
+        # Move to the next sub-block
+        K_trans_block_ptr += CBLOCK * d
+        V_block_ptr += CBLOCK * e
+
+    # Store the result
+    tl.store(
+        O_block_ptr,
+        qkv.to(O_block_ptr.dtype.element_ty),
+        mask=block_offset + q_index[:, None] < n,
+    )
+
+
+@triton.jit
+def _fwd_kv_parallel(
+    K,
+    V,
+    K_decay,
+    KV,
+    b: tl.constexpr,
+    h: tl.constexpr,
+    n,
+    d: tl.constexpr,
+    e: tl.constexpr,
+    BLOCK: tl.constexpr,
+    NUM_BLOCK,
+    D_FBLOCK: tl.constexpr,
+    E_FBLOCK: tl.constexpr,
+    NUM_FBLOCK: tl.constexpr,
+    CBLOCK: tl.constexpr,
+    NUM_CBLOCK: tl.constexpr,
+):
+    # This kernel computes the key-value outer
+    # products for each block in parallel
+    off_bh = tl.program_id(0)  # batch-head index
+    off_block = tl.program_id(1)  # block index
+
+    off_h = off_bh % h  # head index
+
+    block_offset = off_block * BLOCK
+
+    # Calculate offsets for the current block
+    k_block_offset = block_offset * d
+    v_block_offset = block_offset * e
+    kv_block_offset = off_block * d * e
+
+    # Calculate base offsets for the current batch and head
+    k_offset = off_bh * n * d
+    v_offset = off_bh * n * e
+    kv_offset = off_bh * NUM_BLOCK * d * e
+
+    # Calculate pointers to the key, value, and key-value tensors
+    K_trans_block_ptr = (K + k_offset + k_block_offset +
+                         tl.arange(0, CBLOCK)[None, :] * d +
+                         tl.arange(0, D_FBLOCK)[:, None])
+    V_block_ptr = (V + v_offset + v_block_offset +
+                   tl.arange(0, CBLOCK)[:, None] * e +
+                   tl.arange(0, E_FBLOCK)[None, :])
+    KV_block_ptr = (KV + kv_offset + kv_block_offset +
+                    tl.arange(0, D_FBLOCK)[:, None] * e +
+                    tl.arange(0, E_FBLOCK)[None, :])
+
+    # Load the decay factors for the current head and block
+    k_decay_ptr = (K_decay + off_h * BLOCK + tl.arange(0, CBLOCK)[None, :])
+
+    kv_index = tl.arange(0, CBLOCK)
+
+    # Initialize the key-value outer product accumulator
+    kv = tl.zeros([D_FBLOCK, E_FBLOCK], dtype=tl.float32)
+
+    # Handle the last block which might be smaller than BLOCK
+    if off_block == NUM_BLOCK - 1:
+        split_n = n - (NUM_BLOCK - 1) * BLOCK
+    else:
+        split_n = BLOCK
+    left_shift = tl.cdiv(split_n, CBLOCK) * CBLOCK - split_n
+    num_blocks = min(tl.cdiv(split_n, CBLOCK), NUM_CBLOCK)
+    k_decay_ptr += (NUM_CBLOCK - num_blocks) * CBLOCK
+
+    # Process all sub-blocks in the current block
+    for j in range(num_blocks):
+        left_bound = (1 - j) * left_shift
+        # Load key and value, handling boundary conditions
+        k_trans = tl.load(K_trans_block_ptr - left_shift * d,
+                          mask=kv_index[None, :] >= left_bound,
+                          other=0.0)
+        v = tl.load(V_block_ptr - left_shift * e,
+                    mask=kv_index[:, None] >= left_bound,
+                    other=0.0)
+
+        # Load decay factor and compute weighted key-value outer product
+        k_decay = tl.load(k_decay_ptr)
+        kv += tl.dot(k_trans * k_decay, v)
+
+        # Move to the next sub-block
+        K_trans_block_ptr += CBLOCK * d
+        V_block_ptr += CBLOCK * e
+        k_decay_ptr += CBLOCK
+
+    # Store the result
+    tl.store(KV_block_ptr, kv.to(KV_block_ptr.dtype.element_ty))
+
+
+@triton.jit
+def _fwd_kv_reduce(S, KV, KV_HISTORY, b: tl.constexpr, h: tl.constexpr, n,
+                   d: tl.constexpr, e: tl.constexpr, BLOCK: tl.constexpr,
+                   NUM_BLOCK, D_FBLOCK: tl.constexpr, E_FBLOCK: tl.constexpr):
+    # This kernel reduces the key-value outer products
+    # across blocks and updates the KV history
+    off_bh = tl.program_id(0)  # batch-head index
+    off_h = off_bh % h  # head index
+
+    kv_offset = off_bh * NUM_BLOCK * d * e
+
+    # Calculate pointer to the key-value tensor
+    KV_block_ptr = (KV + kv_offset + tl.arange(0, D_FBLOCK)[:, None] * e +
+                    tl.arange(0, E_FBLOCK)[None, :])
+
+    # Load the decay rate for the current head
+    s_ptrs = S + off_h
+    s = tl.load(s_ptrs)
+
+    # Calculate pointer to the key-value history tensor
+    kv_history_offset = off_bh * d * e
+    KV_HISTORY_block_ptr = (KV_HISTORY + kv_history_offset +
+                            tl.arange(0, D_FBLOCK)[:, None] * e +
+                            tl.arange(0, E_FBLOCK)[None, :])
+
+    # Load the previous key-value history
+    kv_pre = tl.load(KV_HISTORY_block_ptr).to(tl.float32)
+
+    # Process all blocks in reverse order to compute the prefix sum
+    for i in range(NUM_BLOCK):
+        block_size = min(n - i * BLOCK, BLOCK)
+        # Compute decay factor for the current block
+        block_decay = tl.exp(-s.to(tl.float32) * block_size)
+
+        # Load the current key-value outer product
+        kv_cur = tl.load(KV_block_ptr).to(tl.float32)
+        # Store the previous key-value history to the current block
+        tl.store(KV_block_ptr, kv_pre.to(KV_block_ptr.dtype.element_ty))
+
+        # Update the key-value history with the current block
+        kv_pre = block_decay * kv_pre + kv_cur
+        KV_block_ptr += d * e
+
+    # Store the updated key-value history
+    tl.store(KV_HISTORY_block_ptr, kv_pre)
+
+
+@triton.jit
+def _fwd_none_diag_kernel(
+    Q,
+    Out,
+    S,
+    KV,
+    b: tl.constexpr,
+    h: tl.constexpr,
+    n,
+    d: tl.constexpr,
+    e: tl.constexpr,
+    BLOCK: tl.constexpr,
+    NUM_BLOCK,
+    E_FBLOCK: tl.constexpr,
+    CBLOCK: tl.constexpr,
+    NUM_CBLOCK: tl.constexpr,
+):
+    # This kernel computes the non-diagonal blocks of the attention matrix
+    # Each non-diagonal block represents attention
+    # where queries attend to keys in different blocks
+    off_bh = tl.program_id(0)  # batch-head index
+    off_h = off_bh % h  # head index
+
+    off_nc = tl.program_id(1)
+    off_n = off_nc // NUM_CBLOCK  # block index
+    off_c = off_nc % NUM_CBLOCK  # sub-block index
+    off_e = tl.program_id(2)  # output feature block index
+
+    n_offset = off_n * BLOCK
+    c_offset = off_c * CBLOCK
+    e_offset = off_e * E_FBLOCK
+    block_offset = n_offset + c_offset
+
+    # Calculate offsets for the current batch, head, and block
+    q_offset = off_bh * n * d + (n_offset + c_offset) * d
+    o_offset = off_bh * n * e + (n_offset + c_offset) * e + e_offset
+    kv_offset = off_bh * NUM_BLOCK * d * e + off_n * d * e + e_offset
+
+    # Calculate pointers to the query, output, and key-value tensors
+    Q_block_ptr = (Q + q_offset + tl.arange(0, CBLOCK)[:, None] * d +
+                   tl.arange(0, d)[None, :])
+    O_block_ptr = (Out + o_offset + tl.arange(0, CBLOCK)[:, None] * e +
+                   tl.arange(0, E_FBLOCK)[None, :])
+    KV_block_ptr = (KV + kv_offset + tl.arange(0, d)[:, None] * e +
+                    tl.arange(0, E_FBLOCK)[None, :])
+
+    # Load the decay rate for the current head
+    S_block_ptr = S + off_h
+    s = tl.load(S_block_ptr)
+
+    c_array = tl.arange(0, CBLOCK)
+
+    # Load the key-value outer product for the current block
+    kv = tl.load(KV_block_ptr).to(tl.float32)
+    q_index = block_offset + tl.arange(0, CBLOCK)
+
+    # Load query values
+    q = tl.load(Q_block_ptr, mask=q_index[:, None] < n,
+                other=0.).to(tl.float32)
+
+    # Compute decay factors for the current sub-block
+    q_decay = tl.exp(-s.to(tl.float32) * (off_c * CBLOCK + c_array[:, None]))
+
+    # Compute non-diagonal attention output
+    qkv_none_diag = tl.dot(q, kv) * q_decay
+
+    # Load diagonal attention output (computed by _fwd_diag_kernel)
+    qkv_diag = tl.load(O_block_ptr, mask=q_index[:, None] < n,
+                       other=0.).to(tl.float32)
+
+    # Combine diagonal and non-diagonal attention outputs
+    qkv = qkv_diag + qkv_none_diag
+
+    # Store the result
+    tl.store(O_block_ptr,
+             qkv.to(O_block_ptr.dtype.element_ty),
+             mask=q_index[:, None] < n)
+
+
+class _attention(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, q, k, v, s, kv_history):
+        # Forward pass of the lightning attention algorithm
+        q = q.contiguous()
+        k = k.contiguous()
+        v = v.contiguous()
+        s = s.contiguous()
+
+        # Check CUDA compute capability
+        capability = torch.cuda.get_device_capability()
+        if capability[0] < 8:
+            raise RuntimeError("Flash attention currently only supported",
+                               "for compute capability >= 80")
+
+        # Get input dimensions
+        b, h, n, d = q.shape
+        e = v.shape[-1]
+
+        # Initialize output tensor
+        o = torch.empty((b, h, n, e), dtype=q.dtype, device=q.device)
+
+        # Set block sizes
+        BLOCK = 256
+        NUM_BLOCK = triton.cdiv(n, BLOCK)
+
+        CBLOCK = 32
+        NUM_CBLOCK = BLOCK // CBLOCK
+        assert BLOCK % CBLOCK == 0, "BLOCK must be a multiple of CBLOCK"
+
+        # Compute decay factors for keys
+        array = torch.arange(0, BLOCK, device=q.device) + 1
+        k_decay = torch.exp(-s * (BLOCK - array.reshape(1, -1)))
+
+        # Step 1: Compute diagonal blocks of attention
+        grid = (b * h * NUM_BLOCK, NUM_CBLOCK)
+        _fwd_diag_kernel[grid](q,
+                               k,
+                               v,
+                               o,
+                               s,
+                               b,
+                               h,
+                               n,
+                               d,
+                               e,
+                               BLOCK=BLOCK,
+                               NUM_BLOCK=NUM_BLOCK,
+                               CBLOCK=CBLOCK)
+
+        # Set feature block sizes
+        NUM_FBLOCK = 1
+        D_FBLOCK = d // NUM_FBLOCK
+        assert d % NUM_FBLOCK == 0
+        E_FBLOCK = e // NUM_FBLOCK
+        assert e % NUM_FBLOCK == 0
+
+        CBLOCK = 64
+        NUM_CBLOCK = BLOCK // CBLOCK
+        assert BLOCK % CBLOCK == 0, "BLOCK must be a multiple of CBLOCK"
+
+        # Step 2: Compute key-value outer products for each block in parallel
+        kv = torch.empty((b, h, NUM_BLOCK, d, e),
+                         dtype=torch.float32,
+                         device=q.device)
+        grid = (b * h, NUM_BLOCK)
+        _fwd_kv_parallel[grid](
+            k,
+            v,
+            k_decay,
+            kv,
+            b,
+            h,
+            n,
+            d,
+            e,
+            BLOCK=BLOCK,
+            NUM_BLOCK=NUM_BLOCK,
+            D_FBLOCK=D_FBLOCK,
+            E_FBLOCK=E_FBLOCK,
+            NUM_FBLOCK=NUM_FBLOCK,
+            CBLOCK=CBLOCK,
+            NUM_CBLOCK=NUM_CBLOCK,
+        )
+
+        # Step 3: Reduce key-value outer products
+        # across blocks and update KV history
+        grid = (b * h, NUM_FBLOCK)
+        _fwd_kv_reduce[grid](s,
+                             kv,
+                             kv_history,
+                             b,
+                             h,
+                             n,
+                             d,
+                             e,
+                             BLOCK=BLOCK,
+                             NUM_BLOCK=NUM_BLOCK,
+                             D_FBLOCK=D_FBLOCK,
+                             E_FBLOCK=E_FBLOCK)
+
+        # Step 4: Compute non-diagonal blocks of attention
+        grid = (b * h, NUM_BLOCK * NUM_CBLOCK)
+        _fwd_none_diag_kernel[grid](
+            q,
+            o,
+            s,
+            kv,
+            b,
+            h,
+            n,
+            d,
+            e,
+            BLOCK=BLOCK,
+            NUM_BLOCK=NUM_BLOCK,
+            E_FBLOCK=E_FBLOCK,
+            CBLOCK=CBLOCK,
+            NUM_CBLOCK=NUM_CBLOCK,
+        )
+
+        # Save tensors for backward pass
+        ctx.save_for_backward(q, k, v, s, kv)
+        ctx.BLOCK = BLOCK
+
+        return o, torch.cat([kv, kv_history.unsqueeze(2)], dim=2)
+
+
+# Apply the lightning attention function
+lightning_attention_ = _attention.apply
+
+
+def lightning_attention(q, k, v, ed, block_size=256, kv_history=None):
+    """
+    Apply lightning attention algorithm 
+    to compute attention efficiently.
+    
+    Args:
+        q: Query tensor of shape [batch, heads, seq_len, dim]
+        k: Key tensor of shape [batch, heads, seq_len, dim]
+        v: Value tensor of shape [batch, heads, seq_len, dim_v]
+        ed: Decay rate tensor of shape [heads]
+        block_size: Size of blocks for block-sparse attention
+        kv_history: Optional key-value history from previous computations
+        
+    Returns:
+        output: Attention output
+        kv: Updated key-value history
+    """
+    d = q.shape[-1]
+    e = v.shape[-1]
+
+    if ed.dim() == 1:
+        ed = ed.view(1, -1, 1, 1)
+
+    # Split the computation into chunks for better parallelism
+    m = 128 if d >= 128 else 64
+    assert d % m == 0, f"Dimension d ({d}) must be divisible by m ({m})"
+    arr = [m * i for i in range(d // m + 1)]
+    if arr[-1] != d:
+        arr.append(d)
+    n = len(arr)
+    output = 0
+
+    # Initialize or clone key-value history
+    if kv_history is None:
+        kv_history = torch.zeros((q.shape[0], q.shape[1], d, e),
+                                 dtype=torch.float32,
+                                 device=q.device)
+    else:
+        kv_history = kv_history.clone().contiguous()
+
+    # Process each chunk and accumulate results
+    for i in range(n - 1):
+        s = arr[i]
+        e = arr[i + 1]
+        q1 = q[..., s:e]
+        k1 = k[..., s:e]
+        o, kv = lightning_attention_(q1, k1, v, ed, kv_history)
+        output = output + o
+    return output, kv
+
+
+@triton.jit
+def _linear_attn_decode_kernel(
+    q_ptr,
+    k_ptr,
+    v_ptr,
+    kv_cache_ptr,
+    slope_rate,
+    slot_idx,
+    output_ptr,
+    D: tl.constexpr,
+    qkv_b_stride,
+    qkv_h_stride,
+    cache_b_stride,
+    cache_h_stride,
+    cache_d0_stride,
+    cache_d1_stride,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """
+    Kernel for linear attention decoding with KV cache.
+    
+    This kernel computes attention for a single token using the KV cache.
+    """
+    pid_b = tl.program_id(0)  # batch index
+    pid_h = tl.program_id(1)  # head index
+    pid_d = tl.program_id(2)  # dimension block index
+
+    # Load slot index for the current batch
+    slot_id = tl.load(slot_idx + pid_b).to(tl.int64)
+
+    # Skip if slot_id is -1 (padding)
+    if slot_id == -1:
+        return
+
+    batch_id = pid_b
+    head_id = pid_h
+
+    # Load decay rate for the current head
+    ratio = tl.load(slope_rate + pid_h)
+
+    # Calculate offsets for dimensions
+    qk_d_offsets = tl.arange(0, D)
+    v_d_offsets = tl.arange(0, BLOCK_SIZE) + pid_d * BLOCK_SIZE
+    cache_d_offsets = qk_d_offsets[:, None] * cache_d0_stride + v_d_offsets[
+        None, :] * cache_d1_stride
+
+    # Calculate offsets for the current batch and head
+    q_offset = batch_id * qkv_b_stride + head_id * qkv_h_stride
+    k_offset = batch_id * qkv_b_stride + head_id * qkv_h_stride
+    v_offset = batch_id * qkv_b_stride + head_id * qkv_h_stride
+
+    cache_offset = slot_id * cache_b_stride + head_id * cache_h_stride
+
+    # Create masks for loading tensors
+    qk_mask = qk_d_offsets < D
+    v_mask = v_d_offsets < D
+
+    # Load query, key, and value tensors
+    q = tl.load(q_ptr + q_offset + qk_d_offsets, mask=qk_mask, other=0.0)
+    k = tl.load(k_ptr + k_offset + qk_d_offsets, mask=qk_mask, other=0.0)
+    v = tl.load(v_ptr + v_offset + v_d_offsets, mask=v_mask, other=0.0)
+
+    # Compute key-value outer product
+    kv_outer = k[:, None] * v[None, :]
+    kv_mask = qk_mask[:, None] & v_mask[None, :]
+
+    # Apply decay to previous KV cache
+    ratio = tl.exp(-ratio)
+    kv_ptr = kv_cache_ptr + cache_offset + cache_d_offsets
+    kv_cache_old = tl.load(kv_ptr, mask=kv_mask, other=0.0)
+    kv_outer = kv_outer + ratio * kv_cache_old
+
+    # Compute attention output
+    output = q[:, None].to(tl.float32) * kv_outer
+    output = tl.sum(output, axis=0)
+
+    # Update KV cache and store output
+    tl.store(kv_ptr, kv_outer, mask=kv_mask)
+    tl.store(output_ptr + q_offset + v_d_offsets, output, mask=v_mask)
+
+
+def linear_decode_forward_triton(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    kv_caches: torch.Tensor,
+    slope_rate: torch.Tensor,
+    slot_idx: torch.Tensor,
+    BLOCK_SIZE: int = 32,
+) -> torch.Tensor:
+    """
+    Perform linear attention decoding using Triton kernels.
+    
+    Args:
+        q: Query tensor of shape [B, H, 1, D]
+        k: Key tensor of shape [B, H, 1, D]
+        v: Value tensor of shape [B, H, 1, D]
+        kv_caches: Key-value cache tensor
+        slope_rate: Decay rate tensor
+        slot_idx: Slot indices for batches
+        BLOCK_SIZE: Size of blocks for processing
+        
+    Returns:
+        output: Attention output tensor
+    """
+    B, H, _, D = q.shape
+    assert k.shape == (B, H, 1, D)
+    assert v.shape == (B, H, 1, D)
+
+    # Initialize output tensor
+    output = torch.empty_like(q)
+
+    # Set grid dimensions for the kernel
+    grid = (B, H, D // BLOCK_SIZE)
+
+    # Calculate strides for tensors
+    qkv_b_stride = q.stride(0)
+    qkv_h_stride = q.stride(1)
+
+    cache_b_stride = kv_caches.stride(0)
+    cache_h_stride = kv_caches.stride(1)
+    cache_d0_stride = kv_caches.stride(2)
+    cache_d1_stride = kv_caches.stride(3)
+
+    # Launch the kernel
+    _linear_attn_decode_kernel[grid](
+        q,
+        k,
+        v,
+        kv_caches,
+        slope_rate,
+        slot_idx,
+        output,
+        D,
+        qkv_b_stride,
+        qkv_h_stride,
+        cache_b_stride,
+        cache_h_stride,
+        cache_d0_stride,
+        cache_d1_stride,
+        BLOCK_SIZE=BLOCK_SIZE,
+    )
+
+    # Reshape output and return
+    output = rearrange(output, "b h n d -> b n (h d)")
+    return output.squeeze(1).contiguous()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..48a347a8f561156b263ba3079b53baaaa13847e2
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/layers/utils.py
@@ -0,0 +1,161 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Utility methods for model layers."""
+from typing import Callable, Optional
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm import envs
+from vllm.platforms import current_platform
+from vllm.utils import direct_register_custom_op
+
+
+def shuffle_weight(w: torch.Tensor) -> torch.Tensor:
+    # Shuffle weight along the last dimension so that
+    # we folded the weights to adjance location
+    # Example:
+    # input:
+    #       [[1, 2, 3, 4, 5, 6],
+    #        [7, 8, 9, 10, 11, 12]]
+    # output:
+    #       [[1, 4, 2, 5, 3, 6],
+    #        [7, 10, 8, 11, 9, 12]]
+    # This will be used together with triton swiglu kernel
+    shape = w.shape
+    N = shape[-1]
+    first = w[..., :N // 2]
+    second = w[..., N // 2:]
+
+    stacked = torch.stack((first, second), dim=-1)
+    w_shuffled = stacked.reshape(shape)
+    return w_shuffled
+
+
+def get_token_bin_counts_and_mask(
+    tokens: torch.Tensor,
+    vocab_size: int,
+    num_seqs: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    # Compute the bin counts for the tokens.
+    # vocab_size + 1 for padding.
+    bin_counts = torch.zeros((num_seqs, vocab_size + 1),
+                             dtype=torch.long,
+                             device=tokens.device)
+    bin_counts.scatter_add_(1, tokens, torch.ones_like(tokens))
+    bin_counts = bin_counts[:, :vocab_size]
+    mask = bin_counts > 0
+
+    return bin_counts, mask
+
+
+def apply_penalties(logits: torch.Tensor, prompt_tokens_tensor: torch.Tensor,
+                    output_tokens_tensor: torch.Tensor,
+                    presence_penalties: torch.Tensor,
+                    frequency_penalties: torch.Tensor,
+                    repetition_penalties: torch.Tensor) -> torch.Tensor:
+    """
+    Applies penalties in place to the logits tensor
+    logits : The input logits tensor of shape [num_seqs, vocab_size]
+    prompt_tokens_tensor: A tensor containing the prompt tokens. The prompts 
+        are padded to the maximum prompt length within the batch using 
+        `vocab_size` as the padding value. The value `vocab_size` is used 
+        for padding because it does not correspond to any valid token ID 
+        in the vocabulary.
+    output_tokens_tensor: The output tokens tensor.
+    presence_penalties: The presence penalties of shape (num_seqs, )
+    frequency_penalties: The frequency penalties of shape (num_seqs, )
+    repetition_penalties: The repetition penalties of shape (num_seqs, )
+    """
+    num_seqs, vocab_size = logits.shape
+    _, prompt_mask = get_token_bin_counts_and_mask(prompt_tokens_tensor,
+                                                   vocab_size, num_seqs)
+    output_bin_counts, output_mask = get_token_bin_counts_and_mask(
+        output_tokens_tensor, vocab_size, num_seqs)
+
+    # Apply repetition penalties as a custom op
+    from vllm._custom_ops import apply_repetition_penalties
+    apply_repetition_penalties(logits, prompt_mask, output_mask,
+                               repetition_penalties)
+
+    # We follow the definition in OpenAI API.
+    # Refer to https://platform.openai.com/docs/api-reference/parameter-details
+    logits -= frequency_penalties.unsqueeze(dim=1) * output_bin_counts
+    logits -= presence_penalties.unsqueeze(dim=1) * output_mask
+    return logits
+
+
+def default_unquantized_gemm(layer: torch.nn.Module,
+                             x: torch.Tensor,
+                             weight: torch.Tensor,
+                             bias: Optional[torch.Tensor] = None):
+    return torch.nn.functional.linear(x, weight, bias)
+
+
+def rocm_unquantized_gemm_impl(
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    from vllm.platforms.rocm import on_gfx9
+    k = weight.shape[1]
+    use_skinny = (envs.VLLM_ROCM_USE_SKINNY_GEMM and on_gfx9() and \
+                    x.dtype in [torch.float16, torch.bfloat16] \
+                    and k % 8 == 0 and bias is None)
+
+    if use_skinny is not True:
+        return torch.nn.functional.linear(x, weight, bias)
+
+    x_view = x.view(-1, x.size(-1))
+    n = x_view.shape[0]
+    m = weight.shape[0]
+    cu_count = current_platform.get_cu_count()
+
+    if m > 8 and 0 < n <= 4:
+        out = ops.wvSplitK(weight, x_view, cu_count)
+        return out.view(*x.shape[:-1], weight.shape[0])
+    elif m % 4 == 0 and n == 1 and k <= 8192:
+        out = ops.LLMM1(weight, x_view, 4)
+        return out.view(*x.shape[:-1], weight.shape[0])
+    return torch.nn.functional.linear(x, weight, bias)
+
+
+def rocm_unquantized_gemm_impl_fake(
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    return x.new_empty((*x.shape[:-1], weight.shape[0]))
+
+
+def rocm_unquantized_gemm(layer: torch.nn.Module,
+                          x: torch.Tensor,
+                          weight: torch.Tensor,
+                          bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    return torch.ops.vllm.rocm_unquantized_gemm_impl(x, weight, bias)
+
+
+direct_register_custom_op(
+    op_name="rocm_unquantized_gemm_impl",
+    op_func=rocm_unquantized_gemm_impl,
+    mutates_args=[],
+    fake_impl=rocm_unquantized_gemm_impl_fake,
+    dispatch_key=current_platform.dispatch_key,
+)
+
+
+def cpu_unquantized_gemm(layer: torch.nn.Module,
+                         x: torch.Tensor,
+                         weight: torch.Tensor,
+                         bias: Optional[torch.Tensor] = None):
+    if getattr(layer, "use_cpu_sgl", False):
+        return torch.ops._C.weight_packed_linear(x, weight, bias, True)
+    else:
+        return torch.nn.functional.linear(x, weight, bias)
+
+
+def dispatch_unquantized_gemm() -> Callable[..., torch.Tensor]:
+    if current_platform.is_rocm():
+        return rocm_unquantized_gemm
+    elif current_platform.is_cpu():
+        return cpu_unquantized_gemm
+    else:
+        return default_unquantized_gemm
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/model_loader/dummy_loader.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/model_loader/dummy_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4a7da5744e04bfe1e67d215d739f347f27bbbd6
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/model_loader/dummy_loader.py
@@ -0,0 +1,27 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch.nn as nn
+
+from vllm.config import LoadConfig, ModelConfig
+from vllm.model_executor.model_loader.base_loader import BaseModelLoader
+from vllm.model_executor.model_loader.weight_utils import (
+    initialize_dummy_weights)
+
+
+class DummyModelLoader(BaseModelLoader):
+    """Model loader that will set model weights to random values."""
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+        if load_config.model_loader_extra_config:
+            raise ValueError(f"Model loader extra config is not supported for "
+                             f"load format {load_config.load_format}")
+
+    def download_model(self, model_config: ModelConfig) -> None:
+        pass  # Nothing to download
+
+    def load_weights(self, model: nn.Module,
+                     model_config: ModelConfig) -> None:
+        # NOTE(woosuk): For accurate performance evaluation, we assign
+        # random values to the weights.
+        initialize_dummy_weights(model)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/model_loader/neuronx_distributed.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/model_loader/neuronx_distributed.py
new file mode 100644
index 0000000000000000000000000000000000000000..f450961c64ff4931b2ba193fbc713b821ed9815d
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/model_loader/neuronx_distributed.py
@@ -0,0 +1,685 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Utilities for selecting and loading Neuron models in
+neuronx-distributed-inference framework."""
+# Disabling yapf because yapf and isort have conflicts for the below imports
+# yapf: disable
+import copy
+import hashlib
+import importlib
+import multiprocessing
+import os
+import shutil
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from neuronx_distributed_inference.models.config import (
+    FusedSpecNeuronConfig, OnDeviceSamplingConfig)
+from neuronx_distributed_inference.models.mllama.utils import (
+    create_vision_mask)
+from neuronx_distributed_inference.modules.lora_serving import (
+    LoraServingConfig)
+from neuronx_distributed_inference.utils.hf_adapter import (
+    load_pretrained_config)
+from transformers import AutoModelForCausalLM, AutoTokenizer, PretrainedConfig
+
+from vllm.config import (ModelConfig, ParallelConfig, SchedulerConfig,
+                         SpeculativeConfig)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import (CompletionSequenceGroupOutput, Logprob,
+                           SequenceOutput)
+
+# yapf: enable
+logger = init_logger(__name__)
+
+TORCH_DTYPE_TO_NEURON_AMP = {
+    "auto": "float32",
+    "half": "float16",
+    "float16": "float16",
+    "bfloat16": "bfloat16",
+    "float": "float32",
+    "float32": "float32",
+    torch.float16: "float16",
+    torch.bfloat16: "bfloat16",
+    torch.float32: "float32",
+}
+
+# Models supported by Neuronx distributed for inference.
+_NEURON_SUPPORTED_MODELS: dict[str, tuple[str, str]] = {
+    "LlamaForCausalLM":
+    ("neuronx_distributed_inference.models.llama.modeling_llama",
+     "NeuronLlamaForCausalLM"),
+    "MistralForCausalLM":
+    ("neuronx_distributed_inference.models.llama.modeling_llama",
+     "NeuronLlamaForCausalLM"),
+    "DbrxForCausalLM":
+    ("neuronx_distributed_inference.models.dbrx.modeling_dbrx",
+     "NeuronDbrxForCausalLM"),
+    "MixtralForCausalLM":
+    ("neuronx_distributed_inference.models.mixtral.modeling_mixtral",
+     "NeuronMixtralForCausalLM"),
+    "MllamaForConditionalGeneration":
+    ("neuronx_distributed_inference.models.mllama.modeling_mllama",
+     "NeuronMllamaForCausalLM"),
+}
+
+
+class NeuronCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.logits_processor = LogitsProcessor(config.vocab_size,
+                                                logits_as_input=True)
+        self.sampler = Sampler()
+
+        # Lazy initialized
+        self.model: nn.Module
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                input_block_ids: torch.Tensor,
+                sampling_params: torch.Tensor,
+                prev_hidden: Optional[torch.Tensor] = None,
+                adapter_ids: Optional[torch.Tensor] = None) -> torch.Tensor:
+        # sort block ids sequentially for perf/neuron support reasons
+        sorted_input_block_ids, sorted_indices = torch.sort(input_block_ids)
+        input_ids = torch.index_select(input_ids, 0, sorted_indices)
+        positions = torch.index_select(positions, 0, sorted_indices)
+        sampling_params = torch.index_select(sampling_params, 0,
+                                             sorted_indices)
+        output = self.model(input_ids,
+                            attention_mask=None,
+                            position_ids=positions,
+                            seq_ids=sorted_input_block_ids,
+                            sampling_params=sampling_params,
+                            prev_hidden=prev_hidden,
+                            adapter_ids=adapter_ids)
+        # on-device sampling
+        if self.config.neuron_config.on_device_sampling_config:
+            output = output.hidden_states
+        else:
+            output = output.logits[:, -1, :]
+
+        restored_indices = torch.argsort(sorted_indices)
+        if input_block_ids.shape[0] != 1:
+            output = torch.index_select(output, 0, restored_indices)
+
+        return output
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(None, hidden_states, sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        # on-device sampling
+        if self.config.neuron_config.on_device_sampling_config:
+            batch_size = logits.shape
+            seq_ids = [
+                seq_id for sg in sampling_metadata.seq_groups
+                for seq_id in sg.seq_ids
+            ]
+            assert len(seq_ids) == list(batch_size)[0], "batch size mismatch"
+            # Organize input tensors by step instead of by sequence.
+            accepted_token_ids_by_step = logits.flatten()
+            accepted_token_ids_by_step = accepted_token_ids_by_step.tolist()
+
+            step_output_token_ids = []
+            for i, seq_id in enumerate(seq_ids):
+                token_id = accepted_token_ids_by_step[i]
+                step_output_token_ids.append(
+                    CompletionSequenceGroupOutput(samples=[
+                        SequenceOutput(parent_seq_id=seq_id,
+                                       output_token=token_id,
+                                       logprobs={token_id: Logprob(token_id)})
+                    ],
+                                                  prompt_logprobs=None))
+            return SamplerOutput(outputs=step_output_token_ids)
+        else:
+            return self.sampler(logits, sampling_metadata)
+
+    def load_weights(self, model_name_or_path: str, **kwargs):
+        arch = _get_model_architecture(self.config)
+        neuronx_module_path, neuronx_model_cls_name = (
+            _NEURON_SUPPORTED_MODELS[arch])
+        neuronx_module = importlib.import_module(neuronx_module_path)
+        neuronx_model_cls = getattr(neuronx_module, neuronx_model_cls_name)
+        neuron_config = neuronx_model_cls.get_neuron_config_cls()(
+            **kwargs['neuron_config'])
+        self.config.neuron_config = neuron_config
+        config = neuronx_model_cls.get_config_cls()(
+            neuron_config,
+            load_config=load_pretrained_config(model_name_or_path))
+        hashed_config = hashlib.md5(config.to_json_string().encode('utf-8'),
+                                    usedforsecurity=False).hexdigest()
+        if os.getenv("NEURON_COMPILED_ARTIFACTS") is not None:
+            compiled_model_path = os.getenv("NEURON_COMPILED_ARTIFACTS")
+        elif os.path.exists(model_name_or_path):
+            compiled_model_path = os.path.join(model_name_or_path,
+                                               "neuron-compiled-artifacts",
+                                               hashed_config)
+            shutil.rmtree(compiled_model_path, ignore_errors=True)
+        else:
+            compiled_model_path = os.path.join("local-models",
+                                               model_name_or_path,
+                                               "neuron-compiled-artifacts",
+                                               hashed_config)
+            shutil.rmtree(compiled_model_path, ignore_errors=True)
+        try:
+            self.model = neuronx_model_cls(compiled_model_path)
+            override_neuron_config = kwargs["override_neuron_config"]
+            for k, v in override_neuron_config.items():
+                setattr(self.model.config.neuron_config, k, v)
+            self.model.load(compiled_model_path)
+            return
+        except (FileNotFoundError, ValueError) as e:
+            logger.warning("Exception: %s", e)
+            logger.warning("Failed to load the model from %s, Recompiling...",
+                           compiled_model_path)
+        if not os.path.exists(model_name_or_path):
+            hf_model = AutoModelForCausalLM.from_pretrained(model_name_or_path)
+            saved_path = os.path.join("local-models", model_name_or_path)
+            hf_model.save_pretrained(saved_path)
+            model_name_or_path = saved_path
+        self.model = neuronx_model_cls(model_name_or_path, config)
+        self.model.compile(compiled_model_path)
+        self.model.load(compiled_model_path)
+
+
+class NeuronMllamaForCausalLM(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 on_device_sampling_disabled: bool = False) -> None:
+        super().__init__()
+        # has_image is the only multimodal input that is used in
+        # token-generation
+        # This is a cache (on CPU) that saves has_image data per sequence id
+        # The number of entries in this cache is <= Batch-Size
+        self.has_image_cache: dict[int, torch.Tensor] = {}
+        self.config = config
+        self.logits_processor = LogitsProcessor(
+            config.get_text_config().vocab_size, logits_as_input=True)
+
+        self.on_device_sampling_disabled = on_device_sampling_disabled
+        if self.on_device_sampling_disabled:
+            # Use default sampler
+            self.sampler = Sampler()
+
+        # Lazy initialized
+        self.model: nn.Module
+        self.is_reorder_needed: bool = True
+
+    def read_from_has_image_cache(self, seq_ids: torch.Tensor):
+        has_image_list = []
+        for index in range(len(seq_ids)):
+            seq_id = seq_ids[index].item()
+            if seq_id in self.has_image_cache:
+                has_image_list.append(self.has_image_cache[seq_id])
+            else:
+                has_image_list.append(torch.tensor([0]))
+        return torch.tensor(has_image_list)
+
+    def write_to_has_image_cache(self, seq_ids: torch.Tensor,
+                                 has_image: torch.Tensor):
+        for index in range(len(seq_ids)):
+            seq_id = seq_ids[index].item()
+            if index < len(has_image):
+                self.has_image_cache[seq_id] = has_image[index]
+            else:
+                self.has_image_cache[seq_id] = torch.zeros(1)
+
+    def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
+                seq_ids: torch.Tensor, pixel_values: torch.Tensor,
+                aspect_ratios: torch.Tensor, num_chunks: torch.Tensor,
+                has_image: torch.Tensor, sampling_params) -> torch.Tensor:
+
+        # We update the has_image cache during prefill
+        # and read the has_image cache during decode
+        if input_ids.shape[-1] > 1:  # prefill
+            self.write_to_has_image_cache(seq_ids, has_image)
+        else:
+            has_image = self.read_from_has_image_cache(seq_ids)
+            bs = input_ids.shape[0]
+            num_chunks = torch.zeros((bs, 1))
+            aspect_ratios = torch.zeros((bs, 1, 2))
+
+        input_block_ids = seq_ids
+        origin_input_block_ids = seq_ids
+        if self.is_reorder_needed:
+            # sort block ids sequentially for perf/neuron support reasons
+            input_block_ids, sorted_indices = torch.sort(input_block_ids)
+            input_ids = torch.index_select(input_ids, 0, sorted_indices)
+            positions = torch.index_select(positions, 0, sorted_indices)
+            sampling_params = torch.index_select(sampling_params, 0,
+                                                 sorted_indices)
+            pixel_values = torch.index_select(pixel_values, 0, sorted_indices)
+            aspect_ratios = torch.index_select(aspect_ratios, 0,
+                                               sorted_indices)
+            num_chunks = torch.index_select(num_chunks, 0, sorted_indices)
+            has_image = torch.index_select(has_image, 0, sorted_indices)
+
+        self.vision_mask = create_vision_mask(input_ids, self.vision_token_id)
+        output = self.model(
+            input_ids.to(torch.int32),
+            attention_mask=None,
+            position_ids=positions.to(torch.int32),
+            seq_ids=seq_ids.flatten().to(torch.int32),
+            pixel_values=pixel_values.to(
+                self.config.vision_config.torch_dtype),
+            aspect_ratios=aspect_ratios.to(torch.int32),
+            vision_mask=self.vision_mask.to(torch.int32),
+            sampling_params=sampling_params,
+            num_chunks=num_chunks.to(torch.int32),
+            has_image=has_image.to(torch.int32),
+        )
+        if self.config.neuron_config.on_device_sampling_config:
+            output = output.hidden_states
+        else:
+            output = output.logits[:, -1, :]
+
+        if self.is_reorder_needed and origin_input_block_ids.shape[0] != 1:
+            restored_indices = torch.argsort(sorted_indices)
+            output = torch.index_select(output, 0, restored_indices)
+        return output
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(None, hidden_states, sampling_metadata)
+        return logits
+
+    def sample(self, hidden_states, sampling_metadata):
+        if not self.on_device_sampling_disabled:
+            with torch.profiler.record_function("sample"):
+                hidden_states = hidden_states.flatten()
+                res = []
+                sample_idx = 0
+                for seq_group in sampling_metadata.seq_groups:
+                    seq_ids = seq_group.seq_ids
+                    samples = []
+                    for seq_id in seq_ids:
+                        token_id = hidden_states[sample_idx].item()
+                        samples.append(
+                            SequenceOutput(
+                                parent_seq_id=seq_id,
+                                output_token=token_id,
+                                logprobs={token_id: Logprob(token_id)}))
+                        sample_idx += 1
+                    res.append(
+                        CompletionSequenceGroupOutput(samples=samples,
+                                                      prompt_logprobs=None))
+                next_tokens = SamplerOutput(outputs=res)
+        else:
+            next_tokens = self.sampler(None, hidden_states, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self, model_name_or_path: str, **kwargs):
+        arch = _get_model_architecture(self.config)
+        neuronx_module_path, neuronx_model_cls_name = (
+            _NEURON_SUPPORTED_MODELS[arch])
+        neuronx_module = importlib.import_module(neuronx_module_path)
+        neuronx_model_cls = getattr(neuronx_module, neuronx_model_cls_name)
+        neuron_config = neuronx_model_cls.get_neuron_config_cls()(
+            **kwargs['neuron_config'])
+        self.config.neuron_config = neuron_config
+        logger.info("neuron_config buckets: %s",
+                    self.config.neuron_config.buckets)
+        config = neuronx_model_cls.get_config_cls()(
+            neuron_config,
+            load_config=load_pretrained_config(model_name_or_path))
+        hashed_config = hashlib.md5(config.to_json_string().encode('utf-8'),
+                                    usedforsecurity=False).hexdigest()
+        if os.getenv("NEURON_COMPILED_ARTIFACTS") is not None:
+            compiled_model_path = os.getenv("NEURON_COMPILED_ARTIFACTS")
+        elif os.path.exists(model_name_or_path):
+            compiled_model_path = os.path.join(model_name_or_path,
+                                               "neuron-compiled-artifacts",
+                                               hashed_config)
+        else:
+            compiled_model_path = os.path.join("local-models",
+                                               model_name_or_path,
+                                               "neuron-compiled-artifacts",
+                                               hashed_config)
+        try:
+            self.model = neuronx_model_cls(compiled_model_path)
+            tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+            self.vision_token_id = tokenizer(
+                "<|image|>", add_special_tokens=False).input_ids[0]
+            self.model.load(compiled_model_path)
+            return
+        except (FileNotFoundError, ValueError):
+            logger.warning("Failed to load the model from %s, Recompiling...",
+                           compiled_model_path)
+        if not os.path.exists(model_name_or_path):
+            hf_model = AutoModelForCausalLM.from_pretrained(model_name_or_path)
+            saved_path = os.path.join("local-models", model_name_or_path)
+            hf_model.save_pretrained(saved_path)
+            model_name_or_path = saved_path
+        self.model = neuronx_model_cls(model_name_or_path, config)
+
+        logger.info("\nCompiling and saving model to %s", model_name_or_path)
+
+        p = multiprocessing.Process(target=compile_model,
+                                    args=(self, compiled_model_path))
+        p.start()
+        p.join()
+
+        tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+        tokenizer.save_pretrained(compiled_model_path)
+        logger.info("Successfully compiled and saved the model in %s",
+                    compiled_model_path)
+
+        # Read "<|image|>" token_id from the tokenizer
+        self.vision_token_id = tokenizer("<|image|>",
+                                         add_special_tokens=False).input_ids[0]
+        logger.info("\nLoading model from compiled checkpoint...")
+        self.model.load(compiled_model_path)
+
+
+def compile_model(neuron_model, traced_model_path):
+    neuron_model.model.compile(traced_model_path)
+
+
+class NeuronSpeculationCausalLM(nn.Module):
+    """A Neuron-optimized causal language model with speculative decoding."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.logits_processor = LogitsProcessor(config.vocab_size,
+                                                logits_as_input=True)
+        # Lazy initialized
+        self.model: nn.Module
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        input_block_ids: torch.Tensor,
+        sampling_params: torch.Tensor,
+    ) -> torch.Tensor:
+        # sort block ids sequentially for perf/neuron support reasons
+        sorted_input_block_ids, sorted_indices = torch.sort(input_block_ids)
+        input_ids = torch.index_select(input_ids, 0, sorted_indices)
+        positions = torch.index_select(positions, 0, sorted_indices)
+        sampling_params = torch.index_select(sampling_params, 0,
+                                             sorted_indices)
+
+        output = self.model(input_ids,
+                            attention_mask=None,
+                            position_ids=positions,
+                            seq_ids=sorted_input_block_ids,
+                            sampling_params=sampling_params)
+        restored_indices = torch.argsort(sorted_indices)
+
+        # CTX encoding
+        if (positions[:, 0]).sum().item() == 0:
+            output = output.fused_outputs[0][:, 0:1]
+            if input_block_ids.shape[0] != 1:
+                output = torch.index_select(output, 0, restored_indices)
+            return output
+
+        # Fused Spec (Generation)
+        accepted_tokens_with_padding = output.fused_outputs[0]
+        next_pos_ids = output.fused_outputs[-1]
+        generated_token_counts = next_pos_ids - positions
+
+        assert torch.any(generated_token_counts == 0).item() is False, \
+            "NxDI model generated no output for one or more sequences."
+
+        batch_size, steps = accepted_tokens_with_padding.shape
+        mask = torch.arange(steps).expand(batch_size,
+                                          -1) >= generated_token_counts
+        accepted_tokens_with_padding[mask] = -1
+
+        if input_block_ids.shape[0] != 1:
+            accepted_tokens_with_padding = torch.index_select(
+                accepted_tokens_with_padding, 0, restored_indices)
+
+        return accepted_tokens_with_padding
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[list[SamplerOutput]]:
+        batch_size, num_steps = logits.shape
+        seq_ids = [
+            seq_id for sg in sampling_metadata.seq_groups
+            for seq_id in sg.seq_ids
+        ]
+        # Organize input tensors by step instead of by sequence.
+        accepted_token_ids_by_step = logits.transpose(0, 1)
+        accepted_token_ids_by_step = accepted_token_ids_by_step.tolist()
+
+        sampler_output_list = []
+        for step_index in range(num_steps):
+            if all(token_id == -1
+                   for token_id in accepted_token_ids_by_step[step_index]):
+                break
+            step_output_token_ids = []
+            for sequence_index in range(batch_size):
+                token_id = accepted_token_ids_by_step[step_index][
+                    sequence_index]
+                step_output_token_ids.append(
+                    CompletionSequenceGroupOutput(samples=[
+                        SequenceOutput(parent_seq_id=seq_ids[sequence_index],
+                                       output_token=token_id,
+                                       logprobs={token_id: Logprob(token_id)})
+                    ],
+                                                  prompt_logprobs=None))
+            sampler_output_list.append(
+                SamplerOutput(outputs=step_output_token_ids))
+        return sampler_output_list
+
+    def load_weights(self, model_name_or_path: str,
+                     draft_model_name_or_path: str, **kwargs):
+        arch = _get_model_architecture(self.config)
+        neuronx_module_path, neuronx_model_cls_name = (
+            _NEURON_SUPPORTED_MODELS[arch])
+        neuronx_module = importlib.import_module(neuronx_module_path)
+        neuronx_model_cls = getattr(neuronx_module, neuronx_model_cls_name)
+        neuron_config = neuronx_model_cls.get_neuron_config_cls()(
+            **kwargs['neuron_config'])
+        config = neuronx_model_cls.get_config_cls()(
+            neuron_config,
+            load_config=load_pretrained_config(model_name_or_path))
+
+        draft_neuron_config = copy.deepcopy(config.neuron_config)
+        if not config.neuron_config.enable_eagle_speculation:
+            draft_neuron_config.speculation_length = 0
+        draft_neuron_config.trace_tokengen_model = True
+        draft_neuron_config.enable_fused_speculation = False
+        if getattr(config.neuron_config, "draft_model_modules_to_not_convert",
+                   None):
+            draft_neuron_config.modules_to_not_convert = (
+                draft_neuron_config.draft_model_modules_to_not_convert)
+        if config.neuron_config.enable_eagle_speculation:
+            draft_neuron_config.is_eagle_draft = True
+            draft_neuron_config.sequence_parallel_enabled = False
+        draft_config = neuronx_model_cls.get_config_cls()(
+            draft_neuron_config,
+            load_config=load_pretrained_config(draft_model_name_or_path))
+        fused_spec_config = (FusedSpecNeuronConfig(
+            neuronx_model_cls._model_cls,
+            draft_config=draft_config,
+            draft_model_path=draft_model_name_or_path))
+        config.fused_spec_config = fused_spec_config
+        self.config.neuron_config = neuron_config
+
+        hashed_config = hashlib.md5(config.to_json_string().encode('utf-8'),
+                                    usedforsecurity=False).hexdigest()
+        if os.getenv("NEURON_COMPILED_ARTIFACTS") is not None:
+            compiled_model_path = os.getenv("NEURON_COMPILED_ARTIFACTS")
+        elif os.path.exists(model_name_or_path):
+            compiled_model_path = os.path.join(model_name_or_path,
+                                               "neuron-compiled-artifacts",
+                                               hashed_config)
+            shutil.rmtree(compiled_model_path, ignore_errors=True)
+        else:
+            compiled_model_path = os.path.join("local-models",
+                                               model_name_or_path,
+                                               "neuron-compiled-artifacts",
+                                               hashed_config)
+            shutil.rmtree(compiled_model_path, ignore_errors=True)
+        try:
+            self.model = neuronx_model_cls(compiled_model_path)
+            override_neuron_config = kwargs["override_neuron_config"]
+            for k, v in override_neuron_config.items():
+                setattr(self.model.config.neuron_config, k, v)
+            self.model.load(compiled_model_path)
+            return
+        except (FileNotFoundError, ValueError) as e:
+            logger.warning("Exception: %s", e)
+            logger.warning("Failed to load the model from %s Recompiling...",
+                           compiled_model_path)
+        if not os.path.exists(model_name_or_path):
+            hf_model = AutoModelForCausalLM.from_pretrained(model_name_or_path)
+            saved_path = os.path.join("local-models", model_name_or_path)
+            hf_model.save_pretrained(saved_path)
+            model_name_or_path = saved_path
+        if not os.path.exists(draft_model_name_or_path):
+            if draft_model_name_or_path != model_name_or_path:
+                hf_model = AutoModelForCausalLM.from_pretrained(
+                    draft_model_name_or_path)
+                saved_path = os.path.join("local-models",
+                                          draft_model_name_or_path)
+                hf_model.save_pretrained(saved_path)
+                draft_model_name_or_path = saved_path
+            else:
+                draft_model_name_or_path = model_name_or_path
+            config.fused_spec_config.draft_model_path = draft_model_name_or_path
+        self.model = neuronx_model_cls(model_name_or_path, config)
+        self.model.compile(compiled_model_path)
+        self.model.load(compiled_model_path)
+
+
+def _get_model_architecture(config: PretrainedConfig) -> str:
+    architectures = getattr(config, "architectures", [])
+    for arch in architectures:
+        if arch in _NEURON_SUPPORTED_MODELS:
+            return arch
+    raise ValueError(
+        f"Model architectures {architectures} are not supported on Neuron "
+        f"for now. Supported architectures: "
+        f"{list(_NEURON_SUPPORTED_MODELS.keys())}")
+
+
+def _get_default_neuron_config(model_config: ModelConfig,
+                               parallel_config: ParallelConfig,
+                               scheduler_config: SchedulerConfig,
+                               lora_serving_config: LoraServingConfig):
+    """Generate a neuron config based on vllm config args."""
+    on_device_sampling_config = OnDeviceSamplingConfig(dynamic=True,
+                                                       deterministic=False)
+    batch_size = scheduler_config.max_num_seqs
+
+    neuron_config = dict(
+        tp_degree=parallel_config.tensor_parallel_size,
+        ctx_batch_size=1,
+        batch_size=batch_size,
+        max_context_length=scheduler_config.max_model_len,
+        seq_len=scheduler_config.max_model_len,
+        enable_bucketing=True,
+        is_continuous_batching=True,
+        quantized=False,
+        torch_dtype=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
+        padding_side="right",
+        on_device_sampling_config=on_device_sampling_config,
+        sequence_parallel_enabled=True,
+        lora_serving_config=lora_serving_config)
+    return neuron_config
+
+
+def _get_default_speculation_config(model_config: ModelConfig,
+                                    parallel_config: ParallelConfig,
+                                    scheduler_config: SchedulerConfig,
+                                    speculation_config: SpeculativeConfig):
+    """Generate a neuron config for speculative decoding based on vllm config
+    args."""
+    neuron_config = dict(
+        tp_degree=parallel_config.tensor_parallel_size,
+        ctx_batch_size=1,
+        batch_size=scheduler_config.max_num_seqs,
+        max_context_length=scheduler_config.max_model_len,
+        seq_len=scheduler_config.max_model_len,
+        speculation_length=speculation_config.num_speculative_tokens,
+        trace_tokengen_model=False,
+        enable_fused_speculation=True,
+        enable_bucketing=True,
+        is_continuous_batching=True,
+        quantized=False,
+        torch_dtype=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
+        on_device_sampling_config=dict(
+            top_k=1,
+            do_sample=False,
+        ))
+    return neuron_config
+
+
+def _get_neuron_config_after_override(default_neuron_config,
+                                      overridden_neuron_config):
+    """Update default neuron config values with override args"""
+    overridden_neuron_config = overridden_neuron_config or {}
+    default_neuron_config.update(overridden_neuron_config)
+    return default_neuron_config
+
+
+def get_neuron_model(model_config: ModelConfig,
+                     parallel_config: ParallelConfig,
+                     scheduler_config: SchedulerConfig,
+                     lora_serving_config: LoraServingConfig) -> nn.Module:
+    """Initializes a neuron-optimized model for inference."""
+    model_arch = _get_model_architecture(model_config.hf_config)
+    if model_arch == "MllamaForConditionalGeneration":
+        model = NeuronMllamaForCausalLM(model_config.hf_config)
+    else:
+        model = NeuronCausalLM(model_config.hf_config)
+    default_neuron_config_args = _get_default_neuron_config(
+        model_config, parallel_config, scheduler_config, lora_serving_config)
+    neuron_config = _get_neuron_config_after_override(
+        default_neuron_config_args, model_config.override_neuron_config)
+
+    override_neuron_config = model_config.override_neuron_config
+    model.load_weights(model_config.model,
+                       neuron_config=neuron_config,
+                       override_neuron_config=override_neuron_config)
+    return model.eval()
+
+
+def get_neuron_speculation_model(model_config: ModelConfig,
+                                 parallel_config: ParallelConfig,
+                                 scheduler_config: SchedulerConfig,
+                                 speculation_config: SpeculativeConfig):
+    """Initializes a neuron-optimized speculation model for inference.
+    
+    This model handles speculation using both a draft model and an EAGLE draft. 
+    """
+    model = NeuronSpeculationCausalLM(model_config.hf_config)
+    default_neuron_config_args = _get_default_speculation_config(
+        model_config, parallel_config, scheduler_config, speculation_config)
+    neuron_config = _get_neuron_config_after_override(
+        default_neuron_config_args, model_config.override_neuron_config)
+
+    override_neuron_config = model_config.override_neuron_config
+    model.load_weights(model_config.model,
+                       speculation_config.draft_model_config.model,
+                       neuron_config=neuron_config,
+                       override_neuron_config=override_neuron_config)
+    return model.eval()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/model_loader/sharded_state_loader.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/model_loader/sharded_state_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..3edd4ec4007e85a55ea87949bc1963b9bf95bde1
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/model_loader/sharded_state_loader.py
@@ -0,0 +1,198 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import collections
+import glob
+import os
+from collections.abc import Generator
+from typing import Any, Optional
+
+import torch
+from torch import nn
+
+from vllm.config import LoadConfig, ModelConfig
+from vllm.logger import init_logger
+from vllm.model_executor.model_loader.base_loader import BaseModelLoader
+from vllm.model_executor.model_loader.weight_utils import (
+    download_weights_from_hf, runai_safetensors_weights_iterator)
+from vllm.transformers_utils.s3_utils import glob as s3_glob
+from vllm.transformers_utils.utils import is_s3
+
+logger = init_logger(__name__)
+
+
+class ShardedStateLoader(BaseModelLoader):
+    """
+    Model loader that directly loads each worker's model state dict, which
+    enables a fast load path for large tensor-parallel models where each worker
+    only needs to read its own shard rather than the entire checkpoint. See
+    `examples/offline_inference/save_sharded_state.py` for creating a sharded
+    checkpoint.
+    """
+
+    DEFAULT_PATTERN = "model-rank-{rank}-part-{part}.safetensors"
+
+    def __init__(self, load_config: LoadConfig):
+        super().__init__(load_config)
+
+        extra_config = ({} if load_config.model_loader_extra_config is None
+                        else load_config.model_loader_extra_config.copy())
+        self.pattern = extra_config.pop("pattern", self.DEFAULT_PATTERN)
+        if extra_config:
+            raise ValueError(f"Unexpected extra config keys for load format "
+                             f"{load_config.load_format}: "
+                             f"{load_config.model_loader_extra_config.keys()}")
+
+    @staticmethod
+    def _filter_subtensors(
+        tensors: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]:
+        """
+        Filter out all tensors that share the same memory or a subset of the
+        memory of another tensor.
+        """
+        same_storage_groups: dict[Any, list[tuple[str, torch.Tensor]]] = (
+            collections.defaultdict(list))
+        for key, tensor in tensors.items():
+            if tensor.numel():
+                ptr = tensor.untyped_storage().data_ptr()
+                same_storage_groups[tensor.device, ptr].append((key, tensor))
+
+        def get_end_ptr(tensor: torch.Tensor) -> int:
+            return tensor.view(-1)[-1].data_ptr() + tensor.element_size()
+
+        result: dict[str, torch.Tensor] = {}
+        for group in same_storage_groups.values():
+            for k, t in group:
+                a, b = t.data_ptr(), get_end_ptr(t)
+                for k2, t2 in group:
+                    if not t2.is_contiguous():
+                        continue
+                    a2, b2 = t2.data_ptr(), get_end_ptr(t2)
+                    if a < a2 or b2 < b:
+                        continue
+                    if a2 < a or b < b2 or not t.is_contiguous():
+                        break  # t2 covers strictly more memory than t.
+                    if k2 < k:
+                        # Same tensors, keep the one with the smaller key.
+                        break
+                else:
+                    result[k] = t
+        return result
+
+    def _prepare_weights(self, model_name_or_path: str,
+                         revision: Optional[str]):
+        if is_s3(model_name_or_path) or os.path.isdir(model_name_or_path):
+            return model_name_or_path
+        else:
+            allow_patterns = ["*.safetensors"]
+            return download_weights_from_hf(
+                model_name_or_path,
+                self.load_config.download_dir,
+                allow_patterns,
+                revision,
+                ignore_patterns=self.load_config.ignore_patterns,
+            )
+
+    def download_model(self, model_config: ModelConfig) -> None:
+        self._prepare_weights(model_config.model, model_config.revision)
+
+    def load_weights(self, model: nn.Module,
+                     model_config: ModelConfig) -> None:
+        from vllm.distributed import get_tensor_model_parallel_rank
+
+        model_weights = model_config.model
+        if hasattr(model_config, "model_weights"):
+            model_weights = model_config.model_weights
+        local_model_path = model_weights
+
+        rank = get_tensor_model_parallel_rank()
+        pattern = os.path.join(
+            local_model_path,
+            self.pattern.format(rank=rank, part="*"),
+        )
+
+        filepaths = []
+        if is_s3(local_model_path):
+            file_pattern = f"*{self.pattern.format(rank=rank, part=' * ')}"
+            filepaths = s3_glob(path=local_model_path,
+                                allow_pattern=[file_pattern])
+        else:
+            filepaths = glob.glob(pattern)
+        if not filepaths:
+            # TODO: support un-sharded checkpoints too
+            raise ValueError(
+                f"Could not find checkpoint files '{pattern}', only "
+                f"pre-sharded checkpoints are currently supported!")
+        state_dict = self._filter_subtensors(model.state_dict())
+        for key, tensor in self.iterate_over_files(filepaths):
+            # If loading with LoRA enabled, additional padding may
+            # be added to certain parameters. We only load into a
+            # narrowed view of the parameter data.
+            param_data = state_dict[key].data
+            param_shape = state_dict[key].shape
+            for dim, size in enumerate(tensor.shape):
+                if size < param_shape[dim]:
+                    param_data = param_data.narrow(dim, 0, size)
+            if tensor.shape != param_shape:
+                logger.warning(
+                    "loading tensor of shape %s into "
+                    "parameter '%s' of shape %s",
+                    tensor.shape,
+                    key,
+                    param_shape,
+                )
+            param_data.copy_(tensor)
+            state_dict.pop(key)
+        if state_dict:
+            raise ValueError(
+                f"Missing keys {tuple(state_dict)} in loaded state!")
+
+    def iterate_over_files(
+            self, paths) -> Generator[tuple[str, torch.Tensor], None, None]:
+        if self.load_config.load_format == "runai_streamer_sharded":
+            yield from runai_safetensors_weights_iterator(paths, True)
+        else:
+            from safetensors.torch import safe_open
+            for path in paths:
+                with safe_open(path, framework="pt") as f:
+                    for key in f.keys():  # noqa: SIM118
+                        tensor = f.get_tensor(key)
+                        yield key, tensor
+
+    @staticmethod
+    def save_model(
+        model: torch.nn.Module,
+        path: str,
+        pattern: Optional[str] = None,
+        max_size: Optional[int] = None,
+    ) -> None:
+        from safetensors.torch import save_file
+
+        from vllm.distributed import get_tensor_model_parallel_rank
+
+        if pattern is None:
+            pattern = ShardedStateLoader.DEFAULT_PATTERN
+        rank = get_tensor_model_parallel_rank()
+        part_idx = 0
+        total_size = 0
+        state_dict = ShardedStateLoader._filter_subtensors(model.state_dict())
+        state_dict_part: dict[str, torch.Tensor] = {}
+        for key, tensor in state_dict.items():
+            param_size = tensor.nelement() * tensor.element_size()
+            if max_size is not None and total_size + param_size > max_size:
+                filename = pattern.format(rank=rank, part=part_idx)
+                save_file(
+                    state_dict_part,
+                    os.path.join(path, filename),
+                )
+                part_idx += 1
+                total_size = 0
+                state_dict_part = {}
+            state_dict_part[key] = tensor
+            total_size += param_size
+        if len(state_dict_part) > 0:
+            filename = pattern.format(rank=rank, part=part_idx)
+            save_file(
+                state_dict_part,
+                os.path.join(path, filename),
+            )
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/arcee.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/arcee.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cf73e2e0ea56ac13dcc9a618e3938b28a89948b
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/arcee.py
@@ -0,0 +1,422 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2023-2025 vLLM Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# You may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Inference-only Arcee (AFM) model – adds support for ReLU^2 feed-forward
+# activation.
+
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from transformers import LlamaConfig
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.distributed import get_pp_group
+from vllm.model_executor.layers.activation import ReLUSquaredActivation
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers)
+
+
+class ArceeMLP(nn.Module):
+    """Feed-forward layer for Arcee using ReLU^2 activation
+    (no gating as in LLaMA)."""
+
+    def __init__(self,
+                 hidden_size: int,
+                 intermediate_size: int,
+                 hidden_act: str,
+                 quant_config: Optional[Any] = None,
+                 bias: bool = False,
+                 prefix: str = "",
+                 reduce_results: bool = True) -> None:
+        super().__init__()
+        # Single linear projection up to intermediate size
+        # (no separate gate projection)
+        self.up_proj = ColumnParallelLinear(
+            input_size=hidden_size,
+            output_size=intermediate_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.up_proj",
+        )
+        # Down projection back to hidden size
+        self.down_proj = RowParallelLinear(
+            input_size=intermediate_size,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_act != "relu2":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only 'relu2' is supported for AFM.")
+        # Define ReLU^2 activation: (ReLU(x))^2 elementwise
+        self.act_fn = ReLUSquaredActivation()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.up_proj(x)  # Project to intermediate size
+        x = self.act_fn(x)  # Apply ReLU^2 activation elementwise
+        x, _ = self.down_proj(x)  # Project back down to hidden size
+        return x
+
+
+class ArceeDecoderLayer(nn.Module):
+    """Transformer decoder block for Arcee, with self-attention and
+    ReLU^2 MLP."""
+
+    def __init__(self,
+                 config: LlamaConfig,
+                 cache_config: Optional[Any] = None,
+                 quant_config: Optional[Any] = None,
+                 prefix: str = "") -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Rotary embedding parameters (reuse LLaMA defaults)
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        if rope_scaling is not None and getattr(
+                config, "original_max_position_embeddings", None):
+            rope_scaling["original_max_position_embeddings"] = (
+                config.original_max_position_embeddings)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        # Determine if attention bias is needed (some variants use bias terms)
+        attention_bias = getattr(config, "attention_bias", False) or getattr(
+            config, "bias", False)
+        bias_o_proj = attention_bias
+        if hasattr(config, "qkv_bias"):
+            attention_bias = config.qkv_bias
+
+        # Self-Attention (using LLaMA's attention structure)
+        from vllm.model_executor.models.llama import (
+            LlamaAttention)  # import here to avoid circular import
+        self.self_attn = LlamaAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=getattr(config, "num_key_value_heads",
+                                 config.num_attention_heads),
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=attention_bias,
+            bias_o_proj=bias_o_proj,
+            cache_config=cache_config,
+            prefix=f"{prefix}.self_attn",
+            attn_type=getattr(
+                config, "attn_type",
+                "decoder"),  # assume decoder (causal) unless specified
+        )
+        # MLP with ReLU^2 activation
+        self.mlp = ArceeMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            bias=getattr(config, "mlp_bias", False),
+            prefix=f"{prefix}.mlp",
+        )
+        # Layer normalization layers (RMSNorm as in LLaMA)
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+            self, positions: torch.Tensor, hidden_states: torch.Tensor,
+            residual: Optional[torch.Tensor]
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self-Attention block
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            # Fused residual add + layernorm if supported
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(positions=positions,
+                                       hidden_states=hidden_states)
+        # Feed-forward block
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class ArceeModel(nn.Module):
+    """The transformer model backbone for Arcee (embedding layer + stacked
+    decoder blocks + final norm)."""
+
+    def __init__(self,
+                 *,
+                 vllm_config,
+                 prefix: str = "",
+                 layer_type: type[nn.Module] = ArceeDecoderLayer) -> None:
+        super().__init__()
+        config: LlamaConfig = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.quant_config = quant_config
+        self.config = config
+        self.vocab_size = config.vocab_size
+        self.org_vocab_size = config.vocab_size
+
+        # Word embeddings (parallelized if using pipeline parallel)
+        if get_pp_group().is_first_rank or (config.tie_word_embeddings
+                                            and get_pp_group().is_last_rank):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                quant_config=quant_config,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer(
+            )  # placeholder on non-embedding ranks
+
+        # Build decoder layers across pipeline ranks
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: layer_type(config=config,
+                                      cache_config=cache_config,
+                                      quant_config=quant_config,
+                                      prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+        # Final RMSNorm on the last pipeline stage
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        # For optional capturing of intermediate hidden states
+        # (not used by default)
+        self.aux_hidden_state_layers: tuple[int, ...] = tuple()
+
+        # Prepare factory for empty intermediate tensors
+        # (for pipeline scheduling)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None
+    ) -> Union[torch.Tensor, IntermediateTensors, tuple[torch.Tensor,
+                                                        list[torch.Tensor]]]:
+        # Embedding lookup (on first pipeline rank)
+        if get_pp_group().is_first_rank:
+            hidden_states = (inputs_embeds if inputs_embeds is not None else
+                             self.get_input_embeddings(input_ids))
+            residual = None
+        else:
+            assert intermediate_tensors is not None, (
+                "IntermediateTensors must be provided for non-first "
+                "pipeline ranks")
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        aux_hidden_states: list[torch.Tensor] = []
+        for idx, layer in enumerate(
+                self.layers[self.start_layer:self.end_layer]):
+            if idx in self.aux_hidden_state_layers:
+                aux_hidden_states.append(
+                    hidden_states +
+                    residual)  # capture pre-layer hidden state if needed
+            hidden_states, residual = layer(positions, hidden_states, residual)
+
+        if not get_pp_group().is_last_rank:
+            # Send intermediate results to the next pipeline stage
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        # On last rank: apply final layer norm
+        hidden_states, _ = self.norm(hidden_states, residual)
+        if len(aux_hidden_states) > 0:
+            return hidden_states, aux_hidden_states
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        """Load weights, mapping q/k/v projections to fused qkv_proj."""
+        stacked_params_mapping = [
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                continue
+
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            if "scale" in name:
+                remapped_name = maybe_remap_kv_scale_name(name, params_dict)
+                if remapped_name is None:
+                    continue
+                name = remapped_name
+
+            mapped = False
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+
+                name = name.replace(weight_name, param_name)
+
+                if name.endswith(".bias") and name not in params_dict:
+                    mapped = True
+                    break
+
+                if is_pp_missing_parameter(name, self):
+                    mapped = True
+                    break
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader  # type: ignore[attr-defined]
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name)
+                mapped = True
+                break
+
+            if mapped:
+                continue
+
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+class ArceeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    """Arcee Model for causal language modeling, integrated with vLLM
+    runtime."""
+    # Map fused module names to their sub-module components
+    # (for quantization and LoRA)
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+    }
+
+    def __init__(self, *, vllm_config, prefix: str = "") -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.config = config
+
+        # Initialize the inner Transformer model (ArceeModel)
+        self.model = ArceeModel(vllm_config=vllm_config,
+                                prefix=f"{prefix}.model")
+        # On the last pipeline stage, set up the LM head and logits processor
+        if get_pp_group().is_last_rank:
+            # Determine vocabulary size (including any LoRA extra tokens
+            # for padded LM head)
+            self.unpadded_vocab_size = config.vocab_size
+
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                quant_config=vllm_config.quant_config,
+                bias=getattr(config, "lm_head_bias", False),
+                prefix=f"{prefix}.lm_head",
+            )
+            if config.tie_word_embeddings:
+                # Tie output weights with input embedding matrix
+                self.lm_head = self.lm_head.tie_weights(
+                    self.model.embed_tokens)
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    config.vocab_size,
+                                                    logit_scale)
+        else:
+            # Placeholder for lm_head on non-last ranks
+            self.lm_head = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        model_output = self.model(input_ids=input_ids,
+                                  positions=positions,
+                                  intermediate_tensors=intermediate_tensors,
+                                  inputs_embeds=inputs_embeds)
+        return model_output
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata) -> Optional[torch.Tensor]:
+        # Compute final logits from hidden states (last pipeline rank only)
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        """Load weights into the model (delegates to inner model and handles
+        tied embeddings)."""
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+            skip_substrs=["gate_proj"])
+        # AutoWeightLoader handles weight name remapping, including fusing
+        # separate q_proj, k_proj, v_proj into qkv_proj
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/aria.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/aria.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1368a3f6478ada06be2229dbb86dd995ed9d373
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/aria.py
@@ -0,0 +1,662 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Optional, Union
+
+import torch
+import torch.nn as nn
+from transformers import AriaConfig, AriaTextConfig, BatchFeature
+from transformers.models.aria.modeling_aria import AriaCrossAttention
+from transformers.models.aria.processing_aria import AriaProcessor
+
+from vllm.config import CacheConfig, QuantizationConfig, VllmConfig
+from vllm.distributed import get_tensor_model_parallel_rank
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import MultiModalDataItems
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+# yapf: disable
+from .idefics2_vision_model import Idefics2VisionConfig
+from .idefics2_vision_model import (
+    Idefics2VisionTransformer as Idefics3VisionTransformer)
+# yapf: enable
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsQuant
+from .llama import LlamaDecoderLayer, LlamaMLP, LlamaModel
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+                    is_pp_missing_parameter, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+
+class AriaImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - b: Batch size
+        - n: Number of images
+        - c: Number of channels
+        - h: Height of each image
+        - w: Width of each image
+    """
+
+    pixel_values: Annotated[
+        torch.Tensor,
+        TensorShape("bn", 3, "h", "w"),
+    ]
+
+    pixel_mask: Annotated[
+        Optional[torch.Tensor],
+        TensorShape("bn", "h", "w"),
+    ]
+
+
+class AriaVisionTransformer(Idefics3VisionTransformer, SupportsQuant):
+    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
+
+    def __init__(
+        self,
+        config: Idefics2VisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(config, quant_config=quant_config, prefix=prefix)
+        # Unlike Idefics3VisionTransformer which uses LayerNorm after the
+        # final layer, Aria omits this normalization, so we replace it with an
+        # Identity layer
+        self.post_layernorm = nn.Identity()
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+
+            # NOTE: post_layernorm is not used in Aria
+            if "post_layernorm" in name:
+                continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class AriaProjectorMLP(nn.Module):
+
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int,
+        output_dim: int,
+    ) -> None:
+        super().__init__()
+
+        self.linear_in = ColumnParallelLinear(in_features,
+                                              hidden_features,
+                                              bias=False)
+        self.linear_out = RowParallelLinear(hidden_features,
+                                            output_dim,
+                                            bias=False)
+        self.act = get_act_fn("gelu_new")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.linear_in(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.linear_out(hidden_states)
+        return hidden_states
+
+
+class AriaProjector(nn.Module):
+    """
+    A projection module with one cross attention layer and one FFN layer, which
+    projects ViT's outputs into MoE's inputs.
+
+    Args:
+        patch_to_query_dict (dict): Maps patch numbers to their corresponding
+        query numbers,
+            e.g., {1225: 128, 4900: 256}. This allows for different query sizes
+            based on image resolution.
+        embed_dim (int): Embedding dimension.
+        num_heads (int): Number of attention heads.
+        kv_dim (int): Dimension of key and value.
+        ff_dim (int): Hidden dimension of the feed-forward network.
+        output_dim (int): Output dimension.
+        norm_layer (nn.Module): Normalization layer. Default is nn.LayerNorm.
+
+    Outputs:
+        A tensor with the shape of (batch_size, query_number, output_dim)
+    """
+
+    def __init__(self, config: AriaConfig) -> None:
+        super().__init__()
+
+        self.patch_to_query_dict = config.projector_patch_to_query_dict
+        self.in_features = config.vision_config.hidden_size
+        self.num_heads = config.vision_config.num_attention_heads
+        self.kv_dim = config.vision_config.hidden_size
+        self.hidden_features = config.text_config.hidden_size
+        self.output_dim = config.text_config.hidden_size
+
+        self.query = nn.Parameter(
+            torch.empty(config.max_value_projector_patch_to_query_dict,
+                        self.in_features))
+
+        self.cross_attn = AriaCrossAttention(config)
+
+        self.layer_norm = nn.LayerNorm(self.in_features)
+        self.feed_forward = AriaProjectorMLP(self.in_features,
+                                             self.hidden_features,
+                                             self.output_dim)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attn_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        batch_size, num_patches = x.shape[0], x.shape[1]
+
+        if num_patches not in self.patch_to_query_dict:
+            raise KeyError(f"Number of patches {num_patches} not found in "
+                           "patch_to_query_dict amongst possible values "
+                           f"{self.patch_to_query_dict.keys()}.")
+
+        query_num = self.patch_to_query_dict[num_patches]
+
+        queries = self.query[:query_num].unsqueeze(0).repeat(batch_size, 1, 1)
+
+        if attn_mask is not None:
+            attn_mask = attn_mask.repeat_interleave(self.num_heads, 0)
+            attn_mask = attn_mask.unsqueeze(1).expand(-1, queries.size(1), -1)
+
+        attention_out = self.cross_attn(x, queries, attn_mask=attn_mask)
+
+        out = self.feed_forward(self.layer_norm(attention_out))
+
+        return out
+
+
+class AriaFusedMoE(FusedMoE):
+
+    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor,
+                      shard_id: str) -> None:
+        # Override the weight_loader to handle the expert weights in the Aria
+        # model, which are already packed with experts, and merge the gate and
+        # up weights for each expert.
+        # Note: Loading expert weights with quantization is not supported
+        tp_rank = get_tensor_model_parallel_rank()
+        if shard_id == 'w13':
+            # the shape of loaded_weight is
+            # (num_experts, hidden_size, 2 * moe_intermediate_size)
+            if self.tp_size > 1:
+                up, gate = loaded_weight.chunk(2, dim=-1)
+                up_current_rank = up.chunk(self.tp_size, dim=-1)[tp_rank]
+                gate_current_rank = gate.chunk(self.tp_size, dim=-1)[tp_rank]
+                up_and_gate = torch.cat([up_current_rank, gate_current_rank],
+                                        dim=-1).transpose(1, 2)
+                param.data.copy_(up_and_gate)
+            else:
+                param.data.copy_(loaded_weight.transpose(1, 2))
+        elif shard_id == 'w2':
+            # the shape of loaded_weight is
+            # (num_experts, moe_intermediate_size, hidden_size)
+            if self.tp_size > 1:
+                down_current_rank = loaded_weight.chunk(self.tp_size,
+                                                        dim=1)[tp_rank]
+                param.data.copy_(down_current_rank.transpose(1, 2))
+            else:
+                param.data.copy_(loaded_weight.transpose(1, 2))
+
+
+class AriaTextMoELayer(nn.Module):
+    """
+    Mixture of Experts (MoE) Layer for the AriaMoE model.
+
+    This layer implements the MoE mechanism, which routes input tokens to
+    different experts based on a routing algorithm, processes them through the
+    experts, and then combines the outputs.
+    """
+
+    def __init__(
+        self,
+        config: AriaTextConfig,
+        quant_config: Optional[QuantizationConfig],
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+
+        self.router_weight = nn.Parameter(
+            torch.empty(
+                (self.config.moe_num_experts, self.config.hidden_size)))
+
+        self.experts = AriaFusedMoE(
+            num_experts=config.moe_num_experts,
+            top_k=config.moe_topk,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            quant_config=quant_config,
+            reduce_results=True,
+            prefix=f"{prefix}.experts",
+        )
+        self.shared_experts = LlamaMLP(
+            config.hidden_size,
+            config.intermediate_size * config.moe_num_shared_experts,
+            "silu",
+            quant_config=quant_config,
+            bias=config.mlp_bias,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass of the MoE Layer.
+
+        Args:
+            hidden_states (torch.Tensor): Input tensor of shape (batch_size,
+            sequence_length, hidden_size).
+
+        Returns:
+            torch.Tensor: Output tensor after passing through the MoE layer.
+        """
+
+        router_output = torch.nn.functional.linear(hidden_states,
+                                                   self.router_weight)
+
+        hidden_states_copy = hidden_states.clone()
+        # NOTE: hidden_states will be modified inplace by `FusedMoE`
+        sparse_expert_output = self.experts(hidden_states, router_output)
+        shared_expert_output = self.shared_experts(hidden_states_copy)
+
+        return sparse_expert_output + shared_expert_output
+
+
+class AriaTextDecoderLayer(LlamaDecoderLayer):
+    """
+    Custom Decoder Layer for the AriaMoE model which modifies the standard
+    `LlamaDecoderLayer` by replacing the traditional MLP with a Mixture of
+    Experts (MoE) Layer.
+    """
+
+    def __init__(
+        self,
+        config: AriaTextConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(config, cache_config, quant_config, prefix)
+        self.mlp = AriaTextMoELayer(config,
+                                    quant_config=quant_config,
+                                    prefix=f"{prefix}.mlp")
+
+
+class AriaTextModel(LlamaModel, SupportsQuant):
+    """
+    Custom LlamaModel for the AriaMoE model which modifies the standard
+    LlamaModel by replacing the `LlamaDecoderLayer` with `MoEDecoderLayer`.
+    """
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+        "experts.w13_weight": ["experts.fc1.weight"],
+        "experts.w2_weight": ["experts.fc2.weight"],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config,
+                         prefix=prefix,
+                         layer_type=AriaTextDecoderLayer)
+
+    # Adapted from LlamaModel.load_weights with the modification of adding
+    # the expert weights mapping to `stacked_params_mapping`
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+            ("experts.w13_weight", "experts.fc1.weight", 'w13'),
+            ("experts.w2_weight", "experts.fc2.weight", 'w2'),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class AriaProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(AriaConfig)
+
+    def get_vision_config(self):
+        return self.get_hf_config().vision_config
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(AriaProcessor, **kwargs)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_num_image_tokens(self) -> int:
+        hf_config = self.get_hf_config()
+        return max(hf_config.projector_patch_to_query_dict.values())
+
+
+class AriaDummyInputsBuilder(BaseDummyInputsBuilder[AriaProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        processor = self.info.get_hf_processor()
+        image_token: str = processor.tokenizer.image_token  # type: ignore
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        vision_config = self.info.get_vision_config()
+
+        max_image_size = vision_config.image_size
+        num_images = mm_counts.get("image", 0)
+
+        return {
+            "image":
+            self._get_dummy_images(width=max_image_size,
+                                   height=max_image_size,
+                                   num_images=num_images)
+        }
+
+
+class AriaMultiModalProcessor(BaseMultiModalProcessor[AriaProcessingInfo]):
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            pixel_values=MultiModalFieldConfig.batched("image"),
+            pixel_mask=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_config = self.info.get_hf_config()
+        image_token_id = hf_config.image_token_index
+
+        num_image_tokens = self.info.get_num_image_tokens()
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=[image_token_id],
+                replacement=[image_token_id] * num_image_tokens,
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(AriaMultiModalProcessor,
+                                        info=AriaProcessingInfo,
+                                        dummy_inputs=AriaDummyInputsBuilder)
+class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
+    """
+    Aria model for conditional generation tasks.
+
+    This model combines a vision tower, a multi-modal projector, and a language
+    model to perform tasks that involve both image and text inputs.
+    """
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            # mapping for new names in checkpoint saved after transformers v4.52
+            "model.language_model.": "language_model.model.",
+            "model.vision_tower.": "vision_tower.",
+            "model.multi_modal_projector.": "multi_modal_projector.",
+            # mapping for original checkpoint
+            "language_model.model": "language_model",
+            "language_model.lm_head": "lm_head",
+        },
+        orig_to_new_suffix={
+            "router.weight": "router_weight",
+        },
+    )
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<|fim_prefix|><|img|><|fim_suffix|>"
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.vision_tower = AriaVisionTransformer(
+            config.vision_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.vision_tower",
+        )
+        self.multi_modal_projector = AriaProjector(config)
+        self.vocab_size = config.text_config.vocab_size
+        self.language_model = AriaTextModel(
+            vllm_config=vllm_config.with_hf_config(config.text_config),
+            prefix=maybe_prefix(prefix, "language_model.model"),
+        )
+        self.pad_token_id = (self.config.pad_token_id
+                             if self.config.pad_token_id is not None else -1)
+        self.unpadded_vocab_size = config.text_config.vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.text_config.hidden_size,
+            org_num_embeddings=self.language_model.org_vocab_size,
+            quant_config=quant_config,
+        )
+        logit_scale = getattr(config, "logit_scale", 1.0)
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                self.vocab_size, logit_scale)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[AriaImagePixelInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        pixel_mask = kwargs.pop("pixel_mask", None)
+
+        if pixel_values is None:
+            return None
+
+        return AriaImagePixelInputs(
+            pixel_values=flatten_bn(pixel_values, concat=True),
+            pixel_mask=flatten_bn(pixel_mask, concat=True),
+        )
+
+    def _create_patch_attention_mask(
+            self, pixel_mask: Optional[torch.Tensor]) -> torch.Tensor:
+        if pixel_mask is None:
+            return None
+
+        patches_subgrid = pixel_mask.unfold(
+            dimension=1,
+            size=self.vision_tower.config.patch_size,
+            step=self.vision_tower.config.patch_size,
+        ).unfold(
+            dimension=2,
+            size=self.vision_tower.config.patch_size,
+            step=self.vision_tower.config.patch_size,
+        )
+        return (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
+
+    def _process_image_input(
+        self, image_input: AriaImagePixelInputs
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        assert self.vision_tower is not None
+
+        pixel_values = image_input['pixel_values']
+        pixel_mask = image_input['pixel_mask']
+
+        patch_attention_mask = self._create_patch_attention_mask(pixel_mask)
+
+        image_outputs = self.vision_tower(
+            pixel_values=pixel_values,
+            patch_attention_mask=patch_attention_mask,
+        )
+        image_attn_mask = None
+        if patch_attention_mask is not None:
+            flattened_mask = patch_attention_mask.flatten(1)
+            image_attn_mask = torch.logical_not(flattened_mask)
+
+        return self.multi_modal_projector(image_outputs, image_attn_mask)
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+        multimodal_embeddings = self._process_image_input(image_input)
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.config.image_token_index)
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if inputs_embeds is None:
+            multimodal_embeddings = self.get_multimodal_embeddings(**kwargs)
+            # always pass the input via `inputs_embeds`
+            # to make sure the computation graph is consistent
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      multimodal_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model(
+            input_ids,
+            positions,
+            intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        loader = AutoWeightsLoader(self)
+        loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/baichuan.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/baichuan.py
new file mode 100644
index 0000000000000000000000000000000000000000..804a2f1785d5c4efb7c2f43fa208c2ff136cf8b8
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/baichuan.py
@@ -0,0 +1,474 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only BaiChuan model compatible with HuggingFace weights."""
+import math
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, row_parallel_weight_loader)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers)
+
+
+def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
+    closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
+    base = torch.tensor(
+        2**(-(2**-(math.log2(closest_power_of_2) - 3))),
+        dtype=torch.float32,
+    )
+    powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
+    slopes = torch.pow(base, powers)
+
+    if closest_power_of_2 != total_num_heads:
+        extra_base = torch.tensor(
+            2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
+            dtype=torch.float32,
+        )
+        num_remaining_heads = min(closest_power_of_2,
+                                  total_num_heads - closest_power_of_2)
+        extra_powers = torch.arange(start=1,
+                                    end=1 + 2 * num_remaining_heads,
+                                    step=2,
+                                    dtype=torch.int32)
+        slopes = torch.cat(
+            [slopes, torch.pow(extra_base, extra_powers)], dim=0)
+    return slopes
+
+
+class BaiChuanMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class BaiChuanAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        position_embedding: str,
+        rope_theta: float = 10000,
+        max_position_embeddings: int = 8192,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
+        )
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.position_embedding = position_embedding
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        # pylint: disable=invalid-name
+        self.W_pack = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_heads,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+        # Create the alibi slopes and slice them.
+        if self.position_embedding == "ALIBI":
+            tp_rank = get_tensor_model_parallel_rank()
+            head_start = tp_rank * self.num_heads
+            head_end = (tp_rank + 1) * self.num_heads
+            alibi_slopes = _get_alibi_slopes(self.total_num_heads)
+            alibi_slopes = alibi_slopes[head_start:head_end].tolist()
+
+            scaling = self.head_dim**-0.5
+            self.attn = Attention(self.num_heads,
+                                  self.head_dim,
+                                  scaling,
+                                  alibi_slopes=alibi_slopes,
+                                  quant_config=quant_config,
+                                  prefix=f"{prefix}.attn")
+        else:
+            self.rotary_emb = get_rope(
+                self.head_dim,
+                rotary_dim=self.head_dim,
+                max_position=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+            self.scaling = self.head_dim**-0.5
+            self.attn = Attention(self.num_heads,
+                                  self.head_dim,
+                                  self.scaling,
+                                  cache_config=cache_config,
+                                  quant_config=quant_config,
+                                  prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.W_pack(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        if self.position_embedding != "ALIBI":
+            q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class BaiChuanDecoderLayer(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 position_embedding: str,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = BaiChuanAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            position_embedding=position_embedding,
+            rope_theta=rope_theta,
+            max_position_embeddings=max_position_embeddings,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.mlp = BaiChuanMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class BaiChuanModel(nn.Module):
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        position_embedding: str = "ROPE",
+    ) -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: BaiChuanDecoderLayer(config,
+                                                position_embedding,
+                                                cache_config,
+                                                quant_config,
+                                                prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual,
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
+                              SupportsQuant):
+    packed_modules_mapping = {
+        "W_pack": ["W_pack"],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        position_embedding: str = "ROPE",
+    ):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.lora_config = lora_config
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.quant_config = quant_config
+        self.model = BaiChuanModel(vllm_config=vllm_config,
+                                   prefix=prefix,
+                                   position_embedding=position_embedding)
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config)
+        self.lm_head.weight.weight_loader = self.lm_head_weight_loader
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def lm_head_weight_loader(self, param: nn.Parameter,
+                              loaded_weight: torch.Tensor):
+        # Unlike Baichuan, Baichuan2 normalizes the head weights.
+        # Refer to:
+        # https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/84603cde5ebffb6084e476cfaeceaf0b8b91fe54/modeling_baichuan.py#L508
+        # Distinguish between Baichuan and Baichuan2 by checking the
+        # vocab size. This is suggested by
+        # https://github.com/vllm-project/vllm/pull/1022#discussion_r1325652704
+        is_baichuan2 = self.config.vocab_size == 125696
+        if is_baichuan2:
+            loaded_weight = torch.nn.functional.normalize(loaded_weight)
+        if self.tp_size > 1:
+            row_parallel_weight_loader(param, loaded_weight)
+        else:
+            default_weight_loader(param, loaded_weight)
+
+
+class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
+    """Baichuan 13B and Baichuan2 7B/13B.
+    NOTE: the class name has a lower case 'c'.
+    """
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_config
+        if config.hidden_size == 4096:  # baichuan2 7b
+            super().__init__(vllm_config=vllm_config,
+                             prefix=prefix,
+                             position_embedding="ROPE")
+        else:  # baichuan 13b, baichuan2 13b
+            super().__init__(vllm_config=vllm_config,
+                             prefix=prefix,
+                             position_embedding="ALIBI")
+
+
+class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
+    """Baichuan 7B.
+    NOTE: the class name has an upper case 'C'.
+    """
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config,
+                         prefix=prefix,
+                         position_embedding="ROPE")
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/bert.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/bert.py
new file mode 100644
index 0000000000000000000000000000000000000000..6638f06f98261129efa639aca93504715dcde978
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/bert.py
@@ -0,0 +1,612 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable, Set
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import BertConfig
+
+from vllm.attention import Attention, AttentionType
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, PoolerConfig, VllmConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.pooler import (ClassifierPooler,
+                                               DispatchPooler, Pooler,
+                                               PoolingMethod,
+                                               PoolingParamsUpdate,
+                                               PoolingType)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.pooling_metadata import PoolingMetadata
+from vllm.sequence import IntermediateTensors
+from vllm.tasks import PoolingTask
+
+from .interfaces import (SupportsCrossEncoding, SupportsQuant,
+                         default_pooling_type)
+from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
+
+
+class BertEmbedding(nn.Module):
+
+    def __init__(self, config: BertConfig):
+
+        super().__init__()
+        self.size = config.hidden_size
+        self.word_embeddings = VocabParallelEmbedding(config.vocab_size,
+                                                      config.hidden_size)
+        self.position_embeddings = VocabParallelEmbedding(
+            config.max_position_embeddings, config.hidden_size)
+        self.token_type_embeddings = VocabParallelEmbedding(
+            config.type_vocab_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size,
+                                      eps=config.layer_norm_eps)
+
+        self.register_buffer(
+            "position_ids",
+            torch.arange(config.max_position_embeddings).unsqueeze(0),
+        )
+        self.position_embedding_type = config.position_embedding_type
+        if self.position_embedding_type != "absolute":
+            raise ValueError("Only 'absolute' position_embedding_type" +
+                             " is supported")
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+    ) -> torch.Tensor:
+
+        token_type_ids = _decode_token_type_ids(input_ids)
+
+        inputs_embeds = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + token_type_embeddings + position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        return embeddings
+
+
+class BertPooler(Pooler):
+
+    def __init__(self, config: BertConfig):
+        super().__init__()
+
+        self.pooling = PoolingMethod.from_pooling_type(PoolingType.CLS)
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def get_supported_tasks(self) -> Set[PoolingTask]:
+        return self.pooling.get_supported_tasks()
+
+    def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
+        return self.pooling.get_pooling_updates(task)
+
+    def _head(self, pooled_output: torch.Tensor):
+        pooled_output = self.dense(pooled_output)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+    def forward(
+        self,
+        hidden_states: Union[torch.Tensor, list[torch.Tensor]],
+        pooling_metadata: PoolingMetadata,
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+        pooled_output = self.pooling(hidden_states, pooling_metadata)
+
+        if isinstance(pooled_output, list):
+            pooled_output = [self._head(output) for output in pooled_output]
+        else:
+            pooled_output = self._head(pooled_output)
+
+        return pooled_output
+
+
+class BertEncoder(nn.Module):
+
+    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.layer = nn.ModuleList([
+            BertLayer(config=config,
+                      cache_config=cache_config,
+                      quant_config=quant_config,
+                      prefix=f"{prefix}.layer.{layer_idx}")
+            for layer_idx in range(config.num_hidden_layers)
+        ])
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        for layer in self.layer:
+            hidden_states = layer(hidden_states)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+
+    def __init__(self,
+                 config: BertConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+
+        self.attention = BertAttention(
+            hidden_size=config.hidden_size,
+            num_attention_heads=config.num_attention_heads,
+            layer_norm_eps=config.layer_norm_eps,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attention")
+
+        self.intermediate = BertIntermediate(
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=f"{prefix}.intermediate")
+
+        self.output = BertOutput(hidden_size=config.hidden_size,
+                                 intermediate_size=config.intermediate_size,
+                                 layer_norm_eps=config.layer_norm_eps,
+                                 quant_config=quant_config,
+                                 prefix=f"{prefix}.output")
+
+    def forward(self, hidden_states: torch.Tensor):
+        attn_output = self.attention(hidden_states)
+        intermediate_output = self.intermediate(attn_output)
+        output = self.output(intermediate_output, attn_output)
+        return output
+
+
+class BertAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        layer_norm_eps: float,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.self = BertSelfAttention(hidden_size=hidden_size,
+                                      num_attention_heads=num_attention_heads,
+                                      cache_config=cache_config,
+                                      quant_config=quant_config,
+                                      prefix=f"{prefix}.output")
+
+        self.output = BertSelfOutput(hidden_size=hidden_size,
+                                     layer_norm_eps=layer_norm_eps,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.output")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        self_output = self.self(hidden_states)
+        return self.output(self_output, hidden_states)
+
+
+class BertSelfAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+
+        self.total_num_heads = num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = self.total_num_heads
+        self.head_dim = self.hidden_size // self.total_num_heads
+        assert self.head_dim * self.total_num_heads == self.hidden_size
+
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj")
+
+        self.attn = Attention(num_heads=self.num_heads,
+                              head_size=self.head_dim,
+                              scale=self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn",
+                              attn_type=AttentionType.ENCODER_ONLY)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        output = self.attn(q, k, v)
+        return output
+
+
+class BertSelfOutput(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 layer_norm_eps: float,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.dense = RowParallelLinear(input_size=hidden_size,
+                                       output_size=hidden_size,
+                                       bias=True,
+                                       quant_config=quant_config,
+                                       prefix=f"{prefix}.dense")
+        self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+
+    def forward(self, hidden_states: torch.Tensor,
+                input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.dense(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertIntermediate(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 intermediate_size: int,
+                 hidden_act: str,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.dense = ColumnParallelLinear(input_size=hidden_size,
+                                          output_size=intermediate_size,
+                                          bias=True,
+                                          quant_config=quant_config,
+                                          prefix=f"{prefix}.dense")
+        self.intermediate_act_fn = get_act_fn(hidden_act)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 intermediate_size: int,
+                 layer_norm_eps: float,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+
+        self.dense = RowParallelLinear(input_size=intermediate_size,
+                                       output_size=hidden_size,
+                                       bias=True,
+                                       quant_config=quant_config,
+                                       prefix=f"{prefix}.dense")
+
+        self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+
+    def forward(self, hidden_states: torch.Tensor,
+                input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.dense(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+@support_torch_compile
+@default_pooling_type("CLS")
+class BertModel(nn.Module, SupportsQuant):
+
+    is_pooling_model = True
+
+    packed_modules_mapping = {"qkv_proj": ["query", "key", "value"]}
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        embedding_class: type[nn.Module] = BertEmbedding,
+    ) -> None:
+        super().__init__()
+
+        self.config = vllm_config.model_config.hf_config
+        self.embeddings = embedding_class(self.config)
+        self.encoder = BertEncoder(vllm_config=vllm_config,
+                                   prefix=f"{prefix}.encoder")
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if inputs_embeds is not None:
+            hidden_states = inputs_embeds
+        else:
+            hidden_states = self.embeddings(input_ids=input_ids,
+                                            position_ids=positions)
+        return self.encoder(hidden_states)
+
+    def _load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "query", "q"),
+            ("qkv_proj", "key", "k"),
+            ("qkv_proj", "value", "v"),
+        ]
+
+        loaded_stacked_params = []
+        other_weights = []
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+
+                name = name.replace(weight_name, param_name)
+                if name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_stacked_params.append(name)
+                break
+            else:
+                if name in params_dict:
+                    other_weights.append((name, loaded_weight))
+
+        return other_weights, loaded_stacked_params
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        other_weights, loaded_stacked_params = self._load_weights(weights)
+
+        loader = AutoWeightsLoader(self, skip_prefixes=["pooler."])
+        loaded_params = loader.load_weights(other_weights)
+        loaded_params.update(loaded_stacked_params)
+        return loaded_params
+
+
+@default_pooling_type("ALL")
+class BertPoolingModel(BertModel):
+
+    is_pooling_model = True
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        embedding_class: type[nn.Module] = BertEmbedding,
+    ) -> None:
+        super().__init__(
+            vllm_config=vllm_config,
+            prefix=prefix,
+            embedding_class=embedding_class,
+        )
+
+        config = vllm_config.model_config.hf_config
+        self.pooler = BertPooler(config)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        other_weights, loaded_stacked_params = self._load_weights(weights)
+
+        loader = AutoWeightsLoader(self)
+        loaded_params = loader.load_weights(other_weights)
+        loaded_params.update(loaded_stacked_params)
+        return loaded_params
+
+
+@default_pooling_type("CLS")
+class BertEmbeddingModel(nn.Module, SupportsQuant):
+    """A model that uses Bert to provide embedding functionalities.
+
+    This class encapsulates the BertModel and provides an interface for
+    embedding operations and customized pooling functions.
+
+    Attributes:
+        model: An instance of BertModel used for forward operations.
+        _pooler: An instance of Pooler used for pooling operations.
+    """
+
+    is_pooling_model = True
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.model = self._build_model(vllm_config=vllm_config,
+                                       prefix=maybe_prefix(prefix, "model"))
+        self.pooler = self._build_pooler(pooler_config)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return self.model(input_ids=input_ids,
+                          positions=positions,
+                          inputs_embeds=inputs_embeds,
+                          intermediate_tensors=intermediate_tensors)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        weights_list = list(weights)
+
+        has_model_prefix = any(
+            name.startswith("model.") for name, _ in weights_list)
+        if not has_model_prefix:
+            mapper = WeightsMapper(orig_to_new_prefix={"": "model."})
+
+        loader = AutoWeightsLoader(self, skip_prefixes=["lm_head."])
+        return loader.load_weights(weights_list, mapper=mapper)
+
+    def _build_model(self,
+                     vllm_config: VllmConfig,
+                     prefix: str = "") -> BertModel:
+        return BertModel(vllm_config=vllm_config,
+                         prefix=prefix,
+                         embedding_class=BertEmbedding)
+
+    def _build_pooler(self, pooler_config: PoolerConfig) -> Pooler:
+        return DispatchPooler({
+            "encode": Pooler.for_encode(pooler_config),
+            "embed": Pooler.for_embed(pooler_config),
+        })
+
+
+# Here we encode the token type ids together with the input ids.
+# Since we use int 32 for the input IDs and the vocabulary size
+# is way lower than 2**31, there is room to encode additional
+# bits. At the same time, for cross-encoder use cases, the
+# token type ids are only 0 or 1, requiring only 1 bit.
+# This means that we can store the token type ids in the 31st
+# bit. We void the 32nd bit because that would produce a negative
+# number, which could be used to signal other things.
+#
+# The reason for all of this is that all the tensors that are
+# passed as input to the forward function of a module marked
+# with @support_torch_compile have to be persistent. So to
+# avoid adding more persistent tensors in the model runner, we
+# encode more information in the same persistent tensor.
+#
+# Since the *ForClassification module is outside of the BertModel
+# which is compiled, we can do the encoding here and then separate
+# the information again in the Embedding  layer. Since with bit masks
+# we can do this entirely with torch operations and without branching,
+# it works with torch compile.
+
+TOKEN_TYPE_SHIFT = 30
+
+
+def _encode_token_type_ids(input_ids: torch.Tensor,
+                           token_type_ids: torch.Tensor) -> None:
+    # input_ids can be padded to the right
+    input_ids[:token_type_ids.shape[0]].bitwise_or_(
+        token_type_ids << TOKEN_TYPE_SHIFT)
+
+
+def _decode_token_type_ids(input_ids: torch.Tensor) -> torch.Tensor:
+
+    ids_mask = torch.ones(input_ids.shape,
+                          dtype=torch.int32,
+                          device=input_ids.device) << TOKEN_TYPE_SHIFT
+    tokens_mask = ids_mask.bitwise_not()
+
+    token_type_ids = input_ids.bitwise_and(ids_mask) >> TOKEN_TYPE_SHIFT
+
+    input_ids.bitwise_and_(tokens_mask)
+
+    return token_type_ids
+
+
+@default_pooling_type("CLS")
+class BertForSequenceClassification(nn.Module, SupportsCrossEncoding,
+                                    SupportsQuant):
+    """A model that uses Bert to provide embedding functionalities.
+
+   This class encapsulates the BertModel and provides an interface for
+   embedding operations and customized pooling functions.
+
+   Attributes:
+       model: An instance of BertModel used for forward operations.
+       _pooler: An instance of Pooler used for pooling operations.
+   """
+
+    is_pooling_model = True
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+
+        self.num_labels = config.num_labels
+        self.bert = BertPoolingModel(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(prefix, "bert"),
+                                     embedding_class=BertEmbedding)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.pooler = DispatchPooler({
+            "encode":
+            Pooler.for_encode(pooler_config),
+            "classify":
+            ClassifierPooler(
+                pooling=self.bert.pooler,
+                classifier=self.classifier,
+                act_fn=ClassifierPooler.act_fn_for_seq_cls(
+                    vllm_config.model_config),
+            ),
+            "score":
+            ClassifierPooler(
+                pooling=self.bert.pooler,
+                classifier=self.classifier,
+                act_fn=ClassifierPooler.act_fn_for_cross_encoder(
+                    vllm_config.model_config),
+            ),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        loader = AutoWeightsLoader(self)
+        loaded_params = loader.load_weights(weights)
+        return loaded_params
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        if token_type_ids is not None:
+            assert self.bert.config.vocab_size < (1 << TOKEN_TYPE_SHIFT)
+            assert input_ids is not None
+            _encode_token_type_ids(input_ids, token_type_ids)
+
+        return self.bert(input_ids=input_ids,
+                         positions=positions,
+                         inputs_embeds=inputs_embeds,
+                         intermediate_tensors=intermediate_tensors)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/bloom.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/bloom.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e4a399f3cc6e12c917fa8d3333feeb0b2cc53f8
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/bloom.py
@@ -0,0 +1,373 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/bloom/modeling_bloom.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 HuggingFace Inc. team and BigScience workshop.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only BLOOM model compatible with HuggingFace weights."""
+import math
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import BloomConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP, SupportsQuant, SupportsV0Only
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
+    closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
+    base = torch.tensor(
+        2**(-(2**-(math.log2(closest_power_of_2) - 3))),
+        dtype=torch.float32,
+    )
+    powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
+    slopes = torch.pow(base, powers)
+
+    if closest_power_of_2 != total_num_heads:
+        extra_base = torch.tensor(
+            2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
+            dtype=torch.float32,
+        )
+        num_remaining_heads = min(closest_power_of_2,
+                                  total_num_heads - closest_power_of_2)
+        extra_powers = torch.arange(start=1,
+                                    end=1 + 2 * num_remaining_heads,
+                                    step=2,
+                                    dtype=torch.int32)
+        slopes = torch.cat(
+            [slopes, torch.pow(extra_base, extra_powers)], dim=0)
+    return slopes
+
+
+class BloomAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: BloomConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.total_num_heads = config.n_head
+        self.head_dim = self.hidden_size // self.total_num_heads
+        assert self.head_dim * self.total_num_heads == self.hidden_size
+
+        tp_world_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_world_size == 0
+        self.num_heads = self.total_num_heads // tp_world_size
+
+        self.query_key_value = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.dense = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+        )
+
+        # Create the alibi slopes and slice them.
+        tp_rank = get_tensor_model_parallel_rank()
+        head_start = tp_rank * self.num_heads
+        head_end = (tp_rank + 1) * self.num_heads
+        alibi_slopes = _get_alibi_slopes(self.total_num_heads)
+        alibi_slopes = alibi_slopes[head_start:head_end].tolist()
+
+        scaling = self.head_dim**-0.5
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              scaling,
+                              alibi_slopes=alibi_slopes,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        del position_ids  # Unused.
+        qkv, _ = self.query_key_value(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.dense(attn_output)
+        return output
+
+
+class BloomMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: BloomConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.dense_h_to_4h = ColumnParallelLinear(
+            hidden_size,
+            4 * hidden_size,
+            quant_config=quant_config,
+        )
+        self.gelu_impl = get_act_fn("gelu")
+        self.dense_4h_to_h = RowParallelLinear(
+            4 * hidden_size,
+            hidden_size,
+            quant_config=quant_config,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.dense_h_to_4h(x)
+        x = self.gelu_impl(x)
+        x, _ = self.dense_4h_to_h(x)
+        return x
+
+
+class BloomBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: BloomConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+
+        self.input_layernorm = nn.LayerNorm(hidden_size,
+                                            eps=config.layer_norm_epsilon)
+        self.self_attention = BloomAttention(config,
+                                             cache_config,
+                                             quant_config,
+                                             prefix=f"{prefix}.self_attention")
+        self.post_attention_layernorm = nn.LayerNorm(
+            hidden_size, eps=config.layer_norm_epsilon)
+        self.mlp = BloomMLP(config, quant_config)
+        self.apply_residual_connection_post_layernorm = (
+            config.apply_residual_connection_post_layernorm)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        # Layer norm at the beginning of the transformer layer.
+        layernorm_output = self.input_layernorm(hidden_states)
+
+        # Layer norm post the self attention.
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = hidden_states
+
+        # Self attention.
+        attention_output = self.self_attention(
+            position_ids=position_ids,
+            hidden_states=layernorm_output,
+        )
+        attention_output = attention_output + residual
+        layernorm_output = self.post_attention_layernorm(attention_output)
+
+        # Get residual
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = attention_output
+
+        # MLP.
+        output = self.mlp(layernorm_output) + residual
+        return output
+
+
+@support_torch_compile
+class BloomModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+
+        self.embed_dim = config.hidden_size
+
+        # Embedding + LN Embedding
+        self.word_embeddings = VocabParallelEmbedding(
+            config.vocab_size,
+            self.embed_dim,
+        )
+        self.word_embeddings_layernorm = nn.LayerNorm(
+            self.embed_dim, eps=config.layer_norm_epsilon)
+
+        # Transformer blocks
+        self.start_layer, self.end_layer, self.h = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: BloomBlock(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.h")
+
+        # Final Layer Norm
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.word_embeddings_layernorm(self.word_embeddings(input_ids))
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+        for layer in self.h[self.start_layer:self.end_layer]:
+            hidden_states = layer(position_ids, hidden_states)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if is_pp_missing_parameter(name, self):
+                continue
+            param = params_dict[name]
+
+            if "query_key_value" in name:
+                # NOTE: BLOOM's fused QKV's output_dim has the shape of
+                # (num_heads * 3 * head_size), while the
+                # required shape is (3 * num_heads * head_size).
+                # Thus, we need weight conversion.
+                output_dim = getattr(param, "output_dim", None)
+                num_heads = self.config.num_attention_heads
+                if output_dim is not None:
+                    loaded_weight_shape = loaded_weight.shape
+                    loaded_weight = loaded_weight.view(
+                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
+                        loaded_weight_shape[output_dim + 1:])
+                    loaded_weight = loaded_weight.transpose(
+                        output_dim, output_dim + 1)
+                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)
+
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+class BloomForCausalLM(nn.Module, SupportsPP, SupportsV0Only, SupportsQuant):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.transformer = BloomModel(vllm_config=vllm_config,
+                                      prefix=maybe_prefix(
+                                          prefix, "transformer"))
+        if self.config.tie_word_embeddings:
+            self.lm_head = self.transformer.word_embeddings
+        else:
+            self.lm_head = ParallelLMHead(self.config.vocab_size,
+                                          self.config.hidden_size)
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.transformer.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.transformer(input_ids, positions,
+                                         intermediate_tensors, inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self, skip_prefixes=["lm_head.weight"])
+        weights = _add_transformer_prefix(weights)
+        return loader.load_weights(weights)
+
+
+def _add_transformer_prefix(
+    weights: Iterable[tuple[str, torch.Tensor]]
+) -> Iterable[tuple[str, torch.Tensor]]:
+    for name, tensor in weights:
+        if not name.startswith('transformer.'):
+            name = 'transformer.' + name
+        yield name, tensor
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/clip.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcab0082287046a4870361f15ba8a6f9bdf7d3f6
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/clip.py
@@ -0,0 +1,407 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Minimal implementation of CLIPVisionModel intended to be only used
+within a vision language model."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+from transformers import CLIPVisionConfig
+
+from vllm.attention.layer import MultiHeadAttention
+from vllm.distributed import divide, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.interfaces import SupportsQuant
+
+from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
+
+
+class CLIPEncoderInfo(VisionEncoderInfo[CLIPVisionConfig]):
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        return self.get_patch_grid_length()**2 + 1
+
+    def get_image_size(self) -> int:
+        return self.vision_config.image_size
+
+    def get_patch_size(self) -> int:
+        return self.vision_config.patch_size
+
+    def get_patch_grid_length(self) -> int:
+        image_size, patch_size = self.get_image_size(), self.get_patch_size()
+        assert image_size % patch_size == 0
+        return image_size // patch_size
+
+
+# Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/clip/modeling_clip.py#L164 # noqa
+class CLIPVisionEmbeddings(nn.Module):
+
+    def __init__(self, config: CLIPVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+        assert self.image_size % self.patch_size == 0
+
+        self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            bias=False,
+        )
+
+        self.num_patches = (self.image_size // self.patch_size)**2
+        self.num_positions = self.num_patches + 1
+        self.position_embedding = nn.Embedding(self.num_positions,
+                                               self.embed_dim)
+        self.register_buffer("position_ids",
+                             torch.arange(self.num_positions).expand((1, -1)),
+                             persistent=False)
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        batch_size = pixel_values.shape[0]
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values.to(
+            dtype=target_dtype))  # shape = [*, width, grid, grid]
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+        embeddings = embeddings + self.position_embedding(self.position_ids)
+
+        return embeddings
+
+
+class CLIPAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                "embed_dim must be divisible by num_heads "
+                f"(got `embed_dim`: {self.embed_dim} and `num_heads`:"
+                f" {self.num_heads}).")
+        self.scale = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.embed_dim,
+            head_size=self.head_dim,
+            total_num_heads=self.num_heads,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.out_proj = RowParallelLinear(
+            input_size=self.embed_dim,
+            output_size=self.embed_dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
+
+        self.attn = MultiHeadAttention(self.num_heads_per_partition,
+                                       self.head_dim, self.scale)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ):
+        """Input shape: Batch x Time x Channel"""
+
+        qkv_states, _ = self.qkv_proj(hidden_states)
+        query_states, key_states, value_states = qkv_states.chunk(3, dim=-1)
+        out = self.attn(query_states, key_states, value_states)
+        attn_output, _ = self.out_proj(out)
+
+        return attn_output, None
+
+
+class CLIPMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.activation_fn = get_act_fn(config.hidden_act)
+        self.fc1 = ColumnParallelLinear(config.hidden_size,
+                                        config.intermediate_size,
+                                        bias=True,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.fc1")
+        self.fc2 = RowParallelLinear(config.intermediate_size,
+                                     config.hidden_size,
+                                     bias=True,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.fc2")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+
+        return hidden_states
+
+
+class CLIPEncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.self_attn = CLIPAttention(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.layer_norm1 = nn.LayerNorm(config.hidden_size,
+                                        eps=config.layer_norm_eps)
+        self.mlp = CLIPMLP(config,
+                           quant_config=quant_config,
+                           prefix=f"{prefix}.mlp")
+        self.layer_norm2 = nn.LayerNorm(config.hidden_size,
+                                        eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states, _ = self.self_attn(hidden_states=hidden_states)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class CLIPEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self
+    attention layers. Each layer is a [`CLIPEncoderLayer`].
+
+    Args:
+        config: CLIPConfig
+    """
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        num_hidden_layers_override: Optional[int] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+
+        if num_hidden_layers_override is None:
+            num_hidden_layers = config.num_hidden_layers
+        else:
+            num_hidden_layers = num_hidden_layers_override
+        self.layers = nn.ModuleList([
+            CLIPEncoderLayer(config=config,
+                             quant_config=quant_config,
+                             prefix=f"{prefix}.layers.{layer_idx}")
+            for layer_idx in range(num_hidden_layers)
+        ])
+
+    def forward(
+        self, inputs_embeds: torch.Tensor, return_all_hidden_states: bool
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+        hidden_states_pool = [inputs_embeds]
+        hidden_states = inputs_embeds
+
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(hidden_states)
+            if return_all_hidden_states:
+                hidden_states_pool.append(hidden_states)
+        # If we have multiple feature sample layers, we return all hidden
+        # states in order and grab the ones we need by index.
+        if return_all_hidden_states:
+            return hidden_states_pool
+        return hidden_states
+
+
+class CLIPVisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        require_post_norm: Optional[bool] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        embed_dim = config.hidden_size
+
+        self.embeddings = CLIPVisionEmbeddings(config)
+
+        # NOTE: This typo of "layrnorm" is not fixed on purpose to match
+        # the original transformers code and name of the model weights.
+        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+
+        self.encoder = CLIPEncoder(
+            config=config,
+            quant_config=quant_config,
+            num_hidden_layers_override=num_hidden_layers_override,
+            prefix=f"{prefix}.encoder",
+        )
+
+        num_hidden_layers = config.num_hidden_layers
+        if len(self.encoder.layers) > config.num_hidden_layers:
+            raise ValueError(
+                f"The original encoder only has {num_hidden_layers} "
+                f"layers, but you requested {len(self.encoder.layers)} layers."
+            )
+
+        # If possible, skip post_layernorm to conserve memory
+        if require_post_norm is None:
+            require_post_norm = len(self.encoder.layers) == num_hidden_layers
+
+        if require_post_norm:
+            self.post_layernorm = nn.LayerNorm(embed_dim,
+                                               eps=config.layer_norm_eps)
+        else:
+            self.post_layernorm = None
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        feature_sample_layers: Optional[list[int]] = None,
+    ) -> torch.Tensor:
+
+        hidden_states = self.embeddings(pixel_values)
+        hidden_states = self.pre_layrnorm(hidden_states)
+
+        return_all_hidden_states = feature_sample_layers is not None
+
+        # Produces either the last layer output or all of the hidden states,
+        # depending on if we have feature_sample_layers or not
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            return_all_hidden_states=return_all_hidden_states)
+
+        # Handle post-norm (if applicable) and stacks feature layers if needed
+        encoder_outputs = resolve_visual_encoder_outputs(
+            encoder_outputs, feature_sample_layers, self.post_layernorm,
+            self.config.num_hidden_layers)
+
+        return encoder_outputs
+
+
+class CLIPVisionModel(nn.Module, SupportsQuant):
+    config_class = CLIPVisionConfig
+    main_input_name = "pixel_values"
+    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        require_post_norm: Optional[bool] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.vision_model = CLIPVisionTransformer(
+            config=config,
+            quant_config=quant_config,
+            num_hidden_layers_override=num_hidden_layers_override,
+            require_post_norm=require_post_norm,
+            prefix=f"{prefix}.vision_model")
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        feature_sample_layers: Optional[list[int]] = None,
+    ) -> torch.Tensor:
+        return self.vision_model(pixel_values, feature_sample_layers)
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    # (TODO) Add prefix argument for filtering out weights to be loaded
+    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        layer_count = len(self.vision_model.encoder.layers)
+
+        for name, loaded_weight in weights:
+            # post_layernorm is not needed in CLIPVisionModel
+            if (name.startswith("vision_model.post_layernorm")
+                    and self.vision_model.post_layernorm is None):
+                continue
+
+            # omit layers when num_hidden_layers_override is set
+            if name.startswith("vision_model.encoder.layers"):
+                layer_idx = int(name.split(".")[3])
+                if layer_idx >= layer_count:
+                    continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/cohere2_vision.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/cohere2_vision.py
new file mode 100644
index 0000000000000000000000000000000000000000..f17583768f7959be33f82199fad56ade4d38e6c4
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/cohere2_vision.py
@@ -0,0 +1,445 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Adapted from vllm/model_executor/models/aya_vision.py
+"""Command-A-Vision (Cohere2Vision) multimodal model implementation for vLLM."""
+
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Literal, Optional, Union
+
+import torch
+from torch import nn
+from transformers import BatchFeature, PretrainedConfig
+from transformers.models.cohere2_vision import Cohere2VisionConfig
+from transformers.models.cohere2_vision.processing_cohere2_vision import (
+    Cohere2VisionProcessor)
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.activation import MulAndSilu
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.awq import AWQConfig
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalDataDict, MultiModalKwargs
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+                                   MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo,
+                                        MultiModalFieldConfig,
+                                        PromptReplacement, PromptUpdate,
+                                        PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .siglip import SiglipVisionModel
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+
+class Cohere2VisionImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - np: The total number of patches over each image over each prompt in
+              the batch
+        - c: Number of channels
+        - h: Height of each image patch
+        - w: Width of each image patch
+        - bn: Batch size * number of images
+    """
+
+    type: Literal["pixel_values"]
+
+    pixel_values: Annotated[
+        torch.Tensor,
+        TensorShape("np", 3, "h", "w"),
+    ]
+
+    num_patches: Annotated[
+        torch.Tensor,
+        TensorShape("bn"),
+    ]
+
+
+class Cohere2VisionMultiModalProjector(nn.Module):
+    """Multimodal projector that maps vision features to text embedding space.
+    
+    Uses pixel shuffle downsampling followed by SwiGLU activation.
+    """
+
+    def __init__(self, config: Cohere2VisionConfig, prefix: str = ""):
+        super().__init__()
+        self.downsample_factor = config.downsample_factor
+
+        # Input dimension after pixel shuffle downsampling
+        input_dim = config.vision_config.hidden_size * (
+            config.downsample_factor**2)
+        # MergedColumnParallelLinear expects the intermediate size to be a list
+        # of sizes, so that it will load the weights as two separate linear
+        # layers before applying any parallelism.
+        # We need to divide the alignment intermediate size by 2 because
+        # the weights are merged weights of two linear layers for SwiGLU.
+        self.intermediate_size = config.alignment_intermediate_size // 2
+
+        self.linear_1 = MergedColumnParallelLinear(
+            input_dim,
+            [self.intermediate_size] * 2,
+            bias=True,
+            return_bias=False,
+            prefix=f"{prefix}.linear_1",
+        )
+        self.act = MulAndSilu()
+        self.linear_2 = RowParallelLinear(
+            self.intermediate_size,
+            config.text_config.hidden_size,
+            bias=True,
+            return_bias=False,
+            prefix=f"{prefix}.linear_2",
+        )
+
+    def forward(self, image_features):
+        image_features = self.pixel_shuffle(image_features)
+        hidden_states = self.linear_1(image_features)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+    def pixel_shuffle(self, image_features: torch.Tensor) -> torch.Tensor:
+        """Apply pixel shuffle downsampling to reduce spatial dimensions.
+        
+        Args:
+            image_features: Input tensor of shape [B, S, D] where S = H*W
+            
+        Returns:
+            Downsampled tensor with increased channel dimension
+        """
+        height = width = int(image_features.shape[1]**0.5)
+        x = image_features.reshape(image_features.shape[0], width, height, -1)
+        n, h, w, c = x.size()
+        scale_factor = 1. / self.downsample_factor
+        nh = int(h * scale_factor)
+        nw = int(w * scale_factor)
+        x = x.reshape(n, nh, self.downsample_factor, nw,
+                      self.downsample_factor, c)
+        x = x.permute(0, 1, 3, 2, 4, 5).contiguous()
+        x = x.reshape(n, nh, nw, -1)
+        return x
+
+
+class Cohere2VisionProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self) -> Cohere2VisionConfig:
+        return self.ctx.get_hf_config(Cohere2VisionConfig)
+
+    def get_hf_processor(self, **kwargs: object) -> Cohere2VisionProcessor:
+        return self.ctx.get_hf_processor(Cohere2VisionProcessor, **kwargs)
+
+    def get_image_processor(self, **kwargs: object):
+        return self.get_hf_processor(**kwargs).image_processor
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        image_processor = self.get_image_processor()
+        height = image_processor.size['height']
+        width = image_processor.size['width']
+        max_patches = image_processor.max_patches
+        return ImageSize(height=height * max_patches, width=width)
+
+    def get_num_patches(self, image_width: int, image_height: int) -> int:
+        """
+        Calculate the number of image patches for a given image.
+        Uses the HF processor to determine the actual number of patches.
+        """
+        return self.get_hf_processor(
+        ).image_processor.get_number_of_image_patches(image_height,
+                                                      image_width, {})
+
+
+class Cohere2VisionDummyInputsBuilder(
+        BaseDummyInputsBuilder[Cohere2VisionProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        processor = self.info.get_hf_processor()
+        image_token = processor.image_token
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        image_size = \
+            self.info.get_image_size_with_most_features()
+
+        return {
+            "image":
+            self._get_dummy_images(width=image_size.width,
+                                   height=image_size.height,
+                                   num_images=num_images)
+        }
+
+
+class Cohere2VisionMultiModalProcessor(
+        BaseMultiModalProcessor[Cohere2VisionProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        processed_outputs = super()._call_hf_processor(
+            prompt,
+            mm_data,
+            mm_kwargs,
+            tok_kwargs,
+        )
+
+        # Ensure num_patches is available for proper tensor splitting
+        if "num_patches" not in processed_outputs and (
+                images := mm_data.get("images")) is not None:
+            # Fallback calculation if HF processor didn't provide num_patches
+            parsed_images = self._get_data_parser().parse_mm_data({
+                "image":
+                images
+            }).get_items("image", ImageProcessorItems)
+
+            num_patches = [
+                self.info.get_num_patches(
+                    image_width=parsed_images.get_image_size(i).width,
+                    image_height=parsed_images.get_image_size(i).height)
+                for i in range(len(parsed_images))
+            ]
+            processed_outputs["num_patches"] = torch.tensor(num_patches)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        num_patches = hf_inputs.get("num_patches", torch.empty(0))
+        return dict(
+            pixel_values=MultiModalFieldConfig.flat_from_sizes(
+                "image", num_patches),
+            num_patches=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_token = hf_processor.image_token
+        img_line_break_token = hf_processor.img_line_break_token
+        boi_token = hf_processor.boi_token
+        eoi_token = hf_processor.eoi_token
+
+        def get_replacement(item_idx: int):
+            images: ImageProcessorItems = mm_items.get("image",
+                                                       ImageProcessorItems)
+            image_size: ImageSize = images.get_image_size(item_idx)
+
+            num_patches = self.info.get_num_patches(image_size.height,
+                                                    image_size.width)
+            img_tokens_per_tile = int(hf_processor.patch_size**2)
+            single_tile_tokens = image_token * img_tokens_per_tile + \
+                img_line_break_token
+            img_string = f"{boi_token}\
+                {single_tile_tokens * num_patches}\
+                {eoi_token}"
+
+            return PromptUpdateDetails.select_text(img_string, image_token)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=image_token,
+                replacement=get_replacement,
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Cohere2VisionMultiModalProcessor,
+    info=Cohere2VisionProcessingInfo,
+    dummy_inputs=Cohere2VisionDummyInputsBuilder)
+class Cohere2VisionForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                            SupportsPP):
+
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "model.vision_tower.": "vision_tower.",
+            "model.multi_modal_projector.": "multi_modal_projector.",
+            "model.language_model.": "language_model.model.",
+            "lm_head.": "language_model.lm_head.",
+        })
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config: Cohere2VisionConfig = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.quant_config = quant_config
+        self.multimodal_config = multimodal_config
+        self._patch_quant_config(config, quant_config)
+
+        self.vision_tower = SiglipVisionModel(config.vision_config,
+                                              quant_config,
+                                              prefix=maybe_prefix(
+                                                  prefix, "vision_tower"))
+        self.vocab_size = config.text_config.vocab_size
+        self.multi_modal_projector = \
+            Cohere2VisionMultiModalProjector(
+                config, prefix=maybe_prefix(prefix, "multi_modal_projector"))
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=["Cohere2ForCausalLM"])
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def _process_image_input(self, image_input: Cohere2VisionImagePixelInputs,
+                             **kwargs) -> list[torch.Tensor]:
+        """Process image pixels through vision tower and projector.
+        
+        Args:
+            image_input: Validated image input containing pixel values and 
+                         patch counts
+            
+        Returns:
+            List of flattened image embeddings, one per image
+        """
+        assert self.vision_tower is not None, "Vision tower is required"
+
+        pixel_values = image_input["pixel_values"]
+        num_patches = image_input["num_patches"]
+
+        # Extract visual features
+        image_features = self.vision_tower(pixel_values)
+
+        # Project to text embedding space
+        image_embeds = self.multi_modal_projector(image_features)
+
+        # Split and flatten embeddings per image
+        return [
+            e.flatten(0, 2) for e in image_embeds.split(num_patches.tolist())
+        ]
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Cohere2VisionImagePixelInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        num_patches = kwargs.pop("num_patches", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        assert image_embeds is None, \
+            "Cohere2Vision does not support image_embeds."
+
+        if pixel_values is None:
+            return None
+
+        return Cohere2VisionImagePixelInputs(
+            type="pixel_values",
+            pixel_values=flatten_bn(pixel_values, concat=True),
+            num_patches=flatten_bn(num_patches, concat=True),
+            resolve_bindings={
+                "h": self.config.vision_config.image_size,
+                "w": self.config.vision_config.image_size,
+            })
+
+    def _patch_quant_config(self, config: PretrainedConfig,
+                            quant_config: QuantizationConfig):
+        # the awq models from OpenGVLab missing `modules_to_not_convert`
+        # patch the quant_config to add `modules_to_not_convert` back
+        if isinstance(quant_config, AWQConfig):
+            text_config = config.text_config
+            llm_quant_config = getattr(text_config, "quantization_config",
+                                       None)
+            if (not quant_config.modules_to_not_convert) and (llm_quant_config
+                                                              is not None):
+                quant_config.modules_to_not_convert.append("vision_tower")
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+
+        return self._process_image_input(image_input, **kwargs)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids=input_ids,
+                inputs_embeds=inputs_embeds,
+                multimodal_embeddings=multimodal_embeddings,
+                placeholder_token_id=self.config.image_token_id,
+            )
+
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/config.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..882df7e8162c56e7bcd399903c1fa9f8c0e0ddd5
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/config.py
@@ -0,0 +1,377 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from copy import deepcopy
+from typing import TYPE_CHECKING
+
+import vllm.envs as envs
+from vllm.logger import init_logger
+from vllm.model_executor.models import ModelRegistry
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, cdiv
+from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec
+
+if TYPE_CHECKING:
+
+    from vllm.config import VllmConfig
+
+logger = init_logger(__name__)
+
+
+class VerifyAndUpdateConfig:
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        raise NotImplementedError
+
+
+class GteNewModelConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config.hf_config
+
+        assert config.__class__.__name__ == "NewConfig"
+        assert config.hidden_act == "gelu"
+
+        config.hidden_act = "geglu"
+
+        head_dim = config.hidden_size // config.num_attention_heads
+        config.rotary_kwargs = {
+            "head_size": head_dim,
+            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
+            "max_position": config.max_position_embeddings,
+            "base": config.rope_theta,
+            "rope_scaling": getattr(config, "rope_scaling", None)
+        }
+
+
+class JambaForSequenceClassificationConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        pooler_config = vllm_config.model_config.pooler_config
+        if pooler_config.activation is None:
+            pooler_config.activation = False
+
+
+class JinaRobertaModelConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config.hf_config
+
+        if config.position_embedding_type == "rotary":
+            assert config.__class__.__name__ == "XLMRobertaFlashConfig"
+
+            head_dim = config.hidden_size // config.num_attention_heads
+            config.rotary_kwargs = {
+                "head_size": head_dim,
+                "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
+                "max_position": config.max_position_embeddings,
+                "base": getattr(config, "rope_theta", config.rotary_emb_base),
+                "rope_scaling": getattr(config, "rope_scaling", None)
+            }
+
+
+class NomicBertModelConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config.hf_config
+
+        assert config.__class__.__name__ == "NomicBertConfig"
+        assert config.activation_function in ["swiglu", "gelu"]
+        config.position_embedding_type = getattr(config,
+                                                 "position_embedding_type",
+                                                 "rope")
+
+        if config.activation_function == "swiglu":
+            config.hidden_act = "silu"
+        else:
+            config.hidden_act = config.activation_function
+
+        assert (config.mlp_fc1_bias == config.mlp_fc2_bias ==
+                config.qkv_proj_bias)
+        config.bias = config.qkv_proj_bias
+
+        assert config.rotary_emb_scale_base is None
+        assert not config.rotary_emb_interleaved
+
+        config.layer_norm_eps = config.layer_norm_epsilon
+        config.intermediate_size = config.n_inner
+        config.hidden_size = config.n_embd
+        config.num_hidden_layers = config.n_layer
+
+        head_dim = config.hidden_size // config.num_attention_heads
+        rotary_emb_dim = int(head_dim * config.rotary_emb_fraction)
+        max_trained_positions = getattr(config, "max_trained_positions", 2048)
+        config.rotary_kwargs = {
+            "head_size": head_dim,
+            "rotary_dim": rotary_emb_dim,
+            "max_position": max_trained_positions,
+            "base": getattr(config, "rope_theta", config.rotary_emb_base),
+            "rope_scaling": getattr(config, "rope_scaling", None)
+        }
+
+        # we ignore config.rotary_scaling_factor so that for datasets shorter
+        # than max_trained_positions 2048, the results are consistent
+        # with SentenceTransformer.
+        # The context extension uses vllm style rope_theta and rope_scaling.
+        # See #17785 #18755
+        if (not vllm_config.model_config.hf_overrides
+                and vllm_config.model_config.original_max_model_len is None):
+            # Default
+            # Reset max_model_len to max_trained_positions.
+            # nomic-embed-text-v2-moe the length is set to 512
+            # by sentence_bert_config.json.
+            max_model_len_before = vllm_config.model_config.max_model_len
+            max_model_len = min(vllm_config.model_config.max_model_len,
+                                max_trained_positions)
+
+            vllm_config.recalculate_max_model_len(max_model_len)
+            logger.warning(
+                "Nomic context extension is disabled. "
+                "Changing max_model_len from %s to %s. "
+                "To enable context extension, see: "
+                "https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/context_extension.html",
+                max_model_len_before, vllm_config.model_config.max_model_len)
+        else:
+            # We need to re-verify max_model_len to avoid lengths
+            # greater than position_embedding.
+            model_config = vllm_config.model_config
+            hf_text_config = model_config.hf_text_config
+
+            if isinstance(model_config.hf_overrides, dict):
+                # hf_overrides_kw
+                max_model_len = model_config.hf_overrides.get(
+                    "max_model_len", vllm_config.model_config.max_model_len)
+            else:
+                # hf_overrides_fn
+                # This might be overridden by sentence_bert_config.json.
+                max_model_len = vllm_config.model_config.max_model_len
+
+            # reset hf_text_config for recalculate_max_model_len.
+            if hasattr(hf_text_config, "max_model_len"):
+                delattr(hf_text_config, "max_model_len")
+            hf_text_config.max_position_embeddings = max_trained_positions
+            hf_text_config.rope_scaling = config.rotary_kwargs["rope_scaling"]
+
+            # The priority of sentence_bert_config.json is higher
+            # than max_position_embeddings
+            encoder_config = deepcopy(model_config.encoder_config)
+            encoder_config.pop("max_seq_length", None)
+            model_config.encoder_config = encoder_config
+
+            vllm_config.recalculate_max_model_len(max_model_len)
+
+
+class Qwen2ForProcessRewardModelConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        pooler_config = vllm_config.model_config.pooler_config
+
+        if pooler_config.step_tag_id is None:
+            pooler_config.step_tag_id = 151651
+
+
+class Qwen2ForRewardModelConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        pooler_config = vllm_config.model_config.pooler_config
+
+        if pooler_config.softmax is None:
+            pooler_config.softmax = False
+
+
+class Qwen3ForSequenceClassificationConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config.hf_config
+
+        is_original_qwen3_reranker = getattr(config,
+                                             "is_original_qwen3_reranker",
+                                             False)
+
+        if not is_original_qwen3_reranker:
+            return
+
+        tokens = getattr(config, "classifier_from_token", None)
+        assert tokens is not None and len(tokens) == 2, \
+            ("Try loading the original Qwen3 Reranker?, see: "
+             "https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen3_reranker.py")
+        vllm_config.model_config.hf_config.method = "from_2_way_softmax"
+
+
+class JinaVLForSequenceClassificationConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config.hf_config
+
+        config.num_labels = 1
+
+
+class SnowflakeGteNewModelConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config.hf_config
+
+        assert config.__class__.__name__ == "GteConfig"
+        assert config.hidden_act == "gelu"
+
+        config.hidden_act = "geglu"
+
+        head_dim = config.hidden_size // config.num_attention_heads
+        config.rotary_kwargs = {
+            "head_size": head_dim,
+            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
+            "max_position": config.max_position_embeddings,
+            "base": config.rope_theta,
+            "rope_scaling": getattr(config, "rope_scaling", None)
+        }
+
+
+class GraniteMoeHybridModelConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        config = vllm_config.model_config
+        config.max_seq_len_to_capture = config.max_model_len
+        logger.info(
+            "Setting max_seq_len_to_capture to %d "
+            "to ensure that CUDA graph capture "
+            "covers sequences of length up to max_model_len.",
+            config.max_model_len)
+
+
+class GptOssForCausalLMConfig(VerifyAndUpdateConfig):
+
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        decoding_config = vllm_config.decoding_config
+        if decoding_config.reasoning_backend == "":
+            decoding_config.reasoning_backend = "GptOss"
+
+        # Increase the max capture size from 512 to 1024 for performance.
+        # NOTE(woosuk): This will increase the number of CUDA graphs
+        # from 67 to 83.
+        scheduler_config = vllm_config.scheduler_config
+        if len(scheduler_config.cuda_graph_sizes) == 1:
+            max_capture_size = scheduler_config.cuda_graph_sizes[0]
+            # FIXME(woosuk): When using full cuda graph with FA3, the max
+            # supported size is 992.
+            if max_capture_size < 1024:
+                cuda_graph_sizes = [1, 2, 4]
+                # Step size 8 for small batch sizes
+                cuda_graph_sizes += [i for i in range(8, 256, 8)]
+                # Step size 16 for larger batch sizes
+                cuda_graph_sizes += [i for i in range(256, 1025, 16)]
+                scheduler_config.cuda_graph_sizes = cuda_graph_sizes
+                logger.info(
+                    "Overriding max cuda graph capture size to "
+                    "%d for performance.", 1024)
+
+
+class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
+
+    @classmethod
+    def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None:
+        """
+        Ensure that page size of attention layers is greater than or
+        equal to the mamba layers. If not, automatically set the attention
+        block size to ensure that it is. If the attention page size is
+        strictly greater than the mamba page size, we pad the mamba page size
+        to make them equal.
+
+        Args:
+            vllm_config: vLLM Config
+        """
+
+        if not envs.VLLM_USE_V1:
+            return
+
+        cache_config = vllm_config.cache_config
+        model_config = vllm_config.model_config
+        parallel_config = vllm_config.parallel_config
+
+        if cache_config.cache_dtype == "auto":
+            kv_cache_dtype = model_config.dtype
+        else:
+            kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
+
+        # get attention page size (for 1 token)
+        attn_page_size_1_token = FullAttentionSpec(
+            block_size=1,
+            num_kv_heads=model_config.get_num_kv_heads(parallel_config),
+            head_size=model_config.get_head_size(),
+            dtype=kv_cache_dtype,
+            use_mla=model_config.use_mla).page_size_bytes
+
+        model_cls, _ = ModelRegistry.resolve_model_cls(
+            model_config.architecture,
+            model_config=model_config,
+        )
+
+        # get mamba page size
+        mamba_page_size = MambaSpec(
+            shapes=model_cls.get_mamba_state_shape_from_config(vllm_config),
+            dtypes=model_cls.get_mamba_state_dtype_from_config(vllm_config),
+            block_size=model_config.max_model_len,
+        ).page_size_bytes
+
+        # some attention backends (e.g. FA) only support setting
+        # block size to multiple of 16, so let's suggest a value
+        # that would work (note: FA is currently not compatible
+        # with mamba layers, use FlashInfer instead).
+        attn_block_size = 16 * cdiv(mamba_page_size,
+                                    16 * attn_page_size_1_token)
+
+        # override attention block size if either (a) the
+        # user has not set it or (b) the user has set it
+        # too small.
+        if (cache_config.block_size is None
+                or cache_config.block_size < attn_block_size):
+            cache_config.block_size = attn_block_size
+            logger.info(
+                "Setting attention block size to %d tokens "
+                "to ensure that attention page size is >= mamba page size.",
+                attn_block_size)
+
+        # compute new attention page size
+        attn_page_size = \
+            cache_config.block_size * attn_page_size_1_token
+
+        assert attn_page_size >= mamba_page_size
+
+        if attn_page_size == mamba_page_size:
+            # don't need to pad mamba page size
+            return
+
+        # pad mamba page size to exactly match attention
+        if (cache_config.mamba_page_size_padded is None
+                or cache_config.mamba_page_size_padded != attn_page_size):
+            cache_config.mamba_page_size_padded = (attn_page_size)
+            mamba_padding_pct = 100 * (attn_page_size -
+                                       mamba_page_size) / mamba_page_size
+            logger.info(
+                "Padding mamba page size by %.2f%% to ensure "
+                "that mamba page size and attention page size are "
+                "exactly equal.", mamba_padding_pct)
+
+
+MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = {
+    "GteModel": SnowflakeGteNewModelConfig,
+    "GteNewModel": GteNewModelConfig,
+    "NomicBertModel": NomicBertModelConfig,
+    "Qwen2ForProcessRewardModel": Qwen2ForProcessRewardModelConfig,
+    "Qwen2ForRewardModel": Qwen2ForRewardModelConfig,
+    "Qwen3ForSequenceClassification": Qwen3ForSequenceClassificationConfig,
+    "XLMRobertaModel": JinaRobertaModelConfig,
+    "JinaVLForRanking": JinaVLForSequenceClassificationConfig,
+    "JambaForSequenceClassification": JambaForSequenceClassificationConfig,
+    "GraniteMoeHybridForCausalLM": GraniteMoeHybridModelConfig,
+    "GptOssForCausalLM": GptOssForCausalLMConfig,
+}
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/constant_size_cache.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/constant_size_cache.py
new file mode 100644
index 0000000000000000000000000000000000000000..f03c58a12932faba1207a76d7f76e8fdd95bf331
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/constant_size_cache.py
@@ -0,0 +1,137 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from abc import ABC, abstractmethod
+from typing import Any
+
+import torch
+
+from vllm.attention.backends.utils import PAD_SLOT_ID
+
+
+class ConstantSizeCache(ABC):
+    """
+    Abstract base class for managing constant size caches 
+    like Mamba and Minimax.
+    """
+
+    def __init__(self, max_batch_size: int):
+        # Maps between the request id and a dict that maps between the seq_id
+        # and its index inside the cache
+        self.cache_indices_mapping: dict[str, dict[int, int]] = {}
+        self.free_cache_indices = list(range(max_batch_size))
+
+    @property
+    @abstractmethod
+    def cache(self) -> Any:
+        """Return the underlying cache tensor(s)"""
+        pass
+
+    @abstractmethod
+    def _copy_cache(self, from_index: int, to_index: int):
+        """Copy cache data from one index to another"""
+        pass
+
+    def current_run_tensors(self, **kwargs) -> tuple:
+        """
+        Return the tensors for the current run's conv and ssm state.
+        """
+        if "seqlen_agnostic_capture_inputs" not in kwargs:
+            # We get here only on Prefill/Eager mode runs
+            request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"]
+            finished_requests_ids = kwargs["finished_requests_ids"]
+
+            self._release_finished_requests(finished_requests_ids)
+            state_indices = self._prepare_current_run_cache(
+                request_ids_to_seq_ids, finished_requests_ids)
+
+            state_indices_tensor = torch.as_tensor(state_indices,
+                                                   dtype=torch.int32,
+                                                   device="cuda")
+            cache_tensors = self.cache
+        else:
+            # CUDA graph capturing runs
+            cache_tensors, state_indices_tensor = kwargs[
+                "seqlen_agnostic_capture_inputs"]
+
+        return (cache_tensors, state_indices_tensor)
+
+    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
+        """
+        Copy the relevant state_indices into the CUDA graph input buffer 
+        """
+        assert all(
+            key in kwargs
+            for key in ["request_ids_to_seq_ids", "finished_requests_ids"])
+        finished_requests_ids = kwargs["finished_requests_ids"]
+        request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"]
+        assert "seqlen_agnostic_capture_inputs" in input_buffers
+        _, input_state_indices_buffer = input_buffers[
+            "seqlen_agnostic_capture_inputs"]
+
+        self._release_finished_requests(finished_requests_ids)
+        state_indices = self._prepare_current_run_cache(
+            request_ids_to_seq_ids, finished_requests_ids)
+        cuda_graph_pad_len = input_state_indices_buffer.shape[0] - len(
+            state_indices)
+        state_indices.extend([PAD_SLOT_ID] * cuda_graph_pad_len)
+
+        input_state_indices_buffer.copy_(
+            torch.as_tensor(state_indices, dtype=torch.int32, device="cuda"))
+
+    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
+        """
+        Provide the CUDA graph capture runs with a buffer in adjusted size.
+        The buffer is used to maintain the Cache during the CUDA graph replay
+        runs.
+        """
+        state_indices_tensor = torch.as_tensor([PAD_SLOT_ID] * batch_size,
+                                               dtype=torch.int32,
+                                               device="cuda")
+        return (self.cache, state_indices_tensor)
+
+    def _assign_seq_id_to_cache_index(self, cur_rid: str, seq_id: int,
+                                      finished_requests_ids) -> int:
+        """
+        Assign (req_id,seq_id) pair to a `destination_index` index, if
+        already occupied, move the occupying index to a free index.
+        """
+        if cur_rid in finished_requests_ids:
+            # set as pad, do not allocate destination index
+            return PAD_SLOT_ID
+        elif cur_rid not in self.cache_indices_mapping:
+            destination_index = self.free_cache_indices.pop()
+            self.cache_indices_mapping[cur_rid] = {seq_id: destination_index}
+            return destination_index
+        elif seq_id not in (seq_ids2indices :=
+                            self.cache_indices_mapping[cur_rid]):
+            # parallel sampling , where n > 1, assume prefill have
+            # already happened, so we copy the
+            # existing cache into the siblings seq_ids caches
+            index_exists = next(iter(seq_ids2indices.values()))
+            # case of decoding n>1, copy prefill cache to decoding indices
+            destination_index = self.free_cache_indices.pop()
+            self._copy_cache(from_index=index_exists,
+                             to_index=destination_index)
+            self.cache_indices_mapping[cur_rid][seq_id] = destination_index
+            return destination_index
+        else:
+            return self.cache_indices_mapping[cur_rid][seq_id]
+
+    def _prepare_current_run_cache(
+            self, request_ids_to_seq_ids: dict[str, list[int]],
+            finished_requests_ids: list[str]) -> list[int]:
+        return [
+            self._assign_seq_id_to_cache_index(req_id, seq_id,
+                                               finished_requests_ids)
+            for req_id, seq_ids in request_ids_to_seq_ids.items()
+            for seq_id in seq_ids
+        ]
+
+    def _release_finished_requests(self,
+                                   finished_seq_groups_req_ids: list[str]):
+        for req_id in finished_seq_groups_req_ids:
+            if req_id in self.cache_indices_mapping:
+                for seq_id in self.cache_indices_mapping[req_id]:
+                    self.free_cache_indices.append(
+                        self.cache_indices_mapping[req_id][seq_id])
+                self.cache_indices_mapping.pop(req_id)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/dbrx.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/dbrx.py
new file mode 100644
index 0000000000000000000000000000000000000000..e74d90e0b1d7dd05811dd661b199009f7ea1a495
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/dbrx.py
@@ -0,0 +1,472 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+from transformers import DbrxConfig
+
+from vllm.attention import Attention
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class DbrxRouter(nn.Module):
+    """A Router implementation for DBRX that returns logits for each expert
+    per token.
+    """
+
+    def __init__(
+        self,
+        config: DbrxConfig,
+        params_dtype: Optional[torch.dtype] = None,
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_total_experts = config.ffn_config.moe_num_experts
+        self.d_model = config.d_model
+        self.layer = ReplicatedLinear(
+            self.d_model,
+            self.num_total_experts,
+            bias=False,
+            params_dtype=params_dtype,
+            quant_config=None,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        router_logits, _ = self.layer(hidden_states)
+        return router_logits
+
+
+class DbrxExperts(FusedMoE):
+
+    def __init__(
+        self,
+        config: DbrxConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        params_dtype: Optional[torch.dtype] = None,
+        prefix: str = "",
+    ):
+        super().__init__(
+            num_experts=config.ffn_config.moe_num_experts,
+            top_k=config.ffn_config.moe_top_k,
+            hidden_size=config.d_model,
+            intermediate_size=config.ffn_config.ffn_hidden_size,
+            params_dtype=params_dtype,
+            reduce_results=True,
+            renormalize=True,
+            quant_config=quant_config,
+            tp_size=get_tensor_model_parallel_world_size(),
+            prefix=prefix,
+        )
+        self.config = config
+        self.d_model = config.d_model
+        self.intermediate_size = (self.config.ffn_config.ffn_hidden_size //
+                                  self.tp_size)
+
+    # Define custom weight loader for dbrx model
+    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor,
+                      weight_name: str, param_name: str):
+        tp_rank = get_tensor_model_parallel_rank()
+        param_data = param.data
+        shard_size = self.intermediate_size
+        shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
+        # DBRX uses GLU for each experts.
+        # GLU has 3 linear layers: w1, v1 and w2.
+        if weight_name.endswith("w1"):
+            if param_name.endswith("weight"):
+                loaded_weight = torch.reshape(
+                    loaded_weight,
+                    [-1, self.intermediate_size * self.tp_size, self.d_model],
+                )
+                param_data[:, 0:shard_size, :] = loaded_weight[:, shard, :]
+            elif param_name.endswith("weight_scale"):
+                param_data[:, 0] = loaded_weight
+            else:
+                param_data = loaded_weight
+        if weight_name.endswith("v1"):
+            if param_name.endswith("weight"):
+                loaded_weight = torch.reshape(
+                    loaded_weight,
+                    [-1, self.intermediate_size * self.tp_size, self.d_model],
+                )
+                param_data[:, shard_size:2 *
+                           shard_size, :] = loaded_weight[:, shard, :]
+            elif param_name.endswith("weight_scale"):
+                param_data[:, 1] = loaded_weight
+            else:
+                param_data[:] = loaded_weight
+        if weight_name.endswith("w2"):
+            if param_name.endswith("weight"):
+                loaded_weight = torch.reshape(
+                    loaded_weight,
+                    [-1, self.intermediate_size * self.tp_size, self.d_model],
+                ).transpose(1, 2)
+                param_data[:] = loaded_weight[:, :, shard]
+            else:
+                param_data[:] = loaded_weight
+
+
+class DbrxMoE(nn.Module):
+    """A tensor-parallel MoE implementation for DBRX.
+
+    Each expert's weights are sharded across all ranks and a fused MoE
+    kernel is used for the forward pass, and finally we reduce the outputs
+    across ranks.
+    """
+
+    def __init__(
+        self,
+        config: DbrxConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        params_dtype: Optional[torch.dtype] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.d_model = config.d_model
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        self.params_dtype = params_dtype
+
+        self.router = DbrxRouter(config, self.params_dtype)
+
+        self.experts = DbrxExperts(config=config,
+                                   quant_config=quant_config,
+                                   params_dtype=self.params_dtype,
+                                   prefix=f"{prefix}.experts")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        orig_shape = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.d_model)
+        # router_logits: (num_tokens, n_experts)
+        router_logits = self.router(hidden_states)
+        final_hidden_states = self.experts(hidden_states, router_logits)
+        return final_hidden_states.view(orig_shape)
+
+
+class DbrxAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: DbrxConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.d_model = config.d_model
+        self.total_num_heads = config.n_heads
+        self.head_dim = self.d_model // self.total_num_heads
+        self.total_num_kv_heads = config.attn_config.kv_n_heads
+        self.clip_qkv = config.attn_config.clip_qkv
+        self.rope_theta = config.attn_config.rope_theta
+        self.max_position = config.max_seq_len
+
+        # pylint: disable=invalid-name
+        self.Wqkv = QKVParallelLinear(
+            self.d_model,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.out_proj = RowParallelLinear(
+            self.d_model,
+            self.d_model,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+        )
+
+        tp_world_size = get_tensor_model_parallel_world_size()
+        self.tp_size = tp_world_size
+        assert self.total_num_heads % tp_world_size == 0
+        self.num_heads = self.total_num_heads // tp_world_size
+        if self.total_num_kv_heads >= tp_world_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_world_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_world_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_world_size)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.Wqkv(hidden_states)
+        if self.clip_qkv is not None:
+            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(position_ids, q, k)
+        attn_output = self.attn(q, k, v)
+        hidden_states, _ = self.out_proj(attn_output)
+        return hidden_states
+
+
+class DbrxFusedNormAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: DbrxConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.d_model = config.d_model
+        self.attn = DbrxAttention(config,
+                                  cache_config,
+                                  quant_config,
+                                  prefix=f"{prefix}.attn")
+        self.norm_1 = nn.LayerNorm(self.d_model)
+        self.norm_2 = nn.LayerNorm(self.d_model)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.norm_1(hidden_states)
+        x = self.attn(
+            position_ids=position_ids,
+            hidden_states=hidden_states,
+        )
+        hidden_states = residual + x
+        residual = hidden_states
+        hidden_states = self.norm_2(hidden_states)
+        return hidden_states, residual
+
+
+class DbrxBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: DbrxConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.norm_attn_norm = DbrxFusedNormAttention(
+            config,
+            cache_config,
+            quant_config,
+            prefix=f"{prefix}.norm_attn_norm")
+        self.ffn = DbrxMoE(config, quant_config, prefix=f"{prefix}.ffn")
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        hidden_states, residual = self.norm_attn_norm(
+            position_ids=position_ids,
+            hidden_states=hidden_states,
+        )
+        hidden_states = self.ffn(hidden_states)
+        hidden_states = hidden_states + residual
+        return hidden_states
+
+
+class DbrxModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.quant_config = quant_config
+        self.wte = VocabParallelEmbedding(
+            config.vocab_size,
+            config.d_model,
+        )
+        self.start_layer, self.end_layer, self.blocks = make_layers(
+            config.n_layers,
+            lambda prefix: DbrxBlock(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.blocks",
+        )
+        self.norm_f = nn.LayerNorm(config.d_model, eps=1e-5)
+        for module in self.modules():
+            if hasattr(module, "bias") and isinstance(module.bias,
+                                                      nn.Parameter):
+                # Remove the bias term in Linear and LayerNorm.
+                module.register_parameter("bias", None)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.d_model))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.wte(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+        else:
+            assert intermediate_tensors
+            hidden_states = intermediate_tensors["hidden_states"]
+        for block in self.blocks[self.start_layer:self.end_layer]:
+            hidden_states = block(position_ids, hidden_states)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+        hidden_states = self.norm_f(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        expert_params_mapping = [(
+            "w13" if weight_name in ["w1", "v1"] else "w2",
+            f"mlp.{weight_name}",
+        ) for weight_name in ["w1", "v1", "w2"]]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            if name.endswith(("w1", "w2", "v1")):
+                name = name + "_weight"
+            for param_name, weight_name in expert_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, weight_name, name)
+                break
+
+            else:
+                if is_pp_missing_parameter(name, self):
+                    continue
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class DbrxForCausalLM(nn.Module, SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        if config.tie_word_embeddings:
+            raise ValueError(
+                "tie_word_embeddings is not supported for Dbrx models.")
+        self.quant_config = quant_config
+        self.unpadded_vocab_size = config.vocab_size
+        self.transformer = DbrxModel(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(
+                                         prefix, "transformer"))
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.d_model,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+            quant_config=quant_config,
+        )
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.transformer.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.transformer(input_ids, positions,
+                                         intermediate_tensors, inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/deepseek.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/deepseek.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f0202f1e038d22a01f56637e15f1a10fbe5aa7f
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/deepseek.py
@@ -0,0 +1,486 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Deepseek model."""
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.attention import Attention
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import fused_moe
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .utils import (AutoWeightsLoader, extract_layer_index,
+                    is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class DeepseekMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config,
+                                           reduce_results=reduce_results)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class DeepseekMoE(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.rank = get_tensor_model_parallel_rank()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.n_routed_experts = config.n_routed_experts
+        self.top_k = config.num_experts_per_tok
+        if self.tp_size > self.n_routed_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {self.n_routed_experts}.")
+
+        self.experts = nn.ModuleList([
+            DeepseekMLP(hidden_size=config.hidden_size,
+                        intermediate_size=config.moe_intermediate_size,
+                        hidden_act=config.hidden_act,
+                        quant_config=quant_config,
+                        reduce_results=False)
+            for idx in range(self.n_routed_experts)
+        ])
+        self.pack_params()
+
+        self.gate = ReplicatedLinear(config.hidden_size,
+                                     self.n_routed_experts,
+                                     bias=False,
+                                     quant_config=None)
+
+        if config.n_shared_experts is not None:
+            intermediate_size = (config.moe_intermediate_size *
+                                 config.n_shared_experts)
+            self.shared_experts = DeepseekMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,
+            )
+
+    def pack_params(self):
+        w1 = []
+        w2 = []
+        for expert in self.experts:
+            w1.append(expert.gate_up_proj.weight)
+            w2.append(expert.down_proj.weight)
+        self.w1 = torch._utils._flatten_dense_tensors(w1)
+        w1s = torch._utils._unflatten_dense_tensors(self.w1, w1)
+        for data, param in zip(w1s, w1):
+            param.data = data
+        self.w1 = self.w1.view(len(w1), *w1s[0].shape)
+
+        self.w2 = torch._utils._flatten_dense_tensors(w2)
+        w2s = torch._utils._unflatten_dense_tensors(self.w2, w2)
+        for data, param in zip(w2s, w2):
+            param.data = data
+
+        self.w2 = self.w2.view(len(w2), *w2s[0].shape)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        if self.config.n_shared_experts is not None:
+            shared_output = self.shared_experts(hidden_states)
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+        final_hidden_states = fused_moe(hidden_states,
+                                        self.w1,
+                                        self.w2,
+                                        router_logits,
+                                        self.top_k,
+                                        renormalize=self.config.norm_topk_prob,
+                                        inplace=True)
+
+        if self.config.n_shared_experts is not None:
+            final_hidden_states = final_hidden_states + shared_output
+        final_hidden_states = tensor_model_parallel_all_reduce(
+            final_hidden_states)
+
+        return final_hidden_states.view(num_tokens, hidden_dim)
+
+
+class DeepseekAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class DeepseekDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        layer_idx = extract_layer_index(prefix)
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = DeepseekAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        if (config.n_routed_experts is not None
+                and layer_idx >= config.first_k_dense_replace
+                and layer_idx % config.moe_layer_freq == 0):
+            self.mlp = DeepseekMoE(config=config,
+                                   quant_config=quant_config,
+                                   prefix=f"{prefix}.mlp")
+        else:
+            self.mlp = DeepseekMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class DeepseekModel(nn.Module):
+
+    fall_back_to_pt_during_load = False
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: DeepseekDecoderLayer(
+                config, cache_config, quant_config=quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers")
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(positions, hidden_states, residual)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip experts that are not assigned to this worker.
+                if (("mlp.experts." in name or "mlp.shared_experts." in name)
+                        and name not in params_dict):
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip experts that are not assigned to this worker.
+                if (("mlp.experts." in name or "mlp.shared_experts." in name)
+                        and name not in params_dict):
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class DeepseekForCausalLM(nn.Module, SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = DeepseekModel(vllm_config=vllm_config,
+                                   prefix=maybe_prefix(prefix, "model"))
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config)
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
\ No newline at end of file
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/dots1.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/dots1.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f410c0ae5fb08d3267412722657faaf912ef7d8
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/dots1.py
@@ -0,0 +1,544 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2025 The rednote-hilab team.
+# Copyright 2023 The vLLM team.
+# Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only dots1 model."""
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from transformers import Dots1Config
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.distributed import (get_pp_group,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class Dots1MLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config,
+                                           reduce_results=reduce_results,
+                                           prefix=f"{prefix}.down_proj")
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Dots1MoE(nn.Module):
+
+    def __init__(
+        self,
+        config: Dots1Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.n_shared_experts = config.n_shared_experts
+
+        if config.hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {config.hidden_act}. "
+                             "Only silu is supported for now.")
+
+        self.gate = ReplicatedLinear(config.hidden_size,
+                                     config.n_routed_experts,
+                                     bias=False,
+                                     quant_config=None,
+                                     prefix=f"{prefix}.gate")
+        if config.topk_method == "noaux_tc":
+            self.gate.e_score_correction_bias = (nn.Parameter(
+                torch.empty(config.n_routed_experts)))
+        else:
+            self.gate.e_score_correction_bias = None
+
+        self.experts = FusedMoE(
+            num_experts=config.n_routed_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=False,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            use_grouped_topk=True,
+            num_expert_group=config.n_group,
+            topk_group=config.topk_group,
+            prefix=f"{prefix}.experts",
+            scoring_func=config.scoring_func,
+            e_score_correction_bias=self.gate.e_score_correction_bias)
+
+        if config.n_shared_experts is not None:
+            intermediate_size = (config.moe_intermediate_size *
+                                 config.n_shared_experts)
+            self.shared_experts = Dots1MLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,
+                prefix=f"{prefix}.shared_experts",
+            )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        if self.n_shared_experts is not None:
+            shared_output = self.shared_experts(hidden_states)
+        router_logits, _ = self.gate(hidden_states)
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            router_logits=router_logits) * self.routed_scaling_factor
+        if shared_output is not None:
+            final_hidden_states = final_hidden_states + shared_output
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(
+                final_hidden_states)
+        return final_hidden_states.view(num_tokens, hidden_dim)
+
+
+class Dots1Attention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        config: Dots1Config,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = getattr(config, "head_dim",
+                                hidden_size // self.total_num_heads)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+        attention_bias = config.attention_bias
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=attention_bias,
+            quant_config=quant_config,
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+        self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+    def forward(self, positions: torch.Tensor,
+                hidden_states: torch.Tensor) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q = self.q_norm(q.reshape(-1, self.num_heads,
+                                  self.head_dim)).reshape(q.shape)
+        k = self.k_norm(k.reshape(-1, self.num_kv_heads,
+                                  self.head_dim)).reshape(k.shape)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Dots1DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Dots1Config,
+        prefix: str,
+        model_config: ModelConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        layer_idx = int(prefix.split(sep='.')[-1])
+        self.layer_idx = layer_idx
+
+        self.self_attn = Dots1Attention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            config=config,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        if (config.n_routed_experts is not None
+                and layer_idx >= config.first_k_dense_replace
+                and layer_idx % config.moe_layer_freq == 0):
+            self.mlp = Dots1MoE(config=config,
+                                quant_config=quant_config,
+                                prefix=f"{prefix}.mlp")
+        else:
+            self.mlp = Dots1MLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+            )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+        self.routed_scaling_factor = config.routed_scaling_factor
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(positions=positions,
+                                       hidden_states=hidden_states)
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Dots1Model(nn.Module):
+
+    fall_back_to_pt_during_load = False
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+
+        self.vocab_size = config.vocab_size
+
+        if get_pp_group().is_first_rank:
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=f"{prefix}.embed_tokens")
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Dots1DecoderLayer(
+                config,
+                prefix,
+                model_config=model_config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+            ),
+            prefix=f"{prefix}.layers")
+
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.n_routed_experts)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if (("mlp.experts." in name) and name not in params_dict):
+                    continue
+                name = name.replace(weight_name, param_name)
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param,
+                                  loaded_weight,
+                                  name,
+                                  shard_id=shard_id,
+                                  expert_id=expert_id)
+                    break
+                else:
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class Dots1ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Dots1Model(vllm_config=vllm_config,
+                                prefix=maybe_prefix(prefix, "model"))
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(config.vocab_size,
+                                          config.hidden_size,
+                                          quant_config=quant_config)
+        else:
+            self.lm_head = PPMissingLayer()
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(
+            input_ids,
+            positions,
+            intermediate_tensors,
+            inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/ernie45.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/ernie45.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7302dc5ecdd7bdedcd9a2da55c5b6b972393366
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/ernie45.py
@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 The Baidu team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Erine model compatible with HuggingFace weights."""
+from vllm.config import VllmConfig
+from vllm.model_executor.models.llama import LlamaForCausalLM
+
+from .utils import PPMissingLayer
+
+
+class Ernie4_5ForCausalLM(LlamaForCausalLM):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        # Hack Llama model to fit HF format Ernie4.5 dense implementation
+        # Attention difference between Ernie and Llama:
+        # 1. rotary_dim and no Neox style.
+        # 2. There is no bias for o_proj in attention
+        for layer in self.model.layers:
+            if not isinstance(layer, PPMissingLayer):
+                layer.self_attn.rotary_emb.is_neox_style = False
+                layer.self_attn.o_proj.bias = None
+                layer.self_attn.o_proj.skip_bias_add = True
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/fuyu.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/fuyu.py
new file mode 100644
index 0000000000000000000000000000000000000000..b61e0361fe8c3ee440b79cd4b55d3778ca8be38c
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/fuyu.py
@@ -0,0 +1,401 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://github.com/huggingface/transformers/blob/v4.39.3/src/transformers/models/fuyu/modeling_fuyu.py
+# Copyright 2023 The vLLM team.
+# Copyright 2023 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Fuyu model."""
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Literal, Optional
+
+import torch
+import torch.nn as nn
+from transformers import (BatchFeature, FuyuConfig, FuyuImageProcessor,
+                          FuyuProcessor)
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.linear import ColumnParallelLinear
+from vllm.model_executor.models.persimmon import PersimmonForCausalLM
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+                                   MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+# Cannot find the following 2 numbers from hf config.
+_IMAGE_TOKEN_ID = 71011
+_NEWLINE_TOKEN_ID = 71019
+
+
+class FuyuImagePatchInputs(TensorSchema):
+    """
+    Dimensions:
+        - bn: Batch size * number of images
+        - bnp: Batch size * number of images * number of patches
+        - fn: patch_size_x * patch_size_y * num_channels
+    """
+
+    type: Literal["image_patches"] = "image_patches"
+
+    flat_data: Annotated[
+        torch.Tensor,
+        TensorShape("bnp", "fn"),
+    ]
+
+    patches_per_image: Annotated[list[int], TensorShape("bn")]
+    """
+    The number of total patches for each image in the batch.
+    
+    This is used to split the embeddings which has the first two dimensions
+    flattened just like `flat_data`.
+    """
+
+
+class FuyuProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(FuyuConfig)
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(FuyuProcessor, **kwargs)
+
+    def get_image_processor(self, **kwargs: object) -> FuyuImageProcessor:
+        return self.get_hf_processor(**kwargs).image_processor
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": 1}
+
+    def get_image_feature_grid_size(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> tuple[int, int]:
+        image_processor = self.get_image_processor()
+        target_width = image_processor.size["width"]
+        target_height = image_processor.size["height"]
+        patch_width = image_processor.patch_size["width"]
+        patch_height = image_processor.patch_size["height"]
+
+        if not (image_width <= target_width and image_height <= target_height):
+            height_scale_factor = target_height / image_height
+            width_scale_factor = target_width / image_width
+            optimal_scale_factor = min(height_scale_factor, width_scale_factor)
+
+            image_height = int(image_height * optimal_scale_factor)
+            image_width = int(image_width * optimal_scale_factor)
+
+        ncols = math.ceil(image_width / patch_width)
+        nrows = math.ceil(image_height / patch_height)
+        return ncols, nrows
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        ncols, nrows = self.get_image_feature_grid_size(
+            image_width=image_width,
+            image_height=image_height,
+        )
+
+        return ncols * nrows
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        image_processor = self.get_image_processor()
+        return ImageSize(width=image_processor.size["width"],
+                         height=image_processor.size["height"])
+
+
+class FuyuDummyInputsBuilder(BaseDummyInputsBuilder[FuyuProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        return ""
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+        num_images = mm_counts.get("image", 0)
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+
+class FuyuMultiModalProcessor(BaseMultiModalProcessor[FuyuProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        if not mm_data:
+            # Avoid warning from HF logger for text-only input
+            prompt_ids = self.info.get_tokenizer().encode(prompt)
+            prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
+            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
+
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+
+        image_patches = processed_outputs.get("image_patches")
+        if image_patches is not None:
+            images = mm_data["images"]
+            assert isinstance(images, list)
+
+            # Original output: (1, num_images, Pn, Px * Py * C)
+            # New output: (num_images, Pn, Px * Py * C)
+            # image_patches is a list with shape:
+            # (1, num_images, Pn, Px * Py * C)
+            # before Transformers 4.53
+            if isinstance(image_patches, list):
+                assert len(image_patches) == 1
+                assert (isinstance(image_patches[0], torch.Tensor)
+                        and len(image_patches[0]) == len(images))
+                processed_outputs["image_patches"] = image_patches[0]
+            # image_patches is a tensor with shape:
+            # (num_images, Pn, Px * Py * C)
+            # after Transformers 4.53
+            elif isinstance(image_patches, torch.Tensor):
+                assert len(image_patches) == len(images)
+            else:
+                raise AssertionError("This line should be unreachable.")
+
+        return processed_outputs
+
+    def _apply_hf_processor_tokens_only(
+        self,
+        prompt_tokens: list[int],
+    ) -> list[int]:
+        # HF processor adds boa_token_id
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+
+        boa_token_id = vocab["<0x04>"]
+        if prompt_tokens[-1] != boa_token_id:
+            prompt_tokens.append(boa_token_id)
+
+        return prompt_tokens
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(image_patches=MultiModalFieldConfig.batched("image"))
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_config = self.info.get_hf_config()
+        bos_token_id = hf_config.bos_token_id
+        assert isinstance(bos_token_id, int)
+
+        tokenizer = self.info.get_tokenizer()
+        eot_token_id = tokenizer.bos_token_id
+        assert isinstance(eot_token_id, int)
+
+        def get_replacement_fuyu(item_idx: int):
+            images = mm_items.get_items("image", ImageProcessorItems)
+            image_size = images.get_image_size(item_idx)
+
+            ncols, nrows = self.info.get_image_feature_grid_size(
+                image_width=image_size.width,
+                image_height=image_size.height,
+            )
+            image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
+                            [_NEWLINE_TOKEN_ID]) * nrows
+
+            return PromptUpdateDetails.select_token_id(
+                image_tokens + [bos_token_id],
+                embed_token_id=_IMAGE_TOKEN_ID,
+            )
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=[eot_token_id],
+                replacement=get_replacement_fuyu,
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(FuyuMultiModalProcessor,
+                                        info=FuyuProcessingInfo,
+                                        dummy_inputs=FuyuDummyInputsBuilder)
+class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
+
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "model.vision_embed_tokens.": "vision_embed_tokens.",
+            "model.language_model.": "language_model.model.",
+            "lm_head.": "language_model.lm_head.",
+        })
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return None
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.vocab_size = config.text_config.vocab_size
+        self.image_token_id = _IMAGE_TOKEN_ID
+        self.image_feature_size = config.patch_size**2 * config.num_channels
+
+        self.vision_embed_tokens = ColumnParallelLinear(
+            self.image_feature_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            gather_output=True,
+        )
+        self.language_model = PersimmonForCausalLM(
+            vllm_config=vllm_config.with_hf_config(config.text_config),
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[FuyuImagePatchInputs]:
+        image_patches = kwargs.pop("image_patches", None)
+        if image_patches is not None:
+            image_patches_flat = flatten_bn(image_patches)
+            flat_data = flatten_bn(image_patches_flat, concat=True)
+
+            return FuyuImagePatchInputs(
+                type="image_patches",
+                flat_data=flat_data,
+                patches_per_image=[x.size(0) for x in image_patches_flat],
+                resolve_bindings={"fn": self.image_feature_size},
+            )
+
+        return None
+
+    def _process_image_input(
+            self, image_input: FuyuImagePatchInputs) -> MultiModalEmbeddings:
+        image_patches_flat = image_input["flat_data"]
+        patches_per_image = image_input["patches_per_image"]
+
+        assert self.vision_embed_tokens is not None
+        vision_embeddings_flat, _ = self.vision_embed_tokens(
+            image_patches_flat)
+
+        return vision_embeddings_flat.split(patches_per_image, dim=0)
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+
+        return self._process_image_input(image_input)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                _IMAGE_TOKEN_ID,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ):
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.language_model.logits_processor(
+            self.language_model.lm_head, hidden_states, sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma2.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cfe92c64540f2d45c9e9fd78e84788fc8300ebb
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma2.py
@@ -0,0 +1,424 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2024 The vLLM team.
+# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import Gemma2Config
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import GeluAndMul
+from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, extract_layer_index,
+                    is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+logger = init_logger(__name__)
+
+
+class Gemma2MLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        hidden_activation: str,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config)
+        if not (hidden_act == hidden_activation == "gelu_pytorch_tanh"):
+            raise ValueError(
+                "Gemma2 uses `gelu_pytorch_tanh` as the hidden activation "
+                "function. Please set `hidden_act` and `hidden_activation` to "
+                "`gelu_pytorch_tanh`.")
+        self.act_fn = GeluAndMul(approximate="tanh")
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Gemma2Attention(nn.Module):
+
+    def __init__(self,
+                 config: Gemma2Config,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 head_dim: int,
+                 max_position_embeddings: int,
+                 rope_theta: float,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 attn_logits_soft_cap: Optional[float] = None,
+                 prefix: str = "") -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = config.query_pre_attn_scalar**-0.5
+        self.rope_theta = rope_theta
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=self.rope_theta,
+            is_neox_style=True,
+        )
+
+        layer_idx = extract_layer_index(prefix)
+        is_sliding = config.layer_types[layer_idx] == "sliding_attention"
+        sliding_window = config.sliding_window if is_sliding else None
+
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              logits_soft_cap=attn_logits_soft_cap,
+                              per_layer_sliding_window=sliding_window,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Gemma2DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Gemma2Config,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Gemma2Attention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            head_dim=config.head_dim,
+            max_position_embeddings=config.max_position_embeddings,
+            rope_theta=config.rope_theta,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            attn_logits_soft_cap=config.attn_logit_softcapping,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.hidden_size = config.hidden_size
+        self.mlp = Gemma2MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            hidden_activation=config.hidden_activation,
+            quant_config=quant_config,
+        )
+        self.input_layernorm = GemmaRMSNorm(config.hidden_size,
+                                            eps=config.rms_norm_eps)
+        self.post_attention_layernorm = GemmaRMSNorm(config.hidden_size,
+                                                     eps=config.rms_norm_eps)
+        self.pre_feedforward_layernorm = GemmaRMSNorm(config.hidden_size,
+                                                      eps=config.rms_norm_eps)
+        self.post_feedforward_layernorm = GemmaRMSNorm(config.hidden_size,
+                                                       eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        hidden_states, residual = self.pre_feedforward_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.post_feedforward_layernorm(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Gemma2Model(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Gemma2DecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers")
+        self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        # Normalize the embedding by sqrt(hidden_size)
+        # The normalizer's data type should be downcasted to the model's
+        # data type such as bfloat16, not float32.
+        # See https://github.com/huggingface/transformers/pull/29402
+        normalizer = self.config.hidden_size**0.5
+        self.register_buffer("normalizer",
+                             torch.tensor(normalizer),
+                             persistent=False)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            hidden_states *= self.normalizer
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache scales for compressed-tensors quantization
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = loaded_weight[0]
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            for (param_name, shard_name, shard_id) in stacked_params_mapping:
+                if shard_name not in name:
+                    continue
+                name = name.replace(shard_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+class Gemma2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        del lora_config  # Unused.
+        super().__init__()
+        self.config = config
+        # currently all existing Gemma models have `tie_word_embeddings` enabled
+        assert config.tie_word_embeddings
+        self.quant_config = quant_config
+        self.model = Gemma2Model(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "model"))
+        self.logits_processor = LogitsProcessor(
+            config.vocab_size, soft_cap=config.final_logit_softcapping)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.model.embed_tokens, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma3.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma3.py
new file mode 100644
index 0000000000000000000000000000000000000000..b762be3c5292511f9fb57df9c53e2a62bca4d693
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma3.py
@@ -0,0 +1,532 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2025 The vLLM team.
+# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import Gemma3TextConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import GeluAndMul
+from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, extract_layer_index,
+                    is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+logger = init_logger(__name__)
+
+
+class Gemma3MLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_activation: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_activation != "gelu_pytorch_tanh":
+            raise ValueError(
+                "Gemma3 uses `gelu_pytorch_tanh` as the hidden activation "
+                "function. Please set `hidden_act` and `hidden_activation` to "
+                "`gelu_pytorch_tanh`.")
+        self.act_fn = GeluAndMul(approximate="tanh")
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Gemma3Attention(nn.Module):
+
+    def __init__(self,
+                 config: Gemma3TextConfig,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 head_dim: int,
+                 max_position_embeddings: int,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 attn_logits_soft_cap: Optional[float] = None,
+                 prefix: str = "") -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = config.query_pre_attn_scalar**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self.q_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+        layer_idx = extract_layer_index(prefix)
+        self.is_sliding = config.layer_types[layer_idx] == "sliding_attention"
+        sliding_window = config.sliding_window if self.is_sliding else None
+
+        # Initialize the rotary embedding.
+        if self.is_sliding:
+            # Local attention. Override the values in config.json.
+            self.rope_theta = config.rope_local_base_freq
+            self.rope_scaling = {"rope_type": "default"}
+        else:
+            # Global attention. Use the values in config.json.
+            self.rope_theta = config.rope_theta
+            self.rope_scaling = config.rope_scaling
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=self.rope_theta,
+            is_neox_style=True,
+            rope_scaling=self.rope_scaling,
+        )
+
+        # Initialize the attention.
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              logits_soft_cap=attn_logits_soft_cap,
+                              per_layer_sliding_window=sliding_window,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        q = q.unflatten(-1, (self.num_heads, self.head_dim))
+        q = self.q_norm(q)
+        q = q.flatten(-2, -1)
+        k = k.unflatten(-1, (self.num_kv_heads, self.head_dim))
+        k = self.k_norm(k)
+        k = k.flatten(-2, -1)
+
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+
+        if not kwargs.get("has_images", False):
+            # Fast path for text-only inputs. The performance for the text-only
+            # inputs are not affected by the naive attention below.
+            output, _ = self.o_proj(attn_output)
+            return output
+
+        # NOTE(woosuk): Gemma3 uses bidirectional attention between image tokens
+        # that correspond to the same image while using causal attention
+        # otherwise. Current attention backends cannot handle this pattern, so
+        # we temporarily use a naive attention implementation with mask tensors.
+
+        # We intentionally keep the attention backend as-is and only override
+        # `attn_output` with the naive implementation's output. This minimizes
+        # changes to existing model runners and attention backends. The call to
+        # `self.attn(q, k, v)` is only used to populate the KV cache - its
+        # output is discarded and overwritten below. While this duplicates
+        # computation, it maintains compatibility.
+        # TODO(woosuk): Optimize by implementing custom attention kernels.
+        attn_output = self.naive_attn_with_masks(q,
+                                                 k,
+                                                 v,
+                                                 out=attn_output,
+                                                 **kwargs)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+    def naive_attn_with_masks(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        out: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        # NOTE(woosuk): As described in the comment above, this code is not
+        # meant to be performant. It is only meant to be correct.
+        q = q.view(-1, self.num_heads, self.head_dim)
+        # Expand the key and value to handle GQA.
+        num_queries_per_kv = self.num_heads // self.num_kv_heads
+        k = k.view(-1, self.num_kv_heads, self.head_dim)
+        k = k.repeat_interleave(num_queries_per_kv, dim=-2)
+        v = v.view(-1, self.num_kv_heads, self.head_dim)
+        v = v.repeat_interleave(num_queries_per_kv, dim=-2)
+
+        if self.is_sliding:
+            attn_masks = kwargs["local_attn_masks"]
+        else:
+            attn_masks = kwargs["global_attn_masks"]
+
+        seq_lens = kwargs["seq_lens"]
+        start_idx = 0
+        for seq_len, attn_mask in zip(seq_lens, attn_masks):
+            end_idx = start_idx + seq_len
+            query = q[start_idx:end_idx].unsqueeze(0)
+            key = k[start_idx:end_idx].unsqueeze(0)
+            value = v[start_idx:end_idx].unsqueeze(0)
+
+            # Transpose.
+            query = query.transpose(1, 2)
+            key = key.transpose(1, 2)
+            value = value.transpose(1, 2)
+
+            output = F.scaled_dot_product_attention(
+                query,
+                key,
+                value,
+                attn_mask,
+                self.scaling,
+            )
+            output = output.transpose(1, 2).flatten(-2, -1)
+            out[start_idx:end_idx] = output
+            start_idx = end_idx
+        return out
+
+
+class Gemma3DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Gemma3TextConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Gemma3Attention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            head_dim=config.head_dim,
+            max_position_embeddings=config.max_position_embeddings,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            attn_logits_soft_cap=None,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.hidden_size = config.hidden_size
+        self.mlp = Gemma3MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_activation=config.hidden_activation,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.input_layernorm = GemmaRMSNorm(config.hidden_size,
+                                            eps=config.rms_norm_eps)
+        self.post_attention_layernorm = GemmaRMSNorm(config.hidden_size,
+                                                     eps=config.rms_norm_eps)
+        self.pre_feedforward_layernorm = GemmaRMSNorm(config.hidden_size,
+                                                      eps=config.rms_norm_eps)
+        self.post_feedforward_layernorm = GemmaRMSNorm(config.hidden_size,
+                                                       eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            **kwargs,
+        )
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        hidden_states, residual = self.pre_feedforward_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.post_feedforward_layernorm(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Gemma3Model(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            prefix=f"{prefix}.embed_tokens",
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Gemma3DecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers")
+        self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        # Normalize the embedding by sqrt(hidden_size)
+        # The normalizer's data type should be downcasted to the model's
+        # data type such as bfloat16, not float32.
+        # See https://github.com/huggingface/transformers/pull/29402
+        normalizer = self.config.hidden_size**0.5
+        self.register_buffer("normalizer",
+                             torch.tensor(normalizer),
+                             persistent=False)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        # NOTE(woosuk): Only apply the normalizer to the output of
+        # vocab embedding. Don't apply it to the vision embedding.
+        return self.embed_tokens(input_ids) * self.normalizer
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+                **kwargs,
+            )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache scales for compressed-tensors quantization
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = loaded_weight[0]
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            for (param_name, shard_name, shard_id) in stacked_params_mapping:
+                if shard_name not in name:
+                    continue
+                name = name.replace(shard_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+class Gemma3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        del lora_config  # Unused.
+        super().__init__()
+        self.config = config
+        # currently all existing Gemma models have `tie_word_embeddings` enabled
+        assert config.tie_word_embeddings
+        self.quant_config = quant_config
+        self.model = Gemma3Model(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "model"))
+        self.logits_processor = LogitsProcessor(
+            config.vocab_size, soft_cap=config.final_logit_softcapping)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds, **kwargs)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.model.embed_tokens, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma3_mm.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma3_mm.py
new file mode 100644
index 0000000000000000000000000000000000000000..9871b11b37991f87dad300752dd636663897519d
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma3_mm.py
@@ -0,0 +1,723 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Any, Literal, Optional
+
+import torch
+from torch import nn
+from transformers import BatchFeature, Gemma3Config, Gemma3Processor
+from transformers.models.gemma3.processing_gemma3 import Gemma3ProcessorKwargs
+
+import vllm.envs as envs
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+                                   MultiModalDataItems)
+# yapf: disable
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, BoundPromptUpdate,
+                                        PlaceholderFeaturesInfo,
+                                        PromptReplacement, PromptTargetMatch,
+                                        PromptUpdate, PromptUpdateDetails,
+                                        find_mm_placeholders,
+                                        replace_token_matches)
+# yapf: enable
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
+from .siglip import SiglipVisionModel
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+logger = init_logger(__name__)
+
+
+class Gemma3ImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - p: Number of patches total (over each image over each prompt in the
+          batch)
+        - c: Number of channels (3)
+        - h: Height of each patch
+        - w: Width of each patch
+        - bn: Batch size * number of images
+    """
+    type: Literal["pixel_values"] = "pixel_values"
+
+    pixel_values: Annotated[torch.Tensor, TensorShape("p", 3, "h", "w")]
+
+    num_patches: Annotated[torch.Tensor, TensorShape("bn")]
+
+
+Gemma3ImageInputs = Gemma3ImagePixelInputs
+
+
+class Gemma3ProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Gemma3Config)
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(Gemma3Processor, **kwargs)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def _resolve_image_kwargs(
+        self,
+        processor: Gemma3Processor,
+        keys: set[str],
+    ) -> dict[str, Any]:
+        image_processor = processor.image_processor
+        kwargs = processor._merge_kwargs(
+            Gemma3ProcessorKwargs,
+            tokenizer_init_kwargs=processor.tokenizer.init_kwargs,
+        )
+
+        images_kwargs = kwargs["images_kwargs"]
+
+        def _resolve_kw(key: str):
+            val = getattr(image_processor, key)
+            if val is None:
+                val = images_kwargs[key]
+
+            return val
+
+        return {k: _resolve_kw(k) for k in keys}
+
+    def get_num_crops(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Gemma3Processor],
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        images_kwargs = self._resolve_image_kwargs(
+            processor, {
+                "do_pan_and_scan", "pan_and_scan_min_crop_size",
+                "pan_and_scan_max_num_crops",
+                "pan_and_scan_min_ratio_to_activate"
+            })
+
+        do_pan_and_scan = images_kwargs["do_pan_and_scan"]
+        pan_and_scan_min_crop_size = images_kwargs[
+            "pan_and_scan_min_crop_size"]
+        pan_and_scan_max_num_crops = images_kwargs[
+            "pan_and_scan_max_num_crops"]
+        pan_and_scan_min_ratio_to_activate = images_kwargs[
+            "pan_and_scan_min_ratio_to_activate"]
+
+        if not do_pan_and_scan:
+            return 0
+
+        if envs.VLLM_USE_V1:
+            logger.warning_once(
+                "`do_pan_and_scan=True` has suboptimal results on V1 "
+                "because of the simplified attention pattern being used.")
+
+        # Based on Gemma3ImageProcessor.pan_and_scan
+        if image_width >= image_height:
+            if image_width / image_height < pan_and_scan_min_ratio_to_activate:
+                return 0
+
+            num_crops_w = min(
+                int(math.floor(image_width / pan_and_scan_min_crop_size)),
+                int(math.floor(image_width / image_height + 0.5)),
+            )
+
+            num_crops_w = max(2, num_crops_w)
+            num_crops_w = min(pan_and_scan_max_num_crops, num_crops_w)
+            num_crops_h = 1
+        else:
+            if image_height / image_width < pan_and_scan_min_ratio_to_activate:
+                return 0
+
+            num_crops_h = min(
+                int(math.floor(image_height / pan_and_scan_min_crop_size)),
+                int(math.floor(image_height / image_width + 0.5)),
+            )
+
+            num_crops_h = max(2, num_crops_h)
+            num_crops_h = min(pan_and_scan_max_num_crops, num_crops_h)
+            num_crops_w = 1
+
+        crop_size_w = int(math.ceil(image_width / num_crops_w))
+        crop_size_h = int(math.ceil(image_height / num_crops_h))
+
+        if min(crop_size_w, crop_size_h) < pan_and_scan_min_crop_size:
+            return 0
+
+        return num_crops_w * num_crops_h
+
+    def get_image_repl(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Gemma3Processor],
+    ) -> PromptUpdateDetails[str]:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        boi_token = processor.boi_token
+
+        num_crops = self.get_num_crops(
+            image_width=image_width,
+            image_height=image_height,
+            processor=processor,
+        )
+
+        if num_crops == 0:
+            image_text = boi_token
+        else:
+            crops_image_tokens = " ".join(boi_token for _ in range(num_crops))
+            image_text = (
+                f"Here is the original image {boi_token} and here are some "
+                f"crops to help you see better {crops_image_tokens}")
+
+        repl_full = image_text.replace(boi_token,
+                                       processor.full_image_sequence)
+
+        tokenizer = processor.tokenizer
+        vocab = tokenizer.get_vocab()
+        image_token_id = vocab[tokenizer.image_token]
+
+        return PromptUpdateDetails.select_token_id(repl_full, image_token_id)
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Gemma3Processor],
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        num_crops = self.get_num_crops(
+            image_width=image_width,
+            image_height=image_height,
+            processor=processor,
+        )
+        image_seq_len = processor.image_seq_length
+
+        return (num_crops + 1) * image_seq_len
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        processor = self.get_hf_processor()
+
+        images_kwargs = self._resolve_image_kwargs(
+            processor, {"pan_and_scan_max_num_crops"})
+        max_num_crops = images_kwargs["pan_and_scan_max_num_crops"]
+
+        # Result in the max possible feature size (h:w = max_num_crops:1)
+        return ImageSize(height=50 * max_num_crops, width=50)
+
+
+class Gemma3DummyInputsBuilder(BaseDummyInputsBuilder[Gemma3ProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        processor = self.info.get_hf_processor()
+        image_token = processor.boi_token
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+
+class Gemma3MultiModalProcessor(BaseMultiModalProcessor[Gemma3ProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        processed_outputs = super()._call_hf_processor(
+            prompt,
+            mm_data,
+            mm_kwargs,
+            tok_kwargs,
+        )
+
+        # HF processor pops the `num_crops` kwarg, which is needed by vLLM
+        if (images := mm_data.get("images")) is not None:
+            parsed_images = (self._get_data_parser().parse_mm_data({
+                "image":
+                images
+            }).get_items("image", ImageProcessorItems))
+            image_sizes = [
+                parsed_images.get_image_size(i)
+                for i in range(len(parsed_images))
+            ]
+            hf_processor = self.info.get_hf_processor(**mm_kwargs)
+
+            num_crops = [
+                self.info.get_num_crops(image_width=size.width,
+                                        image_height=size.height,
+                                        processor=hf_processor)
+                for size in image_sizes
+            ]
+            processed_outputs["num_crops"] = torch.tensor(num_crops)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        num_crops = hf_inputs.get("num_crops", torch.empty(0))
+
+        return dict(
+            pixel_values=MultiModalFieldConfig.flat_from_sizes(
+                "image", num_crops + 1),
+            num_crops=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_token = hf_processor.boi_token
+
+        def get_replacement_gemma3(item_idx: int):
+            images = mm_items.get_items("image", ImageProcessorItems)
+
+            image_size = images.get_image_size(item_idx)
+            return self.info.get_image_repl(
+                image_width=image_size.width,
+                image_height=image_size.height,
+                processor=hf_processor,
+            )
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=image_token,
+                replacement=get_replacement_gemma3,
+            )
+        ]
+
+    def _apply_token_matches(
+        self,
+        prompt: list[int],
+        mm_matches: Mapping[str, Sequence[PromptTargetMatch]],
+        mm_item_counts: Mapping[str, int],
+    ) -> list[int]:
+        token_ids = super()._apply_token_matches(
+            prompt,
+            mm_matches,
+            mm_item_counts,
+        )
+
+        # "\n\n\n" and "\n\n\n\n" are single tokens
+        # Since our replacement can insert "\n\n" next to "\n"
+        # tokens, we have to combine them to be consistent with
+        # the output of the tokenizer
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+        newline_1 = vocab["\n"]
+        newline_2 = vocab["\n\n"]
+        newline_3 = vocab["\n\n\n"]
+        newline_4 = vocab["\n\n\n\n"]
+
+        token_ids = replace_token_matches(
+            token_ids,
+            [newline_1, newline_2],
+            [newline_3],
+        )
+        token_ids = replace_token_matches(
+            token_ids,
+            [newline_2, newline_1],
+            [newline_3],
+        )
+        token_ids = replace_token_matches(
+            token_ids,
+            [newline_2, newline_2],
+            [newline_4],
+        )
+
+        return token_ids
+
+    def _find_mm_placeholders(
+        self,
+        mm_prompt_updates: Mapping[str, Sequence[BoundPromptUpdate]],
+        new_token_ids: list[int],
+        mm_item_counts: Mapping[str, int],
+    ) -> Mapping[str, list[PlaceholderFeaturesInfo]]:
+        # We need to detect "\n\n" inside "\n\n\n" and "\n\n\n\n"
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+        newline_1 = vocab["\n"]
+        newline_2 = vocab["\n\n"]
+        newline_3 = vocab["\n\n\n"]
+        newline_4 = vocab["\n\n\n\n"]
+
+        def get_repl_toks(tok: int) -> list[int]:
+            if tok == newline_3:
+                return [newline_1, newline_2]
+            if tok == newline_4:
+                return [newline_2, newline_2]
+
+            return [tok]
+
+        repl_token_ids = list[int]()
+        repl_orig_idxs = list[int]()
+        for orig_idx, orig_tok in enumerate(new_token_ids):
+            repl_toks = get_repl_toks(orig_tok)
+            repl_token_ids.extend(repl_toks)
+            repl_orig_idxs.extend(orig_idx for _ in range(len(repl_toks)))
+
+        repls = find_mm_placeholders(mm_prompt_updates, repl_token_ids,
+                                     mm_item_counts)
+
+        return {
+            modality: [
+                PlaceholderFeaturesInfo(
+                    modality=p.modality,
+                    item_idx=p.item_idx,
+                    start_idx=repl_orig_idxs[p.start_idx],
+                    tokens=p.tokens,
+                    is_embed=p.is_embed,
+                ) for p in placeholders
+            ]
+            for modality, placeholders in repls.items()
+        }
+
+
+class Gemma3MultiModalProjector(nn.Module):
+
+    def __init__(self, config: Gemma3Config):
+        super().__init__()
+
+        self.mm_input_projection_weight = nn.Parameter(
+            torch.zeros(config.vision_config.hidden_size,
+                        config.text_config.hidden_size))
+
+        self.mm_soft_emb_norm = GemmaRMSNorm(
+            config.vision_config.hidden_size,
+            eps=config.vision_config.layer_norm_eps)
+
+        self.patches_per_image = int(config.vision_config.image_size //
+                                     config.vision_config.patch_size)
+        self.tokens_per_side = int(config.mm_tokens_per_image**0.5)
+        self.kernel_size = self.patches_per_image // self.tokens_per_side
+        self.avg_pool = nn.AvgPool2d(kernel_size=self.kernel_size,
+                                     stride=self.kernel_size)
+
+    def forward(self, vision_outputs: torch.Tensor):
+        batch_size, _, seq_length = vision_outputs.shape
+
+        reshaped_vision_outputs = vision_outputs.transpose(1, 2)
+        reshaped_vision_outputs = reshaped_vision_outputs.reshape(
+            batch_size, seq_length, self.patches_per_image,
+            self.patches_per_image)
+        reshaped_vision_outputs = reshaped_vision_outputs.contiguous()
+
+        pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs)
+        pooled_vision_outputs = pooled_vision_outputs.flatten(2)
+        pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2)
+
+        normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs)
+
+        projected_vision_outputs = torch.matmul(
+            normed_vision_outputs, self.mm_input_projection_weight)
+        return projected_vision_outputs.type_as(vision_outputs)
+
+
+@MULTIMODAL_REGISTRY.register_processor(Gemma3MultiModalProcessor,
+                                        info=Gemma3ProcessingInfo,
+                                        dummy_inputs=Gemma3DummyInputsBuilder)
+class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
+                                     SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            # mapping for new names in checkpoint saved after transformers v4.52
+            "model.language_model.": "language_model.model.",
+            "model.vision_tower.": "vision_tower.",
+            "model.multi_modal_projector.": "multi_modal_projector.",
+            "lm_head.": "language_model.lm_head.",
+        })
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<start_of_image>"
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.quant_config = quant_config
+        self.multimodal_config = multimodal_config
+
+        self.vision_tower = SiglipVisionModel(config.vision_config,
+                                              quant_config,
+                                              prefix=maybe_prefix(
+                                                  prefix, "vision_tower"))
+        self.multi_modal_projector = Gemma3MultiModalProjector(config)
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=["Gemma3ForCausalLM"],
+        )
+        logit_scale = getattr(config, "logit_scale", 1.0)
+        self.language_model.logits_processor.scale *= logit_scale
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Gemma3ImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        num_crops = kwargs.pop("num_crops", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        assert image_embeds is None, "Gemma3 does not support image_embeds."
+        if pixel_values is None:
+            return None
+
+        if not isinstance(pixel_values, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of pixel values. "
+                             f"Got type: {type(pixel_values)}")
+
+        if not isinstance(num_crops, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of num_crops. "
+                             f"Got type: {type(num_crops)}")
+
+        image_size = self.config.vision_config.image_size
+
+        return Gemma3ImagePixelInputs(
+            pixel_values=flatten_bn(pixel_values, concat=True),
+            num_patches=flatten_bn(num_crops, concat=True) + 1,
+            resolve_bindings={
+                "h": image_size,
+                "w": image_size
+            })
+
+    def _image_pixels_to_features(
+        self,
+        vision_tower: SiglipVisionModel,
+        pixel_values: torch.Tensor,
+    ) -> torch.Tensor:
+        return vision_tower(pixel_values)
+
+    def _process_image_input(
+        self,
+        image_input: Gemma3ImageInputs,
+    ) -> list[torch.Tensor]:
+        assert self.vision_tower is not None
+
+        pixel_values = image_input["pixel_values"]
+        num_patches = image_input["num_patches"]
+
+        image_features = self._image_pixels_to_features(
+            self.vision_tower,
+            pixel_values,
+        )
+        image_embeds = self.multi_modal_projector(image_features)
+
+        return [
+            e.flatten(0, 1) for e in image_embeds.split(num_patches.tolist())
+        ]
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+
+        return self._process_image_input(image_input)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                self.config.image_token_index,
+            )
+        return inputs_embeds
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs: object) -> IntermediateTensors:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            if (vision_embeddings is not None) and len(vision_embeddings) != 0:
+                kwargs = self.prepare_attn_masks(
+                    input_ids,
+                    positions,
+                    mask_dtype=self.dtype,
+                    **kwargs,
+                )
+            input_ids = None
+
+        hidden_states = self.language_model.model(input_ids,
+                                                  positions,
+                                                  intermediate_tensors,
+                                                  inputs_embeds=inputs_embeds,
+                                                  **kwargs)
+
+        return hidden_states
+
+    def prepare_attn_masks(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        mask_dtype: torch.dtype,
+        **kwargs,
+    ):
+        kwargs["has_images"] = True
+        # NOTE(woosuk): Here, we distinguish the sequences by the position id 0.
+        # This is a HACK. Fix this.
+        start_indices = (positions == 0).cpu().nonzero()
+        num_seqs = len(start_indices)
+        seq_lens = []
+        for i in range(num_seqs):
+            start_idx = start_indices[i].item()
+            if i < num_seqs - 1:
+                end_idx = start_indices[i + 1].item()
+            else:
+                end_idx = len(input_ids)
+            seq_lens.append(end_idx - start_idx)
+        kwargs["seq_lens"] = seq_lens
+
+        global_attn_masks = []
+        local_attn_masks = []
+        start_idx = 0
+        for seq_len in seq_lens:
+            end_idx = start_idx + seq_len
+            input_token_ids = input_ids[start_idx:end_idx]
+            start_idx = end_idx
+            # Create a global causal mask.
+            global_attn_mask = torch.empty(
+                1,
+                1,
+                seq_len,
+                seq_len,
+                dtype=mask_dtype,
+                device=input_ids.device,
+            )
+            global_attn_mask.fill_(float("-inf"))
+            # Fill the lower triangle with 0.
+            global_attn_mask = global_attn_mask.triu(diagonal=1)
+
+            # Consider the bidirectional attention between image tokens.
+            img_mask = torch.zeros_like(global_attn_mask)
+            img_pos = (input_token_ids == self.config.image_token_index)
+            img_mask[:, :, :, img_pos] += 1
+            img_mask[:, :, img_pos, :] += 1
+            global_attn_mask = torch.where(img_mask == 2, 0, global_attn_mask)
+            global_attn_masks.append(global_attn_mask)
+
+            if (sliding_window := self.config.sliding_window) is not None:
+                # Create a local causal mask with sliding window (1024).
+                local_attn_mask = torch.ones_like(global_attn_mask)
+                local_attn_mask = torch.tril(local_attn_mask,
+                                             diagonal=-sliding_window)
+                local_attn_mask = torch.where(local_attn_mask == 0,
+                                              global_attn_mask, float("-inf"))
+                local_attn_masks.append(local_attn_mask)
+        kwargs["global_attn_masks"] = global_attn_masks
+        kwargs["local_attn_masks"] = local_attn_masks
+        return kwargs
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="multi_modal_projector",
+            tower_model="vision_tower")
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma3n_mm.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma3n_mm.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0c3bb50070b3a45f2fac72992a5a86472ca782c
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gemma3n_mm.py
@@ -0,0 +1,700 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Any, Optional, TypedDict, Union, cast
+
+import torch
+from torch import nn
+from transformers import AutoModel, BatchFeature
+from transformers.models.gemma3n import (Gemma3nAudioConfig,
+                                         Gemma3nAudioFeatureExtractor,
+                                         Gemma3nConfig, Gemma3nProcessor,
+                                         Gemma3nTextConfig,
+                                         Gemma3nVisionConfig)
+from transformers.models.siglip import SiglipImageProcessorFast
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import RowParallelLinear
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.models.gemma3n import Gemma3nForCausalLM
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import (ImageProcessorItems, MultiModalDataItems,
+                                   MultiModalDataParser)
+# yapf: disable
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, BoundPromptUpdate,
+                                        PlaceholderFeaturesInfo,
+                                        PromptReplacement, PromptTargetMatch,
+                                        PromptUpdate, PromptUpdateDetails,
+                                        find_mm_placeholders,
+                                        replace_token_matches)
+# yapf: enable
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+logger = init_logger(__name__)
+
+# This should be based on model config but we hardcode them for now.
+TOKENS_PER_IMAGE = 256
+TOKENS_PER_AUDIO = 188
+
+
+class Gemma3nImagePixelInputs(TypedDict):
+    pixel_values: torch.Tensor
+    """Shape: `(batch_size * num_images, num_channels, height, width)`"""
+
+
+class Gemma3nAudioInputs(TypedDict):
+    input_features: torch.Tensor
+    """Shape: `(batch_size * num_audio, seq_length, num_features)`"""
+    input_features_mask: torch.Tensor
+    """Shape: `(batch_size * num_audio, seq_length)`"""
+
+
+Gemma3nImageInputs = Gemma3nImagePixelInputs
+
+
+class Gemma3nProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Gemma3nConfig)
+
+    def get_hf_processor(self, **kwargs: object):
+        return self.ctx.get_hf_processor(Gemma3nProcessor, **kwargs)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None, "audio": None}
+
+    def get_max_tokens_per_item(
+            self, seq_len: int,
+            mm_counts: Mapping[str, int]) -> Optional[Mapping[str, int]]:
+
+        return {"image": TOKENS_PER_IMAGE, "audio": TOKENS_PER_AUDIO}
+
+    def get_image_repl(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Gemma3nProcessor],
+    ) -> str:
+        """
+        Get the replacement text for image tokens.
+        
+        For Gemma3n, this should return the full_image_sequence which includes
+        BOI token, repeated image tokens, and EOI token.
+        """
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        return PromptUpdateDetails.select_token_id(
+            processor.full_image_sequence, processor.image_token_id)
+
+    def get_audio_repl(
+        self,
+        *,
+        processor: Optional[Gemma3nProcessor],
+    ) -> str:
+        """
+        Get the replacement text for audio tokens.
+        
+        For Gemma3n, this should return the full_audio_sequence which includes
+        BOA token, repeated audio tokens, and EOA token.
+        """
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        # Return the full audio sequence as defined by the processor
+        return PromptUpdateDetails.select_token_id(
+            processor.full_audio_sequence, processor.audio_token_id)
+
+
+class Gemma3nDummyInputsBuilder(BaseDummyInputsBuilder[Gemma3nProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+        num_audios = mm_counts.get("audio", 0)
+
+        processor = self.info.get_hf_processor()
+        image_token = processor.image_token
+        audio_token = processor.audio_token
+
+        return image_token * num_images + audio_token * num_audios
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        num_audios = mm_counts.get("audio", 0)
+        processor = self.info.get_hf_processor()
+        audio_feature_extractor: Gemma3nAudioFeatureExtractor = processor.feature_extractor  # noqa: E501
+        audio_len = audio_feature_extractor.fft_length
+        image_processor: SiglipImageProcessorFast = processor.image_processor
+        img_width = image_processor.size.get("width", 224)
+        img_height = image_processor.size.get("height", 224)
+
+        return {
+            "image":
+            self._get_dummy_images(width=img_width,
+                                   height=img_height,
+                                   num_images=num_images),
+            "audio":
+            self._get_dummy_audios(length=audio_len, num_audios=num_audios)
+        }
+
+
+class Gemma3nMultiModalProcessor(BaseMultiModalProcessor[Gemma3nProcessingInfo]
+                                 ):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        feature_extractor = self.info.get_hf_processor().feature_extractor
+        return MultiModalDataParser(target_sr=feature_extractor.sampling_rate)
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+
+        # HF Transformers audio processor no longer accepts `audios` key.
+        # We pop `audios` and replace it with `audio` key to surpress
+        # the warning.
+        if 'audios' in mm_data:
+            mm_data['audio'] = mm_data.pop('audios')
+        processed_outputs = super()._call_hf_processor(
+            prompt,
+            mm_data,
+            mm_kwargs,
+            tok_kwargs,
+        )
+        if 'input_features' in processed_outputs:
+            # Avoid padding since we need the output of each item to be
+            # independent of other items for the cache to work correctly
+            unpadded_features = [
+                f[mask] for f, mask in zip(
+                    processed_outputs["input_features"],
+                    processed_outputs["input_features_mask"],
+                )
+            ]
+            processed_outputs["input_features"] = unpadded_features
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+
+        return dict(pixel_values=MultiModalFieldConfig.batched("image"),
+                    input_features=MultiModalFieldConfig.batched("audio"),
+                    input_features_mask=MultiModalFieldConfig.batched("audio"))
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        prompt_updates = []
+
+        # Handle image tokens
+        if "image" in mm_items:
+            image_token = hf_processor.image_token
+
+            def get_replacement_image(item_idx: int):
+                images = mm_items.get_items("image", ImageProcessorItems)
+                image_size = images.get_image_size(item_idx)
+                return self.info.get_image_repl(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                    processor=hf_processor,
+                )
+
+            prompt_updates.append(
+                PromptReplacement(
+                    modality="image",
+                    target=image_token,
+                    replacement=get_replacement_image,
+                ))
+
+        # Handle audio tokens
+        if "audio" in mm_items:
+            audio_token = hf_processor.audio_token
+
+            def get_replacement_audio(item_idx: int):
+                return self.info.get_audio_repl(processor=hf_processor, )
+
+            prompt_updates.append(
+                PromptReplacement(
+                    modality="audio",
+                    target=audio_token,
+                    replacement=get_replacement_audio,
+                ))
+
+        return prompt_updates
+
+    def _apply_token_matches(
+        self,
+        prompt: list[int],
+        mm_matches: Mapping[str, Sequence[PromptTargetMatch]],
+        mm_item_counts: Mapping[str, int],
+    ) -> list[int]:
+        token_ids = super()._apply_token_matches(
+            prompt,
+            mm_matches,
+            mm_item_counts,
+        )
+
+        # "\n\n\n" and "\n\n\n\n" are single tokens
+        # Since our replacement can insert "\n\n" next to "\n"
+        # tokens, we have to combine them to be consistent with
+        # the output of the tokenizer
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+        newline_1 = vocab["\n"]
+        newline_2 = vocab["\n\n"]
+        newline_3 = vocab["\n\n\n"]
+        newline_4 = vocab["\n\n\n\n"]
+
+        token_ids = replace_token_matches(
+            token_ids,
+            [newline_1, newline_2],
+            [newline_3],
+        )
+        token_ids = replace_token_matches(
+            token_ids,
+            [newline_2, newline_1],
+            [newline_3],
+        )
+        token_ids = replace_token_matches(
+            token_ids,
+            [newline_2, newline_2],
+            [newline_4],
+        )
+
+        return token_ids
+
+    def _find_mm_placeholders(
+        self,
+        mm_prompt_updates: Mapping[str, Sequence[BoundPromptUpdate]],
+        new_token_ids: list[int],
+        mm_item_counts: Mapping[str, int],
+    ) -> Mapping[str, list[PlaceholderFeaturesInfo]]:
+        # We need to detect "\n\n" inside "\n\n\n" and "\n\n\n\n"
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+        newline_1 = vocab["\n"]
+        newline_2 = vocab["\n\n"]
+        newline_3 = vocab["\n\n\n"]
+        newline_4 = vocab["\n\n\n\n"]
+
+        def get_repl_toks(tok: int) -> list[int]:
+            if tok == newline_3:
+                return [newline_1, newline_2]
+            if tok == newline_4:
+                return [newline_2, newline_2]
+
+            return [tok]
+
+        repl_token_ids = list[int]()
+        repl_orig_idxs = list[int]()
+        for orig_idx, orig_tok in enumerate(new_token_ids):
+            repl_toks = get_repl_toks(orig_tok)
+            repl_token_ids.extend(repl_toks)
+            repl_orig_idxs.extend(orig_idx for _ in range(len(repl_toks)))
+
+        repls = find_mm_placeholders(mm_prompt_updates, repl_token_ids,
+                                     mm_item_counts)
+
+        return {
+            modality: [
+                PlaceholderFeaturesInfo(
+                    modality=p.modality,
+                    item_idx=p.item_idx,
+                    start_idx=repl_orig_idxs[p.start_idx],
+                    tokens=p.tokens,
+                    is_embed=p.is_embed,
+                ) for p in placeholders
+            ]
+            for modality, placeholders in repls.items()
+        }
+
+
+class Gemma3nMultimodalEmbedder(nn.Module):
+    """Embeds token ids or soft tokens for multimodal content into language 
+    model space."""
+
+    def __init__(
+        self,
+        multimodal_config: Union[Gemma3nAudioConfig, Gemma3nVisionConfig],
+        text_config: Gemma3nTextConfig,
+    ):
+        super().__init__()
+
+        self.multimodal_hidden_size = multimodal_config.hidden_size
+        self.eps = multimodal_config.rms_norm_eps
+        self.vocab_offset = multimodal_config.vocab_offset
+        self.vocab_size = multimodal_config.vocab_size
+        self.text_hidden_size = text_config.hidden_size
+
+        self.embedding = VocabParallelEmbedding(
+            self.vocab_size,
+            self.multimodal_hidden_size,
+        )
+
+        self.hard_embedding_norm = RMSNorm(
+            self.multimodal_hidden_size,
+            eps=self.eps,
+        )
+
+        self.soft_embedding_norm = RMSNorm(
+            self.multimodal_hidden_size,
+            eps=self.eps,
+        )
+
+        self.embedding_projection = RowParallelLinear(
+            self.multimodal_hidden_size,
+            self.text_hidden_size,
+            bias=False,
+        )
+
+        self.embedding_post_projection_norm = RMSNorm(
+            self.text_hidden_size,
+            eps=self.eps,
+            has_weight=False,
+        )
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Embeds token ids or soft tokens for multimodal content into language model space.
+
+        Args:
+            input_ids: A torch.LongTensor containing the token ids to embed. Values should be in the range
+                `[vocab_offset, vocab_offset + vocab_size)`.
+            inputs_embeds: A torch.Tensor containing the soft tokens to embed.
+
+        Returns:
+            A torch.Tensor of embeddings with  shape `[batch_size, seq_len, self.config.text_config.hidden_size]`.
+        """  # noqa: E501
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is not None:
+            emb_norm = self.soft_embedding_norm(inputs_embeds)
+        else:
+            hard_emb = self.embedding(input_ids - self.vocab_offset)
+            emb_norm = self.hard_embedding_norm(hard_emb)
+
+        emb_norm_proj, _ = self.embedding_projection(emb_norm)
+        return self.embedding_post_projection_norm(emb_norm_proj)
+
+
+@MULTIMODAL_REGISTRY.register_processor(Gemma3nMultiModalProcessor,
+                                        info=Gemma3nProcessingInfo,
+                                        dummy_inputs=Gemma3nDummyInputsBuilder)
+class Gemma3nForConditionalGeneration(nn.Module, SupportsMultiModal):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            # mapping for new names in checkpoint saved after transformers v4.52
+            "model.embed_audio.": "embed_audio.",
+            "model.embed_vision.": "embed_vision.",
+            "model.language_model.": "language_model.model.",
+            "model.vision_tower.": "vision_tower.",
+            "model.audio_tower.": "audio_tower.",
+            "model.multi_modal_projector.": "multi_modal_projector.",
+            "lm_head.": "language_model.lm_head.",
+            "model": "language_model.model",
+        })
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.quant_config = quant_config
+        self.multimodal_config = multimodal_config
+        self.vocab_size = config.text_config.vocab_size
+
+        self.sliding_window = getattr(config.text_config,
+                                      "interleaved_sliding_window", None)
+
+        self.vision_tower = AutoModel.from_config(config=config.vision_config)
+        self.audio_tower = AutoModel.from_config(config=config.audio_config)
+        self.embed_vision = Gemma3nMultimodalEmbedder(config.vision_config,
+                                                      config.text_config)
+        self.embed_audio = Gemma3nMultimodalEmbedder(config.audio_config,
+                                                     config.text_config)
+
+        self.language_model: nn.Module = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=["Gemma3nForCausalLM"],
+        )
+        self.language_model = cast(Gemma3nForCausalLM, self.language_model)
+        # NOTE (NickLucche) In order to be compatible with cudagraph, the
+        # buffer needs to be consistent, so we pre-allocate here.
+        self.per_layer_embeddings = torch.zeros(
+            vllm_config.scheduler_config.max_num_batched_tokens,
+            self.config.text_config.num_hidden_layers,
+            self.config.text_config.hidden_size_per_layer_input,
+            device=self.language_model.model.embed_tokens.weight.device,
+            dtype=self.language_model.model.embed_tokens.weight.dtype)
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype
+
+    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
+        # TODO check if there are any
+        return data
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Gemma3nImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        # TODO is this the case?
+        assert image_embeds is None, "Gemma3n does not support image_embeds."
+        if pixel_values is None:
+            return None
+
+        if not isinstance(pixel_values, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of pixel values. "
+                             f"Got type: {type(pixel_values)}")
+
+        pixel_values = flatten_bn(pixel_values, concat=True)
+        pixel_values = pixel_values.contiguous()
+
+        return Gemma3nImagePixelInputs(
+            pixel_values=self._validate_pixel_values(pixel_values), )
+
+    def _parse_and_validate_audio_input(
+            self, **kwargs: object) -> Optional[Gemma3nAudioInputs]:
+        input_features = kwargs.pop("input_features", None)
+        if input_features is None:
+            return None
+
+        input_features_mask = kwargs.pop("input_features_mask", None)
+        if input_features_mask is None:
+            return None
+
+        return Gemma3nAudioInputs(
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+        )
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        mm_input_by_modality = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if input_key in ("pixel_values", "image_embeds"
+                             ) and "image" not in mm_input_by_modality:
+                mm_input_by_modality[
+                    "image"] = self._parse_and_validate_image_input(**kwargs)
+            if input_key == "input_features" \
+                and "audio" not in mm_input_by_modality:
+                mm_input_by_modality[
+                    "audio"] = self._parse_and_validate_audio_input(**kwargs)
+        return mm_input_by_modality
+
+    def _process_image_input(
+        self,
+        image_input: Gemma3nImageInputs,
+    ) -> list[torch.Tensor]:
+        assert self.vision_tower is not None
+
+        pixel_values = image_input["pixel_values"]
+        vision_outputs = self.vision_tower(pixel_values=pixel_values,
+                                           do_pooling=False,
+                                           return_dict=True).last_hidden_state
+        # TODO try to avoid copy here
+        # (batch, channels, height, width) to (batch, height * width, channels)
+        vision_outputs = vision_outputs.reshape(
+            vision_outputs.shape[0],
+            self.config.vision_config.hidden_size,
+            self.config.vision_soft_tokens_per_image,
+        ).permute(0, 2, 1).contiguous()
+        # Normalize and embed the soft tokens into language model space.
+        vision_outputs *= self.config.vision_config.hidden_size**0.5
+        # Return a list of embeddings instead of a batched tensor
+        return self.embed_vision(inputs_embeds=vision_outputs).unbind(0)
+
+    def _process_audio_input(
+        self,
+        audio_input: Gemma3nAudioInputs,
+    ) -> list[torch.Tensor]:
+        assert self.audio_tower is not None
+        input_features = audio_input["input_features"].squeeze(1)
+        input_features_mask = audio_input["input_features_mask"].squeeze(1)
+        audio_outputs, audio_mask = self.audio_tower(input_features,
+                                                     ~input_features_mask)
+        audio_features = self.embed_audio(inputs_embeds=audio_outputs)
+
+        # ruff: noqa
+        # The Gemma3nProcessor expects all audio will be 30s in length and inserts 188 audio soft tokens into the
+        # text to account for this. However, the audio preprocessing and encoder do not gurarantee they will
+        # produce 188 soft tokens; they will produce at most that many tokens, but they may produce fewer tokens
+        # depending on the length of the longest audio input in the batch. When we encounter this situation, we pad
+        # the audio feature out to 188 soft tokens with the emebedding of the last token in the embed_audio vocab.
+        # TODO precompute and cache padding
+        audio_padding_toks = torch.tensor([[self.vocab_size - 1]],
+                                          dtype=torch.long,
+                                          device=audio_features.device)
+        audio_padding_embs = self.embed_audio(input_ids=audio_padding_toks)
+        audio_features = torch.where(audio_mask.unsqueeze(-1),
+                                     audio_padding_embs, audio_features)
+
+        audio_batch_size, audio_seq_len, audio_embed_dim = audio_features.shape
+        extra_padding_tokens = self.config.audio_soft_tokens_per_image - audio_seq_len  # noqa: E501
+        extra_padding_features = audio_padding_embs.expand(
+            audio_batch_size, extra_padding_tokens, audio_embed_dim)
+
+        audio_features = torch.cat((audio_features, extra_padding_features),
+                                   dim=1)
+        # Return a list of embeddings instead of a batched tensor
+        return audio_features.unbind(0)
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        mm_input_by_modality = self._parse_and_validate_multimodal_inputs(
+            **kwargs)
+        if mm_input_by_modality is None:
+            return []
+
+        multimodal_embeddings: list[torch.Tensor] = []
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in mm_input_by_modality:
+            multimodal_input = mm_input_by_modality[modality]
+            if modality == "image":
+                vision_embeddings = self._process_image_input(multimodal_input)
+                multimodal_embeddings.extend(vision_embeddings)
+            if modality == "audio":
+                audio_embeddings = self._process_audio_input(multimodal_input)
+                multimodal_embeddings.extend(audio_embeddings)
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        # NOTE (NickLucche) Each pass needs tokens to compute PLE so we cache
+        # them here, as the model  forward has only access to the input_embeds.
+        if input_ids is not None:
+            per_layer_inputs = self.language_model.model.get_per_layer_input_embeddings(
+                input_ids)
+            per_layer_inputs = per_layer_inputs.reshape(
+                -1, self.config.text_config.num_hidden_layers,
+                self.config.text_config.hidden_size_per_layer_input)
+            self.per_layer_embeddings[:per_layer_inputs.shape[0]].copy_(
+                per_layer_inputs)
+
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                # NOTE: this order of processing mm items is important
+                [self.config.image_token_id, self.config.audio_token_id])
+        return inputs_embeds
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs: object) -> IntermediateTensors:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE (NickLucche) During profiling, `get_input_embeddings` is not
+        # called, hence we don't have input_ids to compute PLEs. We simply
+        # select a chunk of pre-allocated PLEs. During normal execution,
+        # `get_input_embeddings` is called before forward, hence this slice
+        # will contain PLEs computed from the actual input_ids.
+        per_layer_inputs = self.per_layer_embeddings[:inputs_embeds.shape[0]]
+
+        hidden_states = self.language_model.model(
+            input_ids,
+            positions,
+            per_layer_inputs=per_layer_inputs,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+            **kwargs)
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="multi_modal_projector",
+            tower_model="vision_tower")
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality == "image":
+            return "<image_soft_token>"
+        elif modality == "audio":
+            return "<audio_soft_token>"
+        else:
+            raise ValueError(f"Unsupported modality: {modality}")
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm.py
new file mode 100644
index 0000000000000000000000000000000000000000..defa77b84e44153f330f367bf8d9e6b954ecfeee
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm.py
@@ -0,0 +1,23 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only HF format GLM-4 model compatible with THUDM weights."""
+from vllm.config import VllmConfig
+from vllm.model_executor.models.llama import LlamaForCausalLM
+
+from .utils import PPMissingLayer
+
+
+class GlmForCausalLM(LlamaForCausalLM):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        vllm_config.model_config.hf_config.partial_rotary_factor = 0.5
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        # Hack Llama model to fit HF format GLM implementation
+        # Attention difference between GLM and Llama:
+        # 1. Half partial rotary_dim and no Neox style.
+        # 2. There is no bias for o_proj in attention
+        for layer in self.model.layers:
+            if not isinstance(layer, PPMissingLayer):
+                layer.self_attn.rotary_emb.is_neox_style = False
+                layer.self_attn.o_proj.bias = None
+                layer.self_attn.o_proj.skip_bias_add = True
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm4.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm4.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e2908a82c41858874a92053039546046dfab73f
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm4.py
@@ -0,0 +1,305 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 The Zhipu AI team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only GLM-4-0414 model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import Glm4Config
+
+from vllm.attention import Attention, AttentionType
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .llama import LlamaMLP as Glm4MLP
+from .llama import LlamaModel
+from .utils import AutoWeightsLoader, PPMissingLayer, maybe_prefix
+
+
+class Glm4Attention(nn.Module):
+
+    def __init__(self,
+                 config: Glm4Config,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 max_position: int = 4096 * 32,
+                 head_dim: Optional[int] = None,
+                 qkv_bias: bool = False,
+                 rope_theta: float = 10000,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 rope_scaling: Optional[tuple] = None,
+                 prefix: str = "",
+                 attn_type: str = AttentionType.DECODER) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5)
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim or hidden_size // self.total_num_heads
+        self.rotary_dim = self.head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=qkv_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.rotary_dim,
+            max_position=max_position,
+            base=self.rope_theta,
+            rope_scaling=rope_scaling,
+            partial_rotary_factor=partial_rotary_factor,
+            is_neox_style=False,
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn",
+                              attn_type=attn_type)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Glm4DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Glm4Config,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 1000000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+
+        self.self_attn = Glm4Attention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            qkv_bias=getattr(config, 'attention_bias', False),
+            head_dim=getattr(config, 'head_dim', None),
+            cache_config=cache_config,
+            quant_config=quant_config,
+            rope_scaling=rope_scaling,
+            prefix=f"{prefix}.self_attn",
+            attn_type=AttentionType.DECODER,
+        )
+        self.mlp = Glm4MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+        self.post_self_attn_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+        self.post_mlp_layernorm = RMSNorm(config.hidden_size,
+                                          eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        hidden_states = self.post_self_attn_layernorm(hidden_states)
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.post_mlp_layernorm(hidden_states)
+
+        return hidden_states, residual
+
+
+ALL_DECODER_LAYER_TYPES = {
+    "attention": Glm4DecoderLayer,
+}
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+    })
+class Glm4Model(LlamaModel):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config,
+                         prefix=prefix,
+                         layer_type=Glm4DecoderLayer)
+
+
+class Glm4ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.lora_config = lora_config
+
+        self.quant_config = quant_config
+        self.model = Glm4Model(vllm_config=vllm_config,
+                               prefix=maybe_prefix(prefix, "model"))
+
+        if get_pp_group().is_last_rank:
+            if config.tie_word_embeddings:
+                self.lm_head = self.model.embed_tokens
+            else:
+                self.lm_head = ParallelLMHead(config.vocab_size,
+                                              config.hidden_size,
+                                              quant_config=quant_config,
+                                              prefix=maybe_prefix(
+                                                  prefix, "lm_head"))
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm4_1v.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm4_1v.py
new file mode 100644
index 0000000000000000000000000000000000000000..88c53c8363275ee6d6808c4dfa77d71023ce30a9
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm4_1v.py
@@ -0,0 +1,1595 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/Glm4v/modeling_Glm4v.py
+# Copyright 2025 The vLLM team.
+# Copyright 2025 The ZhipuAI Team.
+# Copyright 2025 The HuggingFace Inc. team.
+# All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only GLM-4V model compatible with HuggingFace weights."""
+
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from functools import partial
+from typing import Annotated, Any, Callable, Literal, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from transformers import BatchFeature
+from transformers.models.glm4v.configuration_glm4v import Glm4vVisionConfig
+from transformers.models.glm4v.image_processing_glm4v import (
+    Glm4vImageProcessor, smart_resize)
+from transformers.models.glm4v.video_processing_glm4v import (
+    Glm4vVideoProcessor)
+from transformers.video_utils import VideoMetadata
+
+from vllm.config import VllmConfig
+from vllm.distributed import parallel_state
+from vllm.distributed import utils as dist_utils
+from vllm.logger import init_logger
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs, VideoItem)
+from vllm.multimodal.parse import (ImageSize, MultiModalDataItems,
+                                   MultiModalDataParser)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.platforms import _Backend
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.config import uses_mrope
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from ..layers.activation import SiluAndMul
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
+from .qwen2_vl import _qwen2vl_field_config, apply_rotary_pos_emb_vision
+from .utils import (AutoWeightsLoader, WeightsMapper,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+from .vision import get_vit_attn_backend
+
+logger = init_logger(__name__)
+
+# For profile run
+_MAX_FRAMES_PER_VIDEO = 600
+
+# === Vision Inputs === #
+
+
+class Glm4vImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - np: Number of patches
+        - cpp: Number of channels * patch_size * patch_size
+        - ni: Number of images
+        - g: Grid dimensions (3 for grid_t, grid_h, grid_w)
+    """
+    type: Literal["pixel_values"] = "pixel_values"
+
+    pixel_values: Annotated[torch.Tensor, TensorShape("np", "cpp")]
+    image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]
+
+
+class Glm4vImageEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - f: Number of image features (varies based on image resolution)
+        - h: Hidden size (must match language model backbone)
+        - n: Number of images
+        - g: Grid dimensions (3 for grid_t, grid_h, grid_w)
+    """
+    type: Literal["image_embeds"] = "image_embeds"
+
+    image_embeds: Annotated[torch.Tensor, TensorShape("f", "h")]
+    image_grid_thw: Annotated[torch.Tensor, TensorShape("n", 3)]
+
+
+Glm4vImageInputs = Union[Glm4vImagePixelInputs, Glm4vImageEmbeddingInputs]
+
+
+class Glm4vVideoPixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - np: Number of patches
+        - ctpp: Number of channels * temporal_patch_size *
+            patch_size * patch_size
+        - f: Number of frames
+        - g: Grid dimensions (3 for grid_t which is usually 1 for processed 
+          video, grid_h, grid_w)
+    """
+    type: Literal["pixel_values_videos"] = "pixel_values_videos"
+
+    pixel_values_videos: Annotated[torch.Tensor, TensorShape("np", "ctpp")]
+    video_grid_thw: Annotated[torch.Tensor, TensorShape("f", 3)]
+
+
+class Glm4vVideoEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - p: Number of video patches across all frames
+        - h: Hidden size (must match language model backbone)
+        - f: Number of frames
+        - g: Grid dimensions (3 for grid_t which is usually 1 for processed 
+          video, grid_h, grid_w)
+    """
+    type: Literal["video_embeds"] = "video_embeds"
+
+    video_embeds: Annotated[torch.Tensor, TensorShape("p", "h")]
+    video_grid_thw: Annotated[torch.Tensor, TensorShape("f", 3)]
+
+
+Glm4vVideoInputs = Union[Glm4vVideoPixelInputs, Glm4vVideoEmbeddingInputs]
+
+# === Vision Encoder === #
+
+
+class Glm4vVisionMLP(nn.Module):
+
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int,
+        bias: bool = False,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=in_features,
+            output_sizes=[hidden_features] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(hidden_features,
+                                           in_features,
+                                           bias=bias,
+                                           quant_config=quant_config,
+                                           prefix=f"{prefix}.down_proj")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x: torch.Tensor):
+        x, _ = self.gate_up_proj(x)
+        x = self.act_fn(x)
+        x, _ = self.down_proj(x)
+        return x
+
+
+def all_gather_interleave(local_tensor, hidden_size: int, tp_size: int):
+    """All-gather the input tensor interleavely across model parallel group."""
+    import torch.distributed as dist
+
+    gathered_tensors = [torch.zeros_like(local_tensor) for _ in range(tp_size)]
+    dist.all_gather(
+        gathered_tensors,
+        local_tensor,
+        group=parallel_state.get_tp_group().device_group,
+    )
+
+    gathered_tensors_split = [
+        torch.split(tensor, hidden_size // tp_size, -1)
+        for tensor in gathered_tensors
+    ]
+    ordered_tensors = [
+        tensor for pair in zip(*gathered_tensors_split) for tensor in pair
+    ]
+    result_tensor = torch.cat(ordered_tensors, dim=-1)
+    return result_tensor
+
+
+class Glm4vVisionAttention(nn.Module):
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        projection_size: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        # Per attention head and per partition values.
+        self.tp_size = parallel_state.get_tensor_model_parallel_world_size()
+        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            projection_size, num_heads)
+        self.num_attention_heads_per_partition = dist_utils.divide(
+            num_heads, self.tp_size)
+
+        self.qkv = QKVParallelLinear(
+            hidden_size=embed_dim,
+            head_size=self.hidden_size_per_attention_head,
+            total_num_heads=num_heads,
+            total_num_kv_heads=num_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv",
+        )
+        self.proj = RowParallelLinear(
+            input_size=projection_size,
+            output_size=embed_dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.proj",
+            bias=False,
+        )
+
+        # Detect attention implementation.
+        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
+        if self.attn_backend not in {
+                _Backend.FLASH_ATTN,
+                _Backend.TORCH_SDPA,
+                _Backend.XFORMERS,
+        }:
+            raise RuntimeError(
+                f"GLM-4V does not support {self.attn_backend} backend now.")
+
+    def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
+        # [s, b, 3 * head * head_dim]
+        seq_len, bs, _ = qkv.shape
+        if self.tp_size > 1:
+            qkv = all_gather_interleave(qkv, self.qkv.hidden_size,
+                                        self.tp_size)
+
+        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head * head_dim]
+        q, k, v = qkv.chunk(3, dim=2)
+
+        # 3 * [s, b, head * head_dim]
+        if self.tp_size > 1:
+            splitter = partial(
+                dist_utils.split_tensor_along_last_dim,
+                num_partitions=self.tp_size,
+            )
+            q = splitter(q)[self.tp_rank]
+            k = splitter(k)[self.tp_rank]
+            v = splitter(v)[self.tp_rank]
+
+        # 3 * [s, b, head * head_dim] -> 3 * [s, b, head, head_dim]
+        new_shape = (
+            seq_len,
+            bs,
+            self.num_attention_heads_per_partition,
+            self.hidden_size_per_attention_head,
+        )
+        q, k, v = (x.view(*new_shape) for x in (q, k, v))
+        return q, k, v
+
+    def forward(
+            self,
+            x: torch.Tensor,
+            cu_seqlens: torch.Tensor,
+            rotary_pos_emb: torch.Tensor,
+            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
+            seqlens: Optional[list[int]] = None,  # Only used for xFormers
+    ) -> torch.Tensor:
+        # [s, b, c] --> [s, b, head * 3 * head_dim]
+        x, _ = self.qkv(x)
+
+        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head, head_dim]
+        q, k, v = self.split_qkv(x)
+        batch_size = q.shape[1]
+
+        q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous()
+                   for x in (q, k, v))
+        if rotary_pos_emb is not None:
+            q = apply_rotary_pos_emb_vision(q, rotary_pos_emb)
+            k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)
+
+        if self.attn_backend == _Backend.FLASH_ATTN:
+            # from vllm_flash_attn.flash_attn_interface import (
+            #   flash_attn_varlen_func)
+            from flash_attn import flash_attn_varlen_func
+
+            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
+
+            output = flash_attn_varlen_func(
+                q,
+                k,
+                v,
+                cu_seqlens_q=cu_seqlens,
+                cu_seqlens_k=cu_seqlens,
+                max_seqlen_q=max_seqlen,
+                max_seqlen_k=max_seqlen,
+                dropout_p=0,
+                causal=False,
+            )
+
+            context_layer = rearrange(output,
+                                      "(b s) ... -> b s ...",
+                                      b=batch_size)
+        elif self.attn_backend == _Backend.TORCH_SDPA:
+            # Execute attention entry by entry for speed & less VRAM.
+            outputs = []
+            for i in range(1, len(cu_seqlens)):
+                start_idx = cu_seqlens[i - 1]
+                end_idx = cu_seqlens[i]
+                q_i = q[:, start_idx:end_idx]
+                k_i = k[:, start_idx:end_idx]
+                v_i = v[:, start_idx:end_idx]
+                q_i, k_i, v_i = (rearrange(x, "b s h d -> b h s d")
+                                 for x in [q_i, k_i, v_i])
+                output_i = F.scaled_dot_product_attention(q_i,
+                                                          k_i,
+                                                          v_i,
+                                                          dropout_p=0.0)
+                output_i = rearrange(output_i, "b h s d -> b s h d ")
+                outputs.append(output_i)
+            context_layer = torch.cat(outputs, dim=1)
+        elif self.attn_backend == _Backend.XFORMERS:
+            from xformers import ops as xops
+            from xformers.ops.fmha.attn_bias import BlockDiagonalMask
+
+            attn_bias = BlockDiagonalMask.from_seqlens(q_seqlen=seqlens,
+                                                       kv_seqlen=None,
+                                                       device=q.device)
+
+            context_layer = xops.memory_efficient_attention_forward(
+                q, k, v, attn_bias=attn_bias, p=0, scale=None)
+
+        context_layer = rearrange(context_layer,
+                                  "b s h d -> s b (h d)").contiguous()
+
+        output, _ = self.proj(context_layer)
+        return output
+
+
+class Glm4vVisionBlock(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_hidden_dim: int,
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.norm1 = norm_layer(dim)
+        self.norm2 = norm_layer(dim)
+        self.attn = Glm4vVisionAttention(
+            embed_dim=dim,
+            num_heads=num_heads,
+            projection_size=dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+        self.mlp = Glm4vVisionMLP(
+            dim,
+            mlp_hidden_dim,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+    def forward(
+            self,
+            x: torch.Tensor,
+            cu_seqlens: torch.Tensor,
+            rotary_pos_emb: torch.Tensor,
+            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
+            seqlens: Optional[list[int]] = None,  # Only used for xFormers
+    ) -> torch.Tensor:
+        x = x + self.attn(
+            self.norm1(x),
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb=rotary_pos_emb,
+            max_seqlen=max_seqlen,
+            seqlens=seqlens,
+        )
+
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class Glm4vVisionPatchEmbed(nn.Module):
+
+    def __init__(
+        self,
+        patch_size: int = 14,
+        temporal_patch_size: int = 1,
+        in_channels: int = 3,
+        hidden_size: int = 1536,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.hidden_size = hidden_size
+
+        kernel_size = (temporal_patch_size, patch_size, patch_size)
+        self.proj = nn.Conv3d(
+            in_channels,
+            hidden_size,
+            kernel_size=kernel_size,
+            stride=kernel_size,
+            bias=True,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        L, C = x.shape
+        x = x.view(L, -1, self.temporal_patch_size, self.patch_size,
+                   self.patch_size)
+        x = self.proj(x).view(L, self.hidden_size)
+        return x
+
+
+class Glm4vPatchMerger(nn.Module):
+
+    def __init__(
+        self,
+        d_model: int,
+        context_dim: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = d_model
+        self.proj = ColumnParallelLinear(self.hidden_size,
+                                         self.hidden_size,
+                                         bias=bias,
+                                         gather_output=True,
+                                         quant_config=quant_config,
+                                         prefix=f"{prefix}.proj")
+        self.post_projection_norm = nn.LayerNorm(self.hidden_size)
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_sizes=[context_dim] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            context_dim,
+            self.hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+        self.act_fn = SiluAndMul()
+        self.extra_activation_func = nn.GELU()
+
+    def forward(self, x: torch.Tensor):
+        x, _ = self.proj(x)
+        x = self.extra_activation_func(self.post_projection_norm(x))
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Glm4vVisionEmbeddings(nn.Module):
+
+    def __init__(self, config: Glm4vVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.num_patches = (self.image_size // self.patch_size)**2
+        self.num_positions = self.num_patches
+        self.position_embedding = nn.Embedding(self.num_positions,
+                                               self.embed_dim)
+        self.register_buffer(
+            "position_ids",
+            torch.arange(self.num_positions).expand((1, -1)),
+            persistent=False,
+        )
+
+    def forward(self, embeddings, lengths, image_shapes, h_coords,
+                w_coords) -> torch.Tensor:
+        pos_embed_weight = self.position_embedding.weight
+        hidden_size = pos_embed_weight.shape[1]
+        total_seq = h_coords.shape[0]
+        device = pos_embed_weight.device
+
+        # Move coordinates to correct device
+        h_coords, w_coords = h_coords.to(device), w_coords.to(device)
+
+        # Handle empty sequence case
+        if total_seq == 0:
+            adapted_pos_embed = torch.empty(0,
+                                            hidden_size,
+                                            device=device,
+                                            dtype=pos_embed_weight.dtype)
+        else:
+            # Convert inputs to tensors if needed
+            if isinstance(lengths, list):
+                lengths = torch.tensor(lengths,
+                                       device=device,
+                                       dtype=torch.long)
+            if not isinstance(image_shapes, torch.Tensor):
+                image_shapes = torch.tensor(image_shapes,
+                                            device=device,
+                                            dtype=torch.long)
+
+            # Prepare 2D position embedding
+            orig_size_sq = pos_embed_weight.shape[0]
+            orig_size = int(orig_size_sq**0.5)
+            pos_embed_2d = (pos_embed_weight.view(
+                orig_size, orig_size,
+                hidden_size).permute(2, 0,
+                                     1).unsqueeze(0).to(device=device,
+                                                        dtype=torch.float32))
+
+            # Calculate target dimensions for each patch
+            target_h = torch.cat([
+                image_shapes[i, 1].repeat(lengths[i])
+                for i in range(len(lengths))
+            ]).to(device=device, dtype=torch.float32)
+            target_w = torch.cat([
+                image_shapes[i, 2].repeat(lengths[i])
+                for i in range(len(lengths))
+            ]).to(device=device, dtype=torch.float32)
+
+            # Normalize coordinates to [-1, 1] range for grid_sample
+            h_coords = h_coords.to(device=device, dtype=torch.float32)
+            w_coords = w_coords.to(device=device, dtype=torch.float32)
+            norm_w = ((w_coords + 0.5) / target_w) * 2 - 1
+            norm_h = ((h_coords + 0.5) / target_h) * 2 - 1
+
+            # Create sampling grid
+            grid = (torch.stack((norm_w, norm_h),
+                                dim=-1).unsqueeze(0).unsqueeze(2))
+
+            # Perform bicubic interpolation
+            interpolated_embed_fp32 = F.grid_sample(
+                pos_embed_2d,
+                grid,
+                mode="bicubic",
+                align_corners=False,
+                padding_mode="border",
+            )
+
+            # Reshape and convert back to original dtype
+            adapted_pos_embed_fp32 = (
+                interpolated_embed_fp32.squeeze(0).squeeze(-1).permute(1, 0))
+            adapted_pos_embed = adapted_pos_embed_fp32.to(
+                pos_embed_weight.dtype).to(embeddings.device)
+
+        # Add adapted position encoding to embeddings
+        embeddings = embeddings + adapted_pos_embed
+        return embeddings
+
+
+class Glm4vVisionRotaryEmbedding(nn.Module):
+
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        inv_freq = 1.0 / (theta
+                          **(torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._freqs_cached = None
+
+    def update_freqs_cache(self, seqlen: int) -> None:
+        if seqlen > self._seq_len_cached:
+            seqlen *= 2
+            self._seq_len_cached = seqlen
+            self.inv_freq = 1.0 / (self.theta**(torch.arange(
+                0,
+                self.dim,
+                2,
+                dtype=torch.float,
+                device=self.inv_freq.device,
+            ) / self.dim))
+            seq = torch.arange(seqlen,
+                               device=self.inv_freq.device,
+                               dtype=self.inv_freq.dtype)
+            freqs = torch.outer(seq, self.inv_freq)
+            self._freqs_cached = freqs
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        self.update_freqs_cache(seqlen)
+        return self._freqs_cached[:seqlen]
+
+
+class Glm4vVisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        vision_config: Glm4vVisionConfig,
+        norm_eps: float = 1e-6,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        patch_size = vision_config.patch_size
+        temporal_patch_size = vision_config.temporal_patch_size
+        in_channels = vision_config.in_channels
+        depth = vision_config.depth
+        self.hidden_size = vision_config.hidden_size
+        self.num_heads = vision_config.num_heads
+
+        self.patch_size = vision_config.patch_size
+        self.spatial_merge_size = vision_config.spatial_merge_size
+        self.out_hidden_size = vision_config.out_hidden_size
+
+        self.patch_embed = Glm4vVisionPatchEmbed(
+            patch_size=patch_size,
+            temporal_patch_size=temporal_patch_size,
+            in_channels=in_channels,
+            hidden_size=self.hidden_size,
+        )
+
+        norm_layer = partial(RMSNorm, eps=norm_eps)
+        head_dim = self.hidden_size // self.num_heads
+        self.rotary_pos_emb = Glm4vVisionRotaryEmbedding(head_dim // 2)
+        self.blocks = nn.ModuleList([
+            Glm4vVisionBlock(
+                dim=self.hidden_size,
+                num_heads=self.num_heads,
+                mlp_hidden_dim=vision_config.out_hidden_size,
+                norm_layer=norm_layer,
+                quant_config=quant_config,
+                prefix=f"{prefix}.blocks.{layer_idx}",
+            ) for layer_idx in range(depth)
+        ])
+        self.merger = Glm4vPatchMerger(
+            d_model=vision_config.out_hidden_size,
+            context_dim=vision_config.intermediate_size,
+            quant_config=quant_config,
+            bias=False,
+            prefix=f"{prefix}.merger",
+        )
+        self.embeddings = Glm4vVisionEmbeddings(vision_config)
+
+        self.post_conv_layernorm = RMSNorm(vision_config.hidden_size,
+                                           eps=vision_config.rms_norm_eps)
+        self.downsample = nn.Conv2d(
+            in_channels=vision_config.hidden_size,
+            out_channels=vision_config.out_hidden_size,
+            kernel_size=vision_config.spatial_merge_size,
+            stride=vision_config.spatial_merge_size,
+        )
+        self.post_layernorm = RMSNorm(vision_config.hidden_size,
+                                      eps=vision_config.rms_norm_eps)
+
+        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.patch_embed.proj.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.patch_embed.proj.weight.device
+
+    def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
+        pos_ids = []
+        for t, h, w in grid_thw:
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            hpos_ids = (hpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            ).permute(0, 2, 1, 3).flatten())
+            wpos_ids = (wpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            ).permute(0, 2, 1, 3).flatten())
+            pos_ids.append(
+                torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        pos_ids = torch.cat(pos_ids, dim=0)
+        max_grid_size = grid_thw[:, 1:].max()
+        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+        return rotary_pos_emb, pos_ids
+
+    def compute_attn_mask_seqlen(
+        self,
+        cu_seqlens: torch.Tensor,
+    ) -> tuple[Optional[int], Optional[list[int]]]:
+        max_seqlen, seqlens = None, None
+        seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+        if self.attn_backend == _Backend.FLASH_ATTN:
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
+        return max_seqlen, seqlens
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        grid_thw: torch.Tensor,
+    ) -> torch.Tensor:
+        # patchify
+        x = x.to(device=self.device, dtype=self.dtype)
+        x = self.patch_embed(x)
+        x = self.post_conv_layernorm(x)
+
+        # compute position embedding
+        rotary_pos_emb, image_type_ids = self.rot_pos_emb(grid_thw)
+        # compute cu_seqlens
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2],
+                                             grid_thw[:, 0]).cumsum(
+                                                 dim=0, dtype=torch.int32)
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
+
+        # pre-compute seqlens for attn mask to reduce cuMemcpy operations
+        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+        x = self.embeddings(x, seqlens, grid_thw, image_type_ids[:, 0],
+                            image_type_ids[:, 1])
+
+        # transformers
+        x = x.unsqueeze(1)
+        for blk in self.blocks:
+            x = blk(
+                x,
+                cu_seqlens=cu_seqlens,
+                rotary_pos_emb=rotary_pos_emb,
+                max_seqlen=max_seqlen,
+                seqlens=seqlens,
+            )
+
+        # adapter
+        x = self.post_layernorm(x)
+
+        x = x.view(-1, self.spatial_merge_size, self.spatial_merge_size,
+                   x.shape[-1])
+        x = x.permute(0, 3, 1, 2)
+        x = self.downsample(x).view(-1, self.out_hidden_size)
+        x = self.merger(x)
+
+        return x
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("attn.qkv.", "attn.q.", "q"),
+            ("attn.qkv.", "attn.k.", "k"),
+            ("attn.qkv.", "attn.v.", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class Glm4vProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config()
+
+    def get_tokenizer(self):
+        return self.ctx.tokenizer
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None, "video": 1}
+
+    def get_image_processor(self, **kwargs: object) -> Glm4vImageProcessor:
+        return self.get_hf_processor(**kwargs).image_processor
+
+    def get_video_processor(self, **kwargs: object) -> Glm4vVideoProcessor:
+        return self.get_hf_processor(**kwargs).video_processor
+
+    def _get_vision_info(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int = 16,
+        do_resize: bool = True,
+        max_image_pixels: int = 28 * 28 * 2 * 30000,
+    ) -> tuple[ImageSize, int]:
+        hf_config = self.get_hf_config()
+        vision_config = hf_config.vision_config
+        patch_size = vision_config.patch_size
+        merge_size = vision_config.spatial_merge_size
+        temporal_patch_size = vision_config.temporal_patch_size
+        if do_resize:
+            resized_height, resized_width = smart_resize(
+                num_frames=num_frames
+                if num_frames > temporal_patch_size else temporal_patch_size,
+                height=image_height,
+                width=image_width,
+                factor=patch_size * merge_size,
+                max_pixels=max_image_pixels,
+            )
+            preprocessed_size = ImageSize(width=resized_width,
+                                          height=resized_height)
+        else:
+            preprocessed_size = ImageSize(width=image_width,
+                                          height=image_height)
+
+        # NOTE: Frames are padded to be divisible by `temporal_patch_size`
+        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L294
+        padded_num_frames = num_frames + num_frames % temporal_patch_size
+
+        grid_t = max(padded_num_frames // temporal_patch_size, 1)
+        grid_h = preprocessed_size.height // patch_size
+        grid_w = preprocessed_size.width // patch_size
+
+        num_patches = grid_t * grid_h * grid_w
+        num_vision_tokens = num_patches // (merge_size**2)
+
+        return preprocessed_size, num_vision_tokens
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        max_image_size, _ = self._get_vision_info(image_width=9999999,
+                                                  image_height=9999999)
+        return max_image_size
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        _, num_image_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            max_image_pixels=28 * 28 * 2 * 6144,
+        )
+        return num_image_tokens
+
+    def get_max_image_tokens(self) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_image_tokens(
+            image_width=target_width,
+            image_height=target_height,
+        )
+
+    def get_num_video_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int,
+    ) -> int:
+        _, num_video_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            num_frames=num_frames,
+            max_image_pixels=28 * 28 * 2 * 30000,
+        )
+        return num_video_tokens
+
+    def _get_max_video_frames(self, max_tokens: int) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        num_frames = 0
+
+        while True:
+            next_num_frames = num_frames + 1
+            next_max_tokens = self.get_num_video_tokens(
+                image_width=target_width,
+                image_height=target_height,
+                num_frames=next_num_frames,
+            )
+            if next_max_tokens > max_tokens or next_max_tokens == 0:
+                break
+
+            num_frames = next_num_frames
+
+        return num_frames
+
+    def get_num_frames_with_most_features(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        max_images = mm_counts.get("image", 0)
+        max_videos = mm_counts.get("video", 0)
+
+        max_image_tokens = self.get_max_image_tokens() * max_images
+        max_total_frames = self._get_max_video_frames(seq_len -
+                                                      max_image_tokens)
+        max_frames_per_video = min(max_total_frames // max(max_videos, 1),
+                                   _MAX_FRAMES_PER_VIDEO)
+
+        return max(max_frames_per_video, 1)
+
+    def _get_video_second_idx(self, metadata: dict[str, Any],
+                              total_frames: int) -> list[int]:
+        video_processor = self.get_video_processor()
+
+        video_fps = metadata.get("fps", video_processor.fps)
+        meta_frames = metadata.get("total_num_frames", total_frames)
+        max_frame_idx = meta_frames - 1
+        duration = metadata.get("duration",
+                                round(max_frame_idx / video_fps) + 1)
+        if duration <= video_processor.max_duration:
+            n = int(math.floor(duration * video_processor.fps))
+            frame_indices = [
+                min(
+                    max_frame_idx,
+                    int(math.ceil(i * video_fps / video_processor.fps)),
+                ) for i in range(n)
+            ]
+        else:
+            num_samples = int(video_processor.max_duration *
+                              video_processor.fps)
+            if num_samples >= meta_frames:
+                frame_indices = list(range(meta_frames))
+            else:
+                target_seconds = np.linspace(0,
+                                             duration,
+                                             num_samples,
+                                             endpoint=True)
+                frame_indices = [
+                    min(max_frame_idx, int(math.ceil(t * video_fps)))
+                    for t in target_seconds
+                ]
+
+        seen, uniq = set(), []
+        for idx in frame_indices:
+            if idx not in seen:
+                seen.add(idx)
+                uniq.append(idx)
+        if len(uniq) & 1:
+            uniq.append(uniq[-1])
+        frame_indices = uniq
+
+        full_second_idxs = [int(idx / video_fps) for idx in frame_indices]
+        timestamps_list = full_second_idxs[::2]
+        selected_timestamps = []
+        for idx in range(0, len(timestamps_list)):
+            selected_timestamps.append(timestamps_list[idx])
+        return selected_timestamps
+
+
+class Glm4vDummyInputsBuilder(BaseDummyInputsBuilder[Glm4vProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        hf_config = self.info.get_hf_config()
+        hf_processor = self.info.get_hf_processor()
+        tokenizer = self.info.get_tokenizer()
+
+        image_token: str = hf_processor.image_token
+        video_token_ids = [
+            hf_config.video_start_token_id,
+            hf_processor.video_token_id,
+            hf_config.video_end_token_id,
+        ]
+        video_token = tokenizer.decode(video_token_ids)
+
+        return image_token * num_images + video_token * num_videos
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        target_width, target_height = (
+            self.info.get_image_size_with_most_features())
+        target_num_frames = self.info.get_num_frames_with_most_features(
+            seq_len, mm_counts)
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images),
+            "video":
+            self._get_dummy_videos(
+                width=target_width,
+                height=target_height,
+                num_frames=target_num_frames,
+                num_videos=num_videos,
+            ),
+        }
+
+    def _get_dummy_videos(
+        self,
+        *,
+        width: int,
+        height: int,
+        num_frames: int,
+        num_videos: int,
+    ) -> list[VideoItem]:
+        video = np.full((num_frames, width, height, 3), 255, dtype=np.uint8)
+        video_items = []
+        for i in range(num_videos):
+            video_metadata = {
+                "fps": 2.0,
+                "duration": num_frames / 2.0,
+                "total_num_frames": num_frames,
+                "video_backend": "opencv",
+            }
+            video_item = (video.copy(), video_metadata)
+            video_items.append(video_item)
+
+        return video_items
+
+
+class Glm4vMultiModalProcessor(BaseMultiModalProcessor[Glm4vProcessingInfo]):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        return MultiModalDataParser(video_needs_metadata=True)
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        mm_data = dict(mm_data)
+        processor = self.info.get_hf_processor(**mm_kwargs)
+
+        # GLM-4.1V use `image_token_id` as video placeholder, we need to
+        # replace it with `video_token_id` for video processing. So we
+        # separate video processing from image processing.
+        if ("videos" in mm_data and isinstance(mm_data["videos"], list)
+                and len(mm_data["videos"]) > 0):
+            video_grid_thw_lst = []
+            pixel_values_videos_lst = []
+            for item in mm_data.pop("videos", []):
+                video_array, metadata = item
+
+                # FIXME(Isotr0py): Activate the below logic after we can disable
+                # resampling from video loader backend.
+                # assert metadata["total_num_frames"] == len(video_array), (
+                #     f"Total frames {metadata['total_num_frames']} does not "
+                #     f"match the length of video array {len(video_array)}.")
+
+                # NOTE: Temporary workaround for resampled videos.
+                # this can cause a divergence with HF implementation if
+                # the input video is resampled in advance.
+
+                if metadata["total_num_frames"] != len(video_array):
+                    logger.warning(
+                        "Total frames in metadata "
+                        "(%s) does not match the length of "
+                        "video array %s. This can "
+                        "be because the video is resampled "
+                        "in advance. This may cause "
+                        "a divergence with HF implementation.",
+                        metadata["total_num_frames"],
+                        len(video_array),
+                    )
+                    metadata["total_num_frames"] = len(video_array)
+                metadata = VideoMetadata(**metadata)
+
+                video_mm_data = dict()
+                video_mm_data["videos"] = [[video_array]]
+                video_mm_data["video_metadata"] = [[metadata]]
+
+                video_outputs = super()._call_hf_processor(
+                    prompt="<|begin_of_video|><|video|><|end_of_video|>",
+                    mm_data=video_mm_data,
+                    mm_kwargs=mm_kwargs,
+                    tok_kwargs=tok_kwargs,
+                )
+                input_ids = video_outputs.pop("input_ids")
+                input_ids[input_ids == processor.image_token_id] = (
+                    processor.video_token_id)
+                video_placeholder = processor.tokenizer.batch_decode(
+                    input_ids)[0]
+                prompt = prompt.replace(
+                    "<|begin_of_video|><|video|><|end_of_video|>",
+                    video_placeholder,
+                )
+
+                video_grid_thw_lst.append(video_outputs["video_grid_thw"])
+                pixel_values_videos_lst.append(
+                    video_outputs["pixel_values_videos"])
+            video_outputs = dict(
+                pixel_values_videos=torch.cat(pixel_values_videos_lst),
+                video_grid_thw=torch.cat(video_grid_thw_lst),
+            )
+        else:
+            video_outputs = dict()
+
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+        combined_outputs = dict(
+            processed_outputs,
+            **video_outputs,
+        )
+        return BatchFeature(combined_outputs)
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return _qwen2vl_field_config(hf_inputs)
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_processor = self.info.get_image_processor(
+            **hf_processor_mm_kwargs)
+        tokenizer = self.info.get_tokenizer()
+        hf_config = self.info.get_hf_config()
+
+        boi_token_id = hf_config.image_start_token_id
+        eoi_token_id = hf_config.image_end_token_id
+
+        bov_token_id = hf_config.video_start_token_id
+        eov_token_id = hf_config.video_end_token_id
+
+        merge_length = image_processor.merge_size**2
+
+        def get_image_replacement_glm4v(item_idx: int):
+            grid_thw = out_mm_kwargs["image_grid_thw"][item_idx]
+            assert isinstance(grid_thw, torch.Tensor)
+
+            num_tokens = int(grid_thw.prod()) // merge_length
+            return [hf_processor.image_token_id] * num_tokens
+
+        def get_video_replacement_glm4v(item_idx: int):
+            grid_thw = out_mm_kwargs["video_grid_thw"][item_idx]
+            assert isinstance(grid_thw, torch.Tensor)
+
+            video, metadata = mm_items["video"][item_idx]
+            timestamps = self.info._get_video_second_idx(metadata, len(video))
+            frames_idx_token = [
+                tokenizer.encode(str(i), add_special_tokens=False)
+                for i in timestamps
+            ]
+            num_tokens_per_frame = int(grid_thw[1:].prod()) // merge_length
+            placeholder = []
+            placeholder.append(bov_token_id)
+            for frame_idx in frames_idx_token:
+                placeholder.append(boi_token_id)
+                placeholder.extend([hf_processor.video_token_id] *
+                                   num_tokens_per_frame)
+                placeholder.append(eoi_token_id)
+                placeholder.extend(frame_idx)
+            placeholder.append(eov_token_id)
+            return PromptUpdateDetails.select_token_id(
+                placeholder,
+                embed_token_id=hf_processor.video_token_id,
+            )
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=hf_processor.image_token,
+                replacement=get_image_replacement_glm4v,
+            ),
+            PromptReplacement(
+                modality="video",
+                target="<|begin_of_video|><|video|><|end_of_video|>",
+                replacement=get_video_replacement_glm4v,
+            ),
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Glm4vMultiModalProcessor,
+    info=Glm4vProcessingInfo,
+    dummy_inputs=Glm4vDummyInputsBuilder,
+)
+class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                    SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": ["gate_up_proj"]
+    }
+
+    # To ensure correct weight loading and mapping.
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "lm_head.": "language_model.lm_head.",
+            "model.language_model.": "language_model.model.",
+            "model.visual.": "visual.",
+        })
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<|begin_of_image|><|image|><|end_of_image|>"
+        if modality.startswith("video"):
+            return "<|begin_of_video|><|video|><|end_of_video|>"
+
+        raise ValueError("Only image or video modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.visual = Glm4vVisionTransformer(
+            config.vision_config,
+            norm_eps=getattr(config, "rms_norm_eps", 1e-5),
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "visual"),
+        )
+
+        if config.model_type == "glm4v":
+            architectures = ["Glm4ForCausalLM"]
+        elif config.model_type == "glm4v_moe":
+            architectures = ["Glm4MoeForCausalLM"]
+        else:
+            architectures = None
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=architectures)
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _validate_and_reshape_mm_tensor(self, mm_input: object,
+                                        name: str) -> torch.Tensor:
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(
+                f"Incorrect type of {name}. Got type: {type(mm_input)}")
+        if isinstance(mm_input, torch.Tensor):
+            if mm_input.ndim == 2:
+                return mm_input
+            if mm_input.ndim != 3:
+                raise ValueError(f"{name} should be 2D or batched 3D tensor. "
+                                 f"Got ndim: {mm_input.ndim} "
+                                 f"(shape={mm_input.shape})")
+            return torch.concat(list(mm_input))
+        else:
+            return torch.concat(mm_input)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Glm4vImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            pixel_values = self._validate_and_reshape_mm_tensor(
+                pixel_values, "image pixel values")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            return Glm4vImagePixelInputs(
+                type="pixel_values",
+                pixel_values=pixel_values,
+                image_grid_thw=image_grid_thw,
+            )
+
+        if image_embeds is not None:
+            image_embeds = self._validate_and_reshape_mm_tensor(
+                image_embeds, "image embeds")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            return Glm4vImageEmbeddingInputs(
+                type="image_embeds",
+                image_embeds=image_embeds,
+                image_grid_thw=image_grid_thw,
+            )
+
+    def _parse_and_validate_video_input(
+            self, **kwargs: object) -> Optional[Glm4vVideoInputs]:
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
+        video_embeds = kwargs.pop("video_embeds", None)
+        video_grid_thw = kwargs.pop("video_grid_thw", None)
+
+        if pixel_values_videos is None and video_embeds is None:
+            return None
+
+        if pixel_values_videos is not None:
+            pixel_values_videos = self._validate_and_reshape_mm_tensor(
+                pixel_values_videos, "video pixel values")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            return Glm4vVideoPixelInputs(
+                type="pixel_values_videos",
+                pixel_values_videos=pixel_values_videos,
+                video_grid_thw=video_grid_thw,
+            )
+
+        if video_embeds is not None:
+            video_embeds = self._validate_and_reshape_mm_tensor(
+                video_embeds, "video embeds")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            return Glm4vVideoEmbeddingInputs(
+                type="video_embeds",
+                video_embeds=video_embeds,
+                video_grid_thw=video_grid_thw,
+            )
+
+    def _process_image_input(
+            self, image_input: Glm4vImageInputs) -> tuple[torch.Tensor, ...]:
+        grid_thw = image_input["image_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        if image_input["type"] == "image_embeds":
+            image_embeds = image_input["image_embeds"].type(self.visual.dtype)
+        else:
+            pixel_values = image_input["pixel_values"].type(self.visual.dtype)
+            image_embeds = self.visual(pixel_values, grid_thw=grid_thw)
+
+        merge_size = self.visual.spatial_merge_size
+        sizes = grid_thw.prod(-1) // merge_size // merge_size
+        return image_embeds.split(sizes.tolist())
+
+    def _process_video_input(
+            self, video_input: Glm4vVideoInputs) -> tuple[torch.Tensor, ...]:
+        grid_thw = video_input["video_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        device = self.visual.device
+        flat_grid_thw = torch.cat([
+            torch.tensor([[1, h, w]] * t, device=device)
+            for t, h, w in grid_thw
+        ])
+        if video_input["type"] == "video_embeds":
+            video_embeds = video_input["video_embeds"].type(self.visual.dtype)
+        else:
+            pixel_values_videos = video_input["pixel_values_videos"].type(
+                self.visual.dtype)
+            video_embeds = self.visual(pixel_values_videos,
+                                       grid_thw=flat_grid_thw)
+
+        # Split concatenated embeddings for each video item.
+        merge_size = self.visual.spatial_merge_size
+        sizes = grid_thw.prod(-1) // merge_size // merge_size
+
+        return video_embeds.split(sizes.tolist())
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        mm_input_by_modality = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if (input_key in ("pixel_values", "image_embeds")
+                    and "image" not in mm_input_by_modality):
+                mm_input_by_modality["image"] = (
+                    self._parse_and_validate_image_input(**kwargs))
+            if (input_key in ("pixel_values_videos", "video_embeds")
+                    and "video" not in mm_input_by_modality):
+                mm_input_by_modality["video"] = (
+                    self._parse_and_validate_video_input(**kwargs))
+        return mm_input_by_modality
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+        mm_input_by_modality = self._parse_and_validate_multimodal_inputs(
+            **kwargs)
+        if not mm_input_by_modality:
+            return None
+
+        # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor correspoending to a multimodal data item (image or video).
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in mm_input_by_modality:
+            multimodal_input = mm_input_by_modality[modality]
+            if modality == "image":
+                vision_embeddings = self._process_image_input(multimodal_input)
+                multimodal_embeddings += vision_embeddings
+            if modality == "video":
+                video_embeddings = self._process_video_input(multimodal_input)
+                multimodal_embeddings += video_embeddings
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if (multimodal_embeddings is not None
+                and len(multimodal_embeddings) != 0
+                and all(embed.numel() > 0 for embed in multimodal_embeddings)):
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                [self.config.image_token_id, self.config.video_token_id],
+            )
+        return inputs_embeds
+
+    def get_input_embeddings_v0(
+        self,
+        input_ids: torch.Tensor,
+        image_input: Optional[Glm4vImageInputs] = None,
+        video_input: Optional[Glm4vVideoInputs] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.get_input_embeddings(input_ids)
+        if image_input is not None:
+            image_embeds = self._process_image_input(image_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                image_embeds,
+                placeholder_token_id=self.config.image_token_id,
+            )
+
+        if video_input is not None:
+            video_embeds = self._process_video_input(video_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                video_embeds,
+                placeholder_token_id=self.config.video_token_id,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        """Run forward pass for GLM-4V.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            positions: Flattened (concatenated) position ids corresponding to a
+                batch.
+                **NOTE**: If mrope is enabled (default setting for GLM-4V
+                opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+            pixel_values: Pixel values to be fed to a model.
+                `None` if no images are passed.
+            image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
+                `None` if no images are passed.
+            pixel_values_videos: Pixel values of videos to be fed to a model.
+                `None` if no videos are passed.
+            video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
+                `None` if no videos are passed.
+            second_per_grid_ts: Tensor `(num_videos)` of video time interval (
+                in seconds) for each grid along the temporal dimension in the
+                3D position IDs. `None` if no videos are passed.
+        """
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner from
+        # `get_multimodal_embeddings` and `get_input_embeddings`, this
+        # condition is only for v0 compatibility.
+        elif inputs_embeds is None:
+            image_input = self._parse_and_validate_image_input(**kwargs)
+            video_input = self._parse_and_validate_video_input(**kwargs)
+
+            if image_input is None and video_input is None:
+                inputs_embeds = None
+            else:
+                if uses_mrope(self.config):
+                    assert positions.ndim == 2 and positions.size(0) == 3, (
+                        "multimodal section rotary embedding requires "
+                        f"(3, seq_len) positions, but got {positions.size()}")
+                inputs_embeds = self.get_input_embeddings_v0(
+                    input_ids,
+                    image_input=image_input,
+                    video_input=video_input)
+                input_ids = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model.model",
+            connector="visual.merger.",
+            tower_model="visual.",
+        )
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Glm4vMultiModalProcessor,
+    info=Glm4vProcessingInfo,
+    dummy_inputs=Glm4vDummyInputsBuilder,
+)
+class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm4_moe.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm4_moe.py
new file mode 100644
index 0000000000000000000000000000000000000000..aff491f9596c358369ea2bd714599d2b200af938
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/glm4_moe.py
@@ -0,0 +1,699 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 The ZhipuAI Team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only GLM-4.5 model compatible with HuggingFace weights."""
+import typing
+from collections.abc import Callable, Iterable
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from transformers.models.glm4_moe import Glm4MoeConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
+from vllm.distributed import (get_ep_group, get_pp_group,
+                              get_tensor_model_parallel_world_size)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+logger = init_logger(__name__)
+
+
+class Glm4MoeMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config,
+                                           reduce_results=reduce_results,
+                                           prefix=f"{prefix}.down_proj")
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Glm4MoE(nn.Module):
+
+    def __init__(
+        self,
+        config: Glm4MoeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        enable_eplb: bool = False,
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.routed_scaling_factor = config.routed_scaling_factor
+
+        self.ep_group = get_ep_group().device_group
+        self.ep_rank = self.ep_group.rank()
+        self.ep_size = self.ep_group.size()
+        self.n_routed_experts: int = config.n_routed_experts
+        self.n_shared_experts: int = config.n_shared_experts
+
+        if config.hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {config.hidden_act}. "
+                             "Only silu is supported for now.")
+        # NOTE In the transformers implementation, the gate isn't an nn.Linear,
+        # so we cannot use ReplicatedLinear here.
+        # See: https://github.com/huggingface/transformers/blob/v4.55.1/src/transformers/models/glm4_moe/modeling_glm4_moe.py#L260
+        self.gate = nn.Linear(
+            config.hidden_size,
+            config.n_routed_experts,
+            bias=False,
+            dtype=torch.float32,
+        )
+        self.gate.e_score_correction_bias = nn.Parameter(
+            torch.empty(config.n_routed_experts, dtype=torch.float32))
+
+        # Load balancing settings.
+        vllm_config = get_current_vllm_config()
+        parallel_config = vllm_config.parallel_config
+        self.enable_eplb = enable_eplb
+
+        self.n_redundant_experts = parallel_config.num_redundant_experts
+        self.n_logical_experts = self.n_routed_experts
+        self.n_physical_experts = (self.n_logical_experts +
+                                   self.n_redundant_experts)
+        self.n_local_physical_experts = self.n_physical_experts // self.ep_size
+
+        self.physical_expert_start = (self.ep_rank *
+                                      self.n_local_physical_experts)
+        self.physical_expert_end = (self.physical_expert_start +
+                                    self.n_local_physical_experts)
+
+        self.experts = FusedMoE(
+            num_experts=config.n_routed_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=False,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            use_grouped_topk=True,
+            num_expert_group=config.n_group,
+            topk_group=config.topk_group,
+            prefix=f"{prefix}.experts",
+            scoring_func="sigmoid",
+            e_score_correction_bias=self.gate.e_score_correction_bias,
+            enable_eplb=self.enable_eplb,
+            num_redundant_experts=self.n_redundant_experts)
+
+        if config.n_shared_experts is not None:
+            intermediate_size = (config.moe_intermediate_size *
+                                 config.n_shared_experts)
+            self.shared_experts = Glm4MoeMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=self.experts.must_reduce_shared_expert_outputs(
+                ),
+                prefix=f"{prefix}.shared_experts",
+            )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        if self.n_shared_experts is not None:
+            shared_output = self.shared_experts(hidden_states)
+        router_logits = self.gate(hidden_states.to(dtype=torch.float32))
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            router_logits=router_logits) * self.routed_scaling_factor
+        if shared_output is not None:
+            final_hidden_states = final_hidden_states + shared_output
+        if self.tp_size > 1:
+            final_hidden_states = (
+                self.experts.maybe_all_reduce_tensor_model_parallel(
+                    final_hidden_states))
+        return final_hidden_states.view(num_tokens, hidden_dim)
+
+
+class Glm4MoeAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: Glm4MoeConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 131072,
+        head_dim: Optional[int] = None,
+        rms_norm_eps: float = 1e-05,
+        qkv_bias: bool = False,
+        use_qk_norm: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim or (hidden_size // self.total_num_heads)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+        self.use_qk_norm = use_qk_norm
+
+        self.qkv_proj = QKVParallelLinear(hidden_size,
+                                          self.head_dim,
+                                          self.total_num_heads,
+                                          self.total_num_kv_heads,
+                                          bias=qkv_bias,
+                                          quant_config=quant_config,
+                                          prefix=f"{prefix}.qkv_proj")
+
+        self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
+                                        hidden_size,
+                                        bias=False,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.o_proj")
+
+        partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5)
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+            partial_rotary_factor=partial_rotary_factor,
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+
+        if self.use_qk_norm:
+            self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
+            self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if self.use_qk_norm:
+            q = self.q_norm(q.reshape(-1, self.num_heads,
+                                      self.head_dim)).reshape(q.shape)
+            k = self.k_norm(k.reshape(-1, self.num_kv_heads,
+                                      self.head_dim)).reshape(k.shape)
+
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Glm4MoeDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Glm4MoeConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        enable_eplb: bool = False,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          131072)
+        # DecoderLayers are created with `make_layers` which passes the prefix
+        # with the layer's index.
+        layer_idx = int(prefix.split(sep='.')[-1])
+        self.layer_idx = layer_idx
+
+        self.self_attn = Glm4MoeAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            head_dim=config.head_dim,
+            rms_norm_eps=config.rms_norm_eps,
+            qkv_bias=config.attention_bias,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+            use_qk_norm=config.use_qk_norm,
+        )
+
+        if (config.n_routed_experts is not None
+                and layer_idx >= config.first_k_dense_replace):
+            self.mlp = Glm4MoE(
+                config=config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.mlp",
+                enable_eplb=enable_eplb,
+            )
+        else:
+            self.mlp = Glm4MoeMLP(hidden_size=config.hidden_size,
+                                  intermediate_size=config.intermediate_size,
+                                  hidden_act=config.hidden_act,
+                                  quant_config=quant_config,
+                                  prefix=f"{prefix}.mlp")
+
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+        self.routed_scaling_factor = config.routed_scaling_factor
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(positions=positions,
+                                       hidden_states=hidden_states)
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+    })
+class Glm4MoeModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        enable_eplb = vllm_config.parallel_config.enable_eplb
+        self.config = config
+
+        self.vocab_size = config.vocab_size
+
+        if get_pp_group().is_first_rank:
+            self.embed_tokens = VocabParallelEmbedding(
+                config.vocab_size,
+                config.hidden_size,
+                prefix=f"{prefix}.embed_tokens")
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Glm4MoeDecoderLayer(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+                enable_eplb=enable_eplb,
+            ),
+            prefix=f"{prefix}.layers")
+
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states, residual = layer(positions, hidden_states, residual)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+            "residual":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+        })
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.n_routed_experts)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+        for name, loaded_weight in weights:
+            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
+            if spec_layer is not None:
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if (("mlp.experts." in name) and name not in params_dict):
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                is_expert_weight = False
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+
+                    # Anyway, this is an expert weight and should not be
+                    # attempted to load as other weights later
+                    is_expert_weight = True
+
+                    # Do not modify `name` since the loop may continue here
+                    # Instead, create a new variable
+                    name_mapped = name.replace(weight_name, param_name)
+
+                    if is_pp_missing_parameter(name_mapped, self):
+                        continue
+
+                    param = params_dict[name_mapped]
+                    # We should ask the weight loader to return success or not
+                    # here since otherwise we may skip experts with other
+                    # available replicas.
+                    weight_loader = typing.cast(Callable[..., bool],
+                                                param.weight_loader)
+                    success = weight_loader(param,
+                                            loaded_weight,
+                                            name_mapped,
+                                            shard_id=shard_id,
+                                            expert_id=expert_id,
+                                            return_success=True)
+                    if success:
+                        name = name_mapped
+                        break
+                else:
+                    if is_expert_weight:
+                        # We've checked that this is an expert weight
+                        # However it's not mapped locally to this rank
+                        # So we simply skip it
+                        continue
+
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        return loaded_params
+
+
+class Glm4MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    fall_back_to_pt_during_load = False
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Glm4MoeModel(vllm_config=vllm_config,
+                                  prefix=maybe_prefix(prefix, "model"))
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(config.vocab_size,
+                                          config.hidden_size,
+                                          quant_config=quant_config)
+        else:
+            self.lm_head = PPMissingLayer()
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+        self.expert_weights = []
+
+        # Set MoE hyperparameters
+        self.num_moe_layers = (config.num_hidden_layers -
+                               config.first_k_dense_replace)
+        self.num_expert_groups = config.n_group
+
+        self.moe_layers: list[FusedMoE] = []
+        example_moe = None
+        for layer in self.model.layers:
+            if isinstance(layer, PPMissingLayer):
+                continue
+
+            assert isinstance(layer, Glm4MoeDecoderLayer)
+            if isinstance(layer.mlp, Glm4MoE):
+                # Pick last one layer since the first ones may be dense layers.
+                example_moe = layer.mlp
+                self.moe_layers.append(layer.mlp.experts)
+
+        if example_moe is None:
+            raise RuntimeError("No Glm4MoE layer found in model.layers.")
+
+        self.num_logical_experts = example_moe.n_logical_experts
+        self.num_physical_experts = example_moe.n_physical_experts
+        self.num_local_physical_experts = example_moe.n_local_physical_experts
+        self.num_routed_experts = example_moe.n_routed_experts
+        self.num_shared_experts = example_moe.n_shared_experts
+        self.num_redundant_experts = example_moe.n_redundant_experts
+
+    def set_eplb_state(
+        self,
+        expert_load_view: torch.Tensor,
+        logical_to_physical_map: torch.Tensor,
+        logical_replica_count: torch.Tensor,
+    ) -> None:
+        for layer_idx, layer in enumerate(self.moe_layers):
+            # Register the expert weights.
+            self.expert_weights.append(layer.get_expert_weights())
+            layer.set_eplb_state(
+                moe_layer_idx=layer_idx,
+                expert_load_view=expert_load_view,
+                logical_to_physical_map=logical_to_physical_map,
+                logical_replica_count=logical_replica_count,
+            )
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
+
+
+def get_spec_layer_idx_from_weight_name(config: Glm4MoeConfig,
+                                        weight_name: str) -> Optional[int]:
+    if hasattr(config,
+               "num_nextn_predict_layers") and (config.num_nextn_predict_layers
+                                                > 0):
+        layer_idx = config.num_hidden_layers
+        for i in range(config.num_nextn_predict_layers):
+            if f"layers.{layer_idx+i}." in weight_name:
+                return layer_idx + i
+    return None
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gpt_j.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gpt_j.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd162a5e57bc18ab84cde42cec832b5def14b465
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gpt_j.py
@@ -0,0 +1,339 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gptj/modeling_gptj.py
+# Copyright 2023 The vLLM team.
+# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only GPT-J model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import GPTJConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class GPTJAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTJConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.total_num_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_size = self.hidden_size // self.total_num_heads
+
+        self.qkv_proj = QKVParallelLinear(
+            config.hidden_size,
+            self.head_size,
+            self.total_num_heads,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.out_proj = RowParallelLinear(
+            config.hidden_size,
+            config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+        tp_world_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_world_size == 0
+        self.num_heads = self.total_num_heads // tp_world_size
+
+        scaling = self.head_size**-0.5
+        assert getattr(config, "rotary", True)
+        assert config.rotary_dim % 2 == 0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.rotary_emb = get_rope(
+            self.head_size,
+            rotary_dim=config.rotary_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            is_neox_style=False,
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_size,
+                              scaling,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k = self.rotary_emb(position_ids, q, k)
+        attn_output = self.attn(q, k, v)
+        attn_output, _ = self.out_proj(attn_output)
+        return attn_output
+
+
+class GPTJMLP(nn.Module):
+
+    def __init__(
+        self,
+        intermediate_size: int,
+        config: GPTJConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        hidden_size = config.n_embd
+        self.fc_in = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            quant_config=quant_config,
+        )
+        self.fc_out = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            quant_config=quant_config,
+        )
+        self.act = get_act_fn(config.activation_function)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc_in(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.fc_out(hidden_states)
+        return hidden_states
+
+
+class GPTJBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTJConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        inner_dim = (4 * config.n_embd
+                     if config.n_inner is None else config.n_inner)
+        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.attn = GPTJAttention(config,
+                                  cache_config,
+                                  quant_config,
+                                  prefix=f"{prefix}.attn")
+        self.mlp = GPTJMLP(inner_dim, config, quant_config)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_output = self.attn(
+            position_ids=position_ids,
+            hidden_states=hidden_states,
+        )
+        mlp_output = self.mlp(hidden_states)
+        hidden_states = attn_output + mlp_output + residual
+        return hidden_states
+
+
+@support_torch_compile
+class GPTJModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.quant_config = quant_config
+        self.embed_dim = config.n_embd
+        self.wte = VocabParallelEmbedding(
+            config.vocab_size,
+            self.embed_dim,
+        )
+        self.start_layer, self.end_layer, self.h = make_layers(
+            config.n_layer,
+            lambda prefix: GPTJBlock(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.h",
+        )
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.n_embd))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.wte(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+        else:
+            hidden_states = intermediate_tensors["hidden_states"]
+        for layer in self.h[self.start_layer:self.end_layer]:
+            hidden_states = layer(position_ids, hidden_states)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "attn.bias" in name or "attn.masked_bias" in name:
+                continue
+
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class GPTJForCausalLM(nn.Module, SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        assert not config.tie_word_embeddings
+        self.transformer = GPTJModel(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(
+                                         prefix, "transformer"))
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.n_embd,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.transformer.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.transformer(input_ids, positions,
+                                         intermediate_tensors, inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata, self.lm_head.bias)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
\ No newline at end of file
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gpt_oss.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gpt_oss.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f5d9ddd9054f33a3d6039a30a6df48ef3ad6600
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gpt_oss.py
@@ -0,0 +1,614 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+import torch.distributed as dist
+from torch import nn
+from transformers import GptOssConfig
+
+from vllm.attention import Attention, AttentionType
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (get_ep_group, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+from vllm.utils import cdiv
+
+from .utils import extract_layer_index, maybe_prefix
+
+
+class OAIAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: GptOssConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.layer_idx = extract_layer_index(prefix)
+        self.head_dim = config.head_dim
+        self.num_attention_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.hidden_size = config.hidden_size
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=config.max_position_embeddings,
+            base=config.rope_theta,
+            dtype=torch.float32,
+            rope_scaling={
+                "rope_type":
+                "yarn",
+                "factor":
+                config.rope_scaling["factor"],
+                "original_max_position_embeddings":
+                config.rope_scaling["original_max_position_embeddings"],
+                "beta_fast":
+                config.rope_scaling["beta_fast"],
+                "beta_slow":
+                config.rope_scaling["beta_slow"],
+            },
+            is_neox_style=True,
+        )
+
+        tp_size = get_tensor_model_parallel_world_size()
+
+        self.sinks = torch.nn.Parameter(
+            torch.empty(config.num_attention_heads // tp_size,
+                        dtype=torch.bfloat16,
+                        requires_grad=False))
+
+        self.norm = RMSNorm(config.hidden_size, eps=1e-5)
+
+        self.q_size = self.num_attention_heads * self.head_dim // tp_size
+        self.kv_size = self.num_key_value_heads * self.head_dim // tp_size
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = config.rope_theta
+
+        self.qkv = QKVParallelLinear(
+            hidden_size=self.hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.num_attention_heads,
+            total_num_kv_heads=self.num_key_value_heads,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.o_proj = RowParallelLinear(
+            input_size=self.num_attention_heads * self.head_dim,
+            output_size=self.hidden_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self.num_local_attention_heads = config.num_attention_heads // tp_size
+        self.num_local_key_value_heads = config.num_key_value_heads // tp_size
+
+        # Only apply sliding window to every other layer
+        sliding_window = (config.sliding_window if self.layer_idx %
+                          2 == 0 else None)
+        self.attn = Attention(
+            self.num_local_attention_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_local_key_value_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            per_layer_sliding_window=sliding_window,
+            attn_type=AttentionType.DECODER,
+            prefix=f"{prefix}.attn",
+            sinks=self.sinks,
+        )
+
+    def forward(self, hidden_states: torch.Tensor,
+                positions: torch.Tensor) -> torch.Tensor:
+        t = self.norm(hidden_states)
+
+        qkv, _ = self.qkv(t)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        v = v.contiguous()
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+
+        return output + hidden_states
+
+
+class MLPBlock(torch.nn.Module):
+
+    def __init__(
+        self,
+        config: GptOssConfig,
+        layer_idx: int,
+        quant_config: QuantizationConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.num_experts = config.num_local_experts
+        self.experts_per_token = config.num_experts_per_tok
+        self.world_size = dist.get_world_size() if dist.is_initialized() else 1
+        self.norm = RMSNorm(config.hidden_size, eps=1e-5)
+        self.router = torch.nn.Linear(config.hidden_size,
+                                      config.num_local_experts,
+                                      dtype=torch.bfloat16)
+        assert config.intermediate_size % self.world_size == 0
+        self.experts = FusedMoE(num_experts=config.num_local_experts,
+                                top_k=config.num_experts_per_tok,
+                                hidden_size=config.hidden_size,
+                                intermediate_size=config.intermediate_size,
+                                reduce_results=True,
+                                renormalize=True,
+                                quant_config=quant_config,
+                                prefix=f"{prefix}.experts",
+                                apply_router_weight_on_input=False,
+                                has_bias=True,
+                                activation="swigluoai")
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        t = self.norm(x)
+        g = self.router(t)
+        t = self.experts(hidden_states=t, router_logits=g)
+        return x + t
+
+
+class TransformerBlock(torch.nn.Module):
+
+    def __init__(
+        self,
+        config: GptOssConfig,
+        quant_config: QuantizationConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.layer_idx = extract_layer_index(prefix)
+        self.attn = OAIAttention(config, prefix=f"{prefix}.attn")
+        self.mlp = MLPBlock(config,
+                            self.layer_idx,
+                            quant_config=quant_config,
+                            prefix=f"{prefix}.mlp")
+
+    def forward(self, hidden_states: torch.Tensor,
+                positions: torch.Tensor) -> torch.Tensor:
+        attn_output = self.attn(hidden_states, positions)
+        output = self.mlp(attn_output)
+        return output
+
+
+@support_torch_compile
+class GptOssModel(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = vllm_config.model_config.hf_config
+        self.quant_config = vllm_config.quant_config
+        self.config.hidden_size = self.config.hidden_size
+        self.embedding = VocabParallelEmbedding(
+            self.config.vocab_size,
+            self.config.hidden_size,
+        )
+        self.layers = torch.nn.ModuleList([
+            TransformerBlock(
+                self.config,
+                quant_config=self.quant_config,
+                prefix=maybe_prefix(prefix, f"block.{layer_idx}"),
+            ) for layer_idx in range(self.config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(self.config.hidden_size, eps=1e-5)
+
+    def forward(self, input_ids: torch.Tensor,
+                positions: torch.Tensor) -> torch.Tensor:
+        x = self.embedding(input_ids)
+        for layer in self.layers:
+            x = layer(x, positions)
+        x = self.norm(x)
+        return x
+
+
+class GptOssForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config.hf_config
+        self.model = GptOssModel(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "model"),
+        )
+        self.lm_head = ParallelLMHead(
+            self.model_config.vocab_size,
+            self.model_config.hidden_size,
+        )
+        self.logits_processor = LogitsProcessor(self.model_config.vocab_size)
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None) -> torch.Tensor:
+        assert intermediate_tensors is None
+        assert inputs_embeds is None
+        return self.model(input_ids, positions)
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def _load_weights_mxfp4(
+            self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        rename_mapping = {
+            "self_attn": "attn",
+            "input_layernorm.weight": "attn.norm.weight",
+            "post_attention_layernorm.weight": "mlp.norm.weight",
+            "embed_tokens": "embedding",
+        }
+
+        def maybe_rename(name: str) -> str:
+            for remap_name, new_name in rename_mapping.items():
+                if remap_name in name:
+                    return name.replace(remap_name, new_name)
+            return name
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        mxfp4_block = 32
+
+        tp_rank = get_tensor_model_parallel_rank()
+        tp_size = get_tensor_model_parallel_world_size()
+        intermediate_size = self.model_config.intermediate_size
+        intermediate_size_block = intermediate_size // mxfp4_block
+        per_rank_intermediate_size_block = cdiv(intermediate_size_block,
+                                                tp_size)
+        per_rank_intermediate_size = (per_rank_intermediate_size_block *
+                                      mxfp4_block)
+
+        # Calculate common slicing bounds for current rank
+        tp_rank_start = tp_rank * per_rank_intermediate_size
+        tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size,
+                          intermediate_size)
+
+        # Attention heads per rank
+        heads_per_rank = self.model_config.num_attention_heads // tp_size
+        head_start = tp_rank * heads_per_rank
+
+        use_ep = self.vllm_config.parallel_config.enable_expert_parallel
+        ep_size = get_ep_group().world_size
+        ep_rank = get_ep_group().rank
+        num_experts = self.model_config.num_local_experts
+        experts_per_rank = num_experts // ep_size
+        ep_rank_start = ep_rank * experts_per_rank
+        ep_rank_end = (ep_rank + 1) * experts_per_rank
+
+        for name, weight in weights:
+            # FIXME(woosuk): Remove this after testing.
+            weight = weight.cuda()
+
+            if "gate_up_proj_blocks" in name:
+                # Handle MLP gate and up projection weights
+                new_name = name.replace("gate_up_proj_blocks", "w13_weight")
+
+                # flat weight from (E, 2 * N, block_size, entry_per_block)
+                # to (E, 2 * N, -1), shouldn't trigger copy for contiguous
+                weight = weight.view(num_experts, 2 * intermediate_size,
+                                     -1).contiguous()
+
+                # Extract gate and up projection parts
+                # since the weight is shuffled, we can slice directly
+                if use_ep:
+                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    narrow_weight = weight[:,
+                                           2 * tp_rank_start:2 * tp_rank_end,
+                                           ...]
+
+                param = params_dict[new_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param,
+                              narrow_weight,
+                              weight_name=new_name,
+                              shard_id=None,
+                              expert_id=None)
+                loaded_params.add(new_name)
+
+            elif "down_proj_blocks" in name:
+                # Handle MLP down projection weights
+                new_name = name.replace("down_proj_blocks", "w2_weight")
+                # same flatten here, but since 2 mx4 value are packed in 1
+                # uint8, divide by 2
+                weight = weight.view(num_experts, -1,
+                                     intermediate_size // 2).contiguous()
+                if use_ep:
+                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    narrow_weight = weight[...,
+                                           tp_rank_start // 2:tp_rank_end // 2]
+
+                param = params_dict[new_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param,
+                              narrow_weight,
+                              weight_name=new_name,
+                              shard_id=None,
+                              expert_id=None)
+                loaded_params.add(new_name)
+
+            elif "gate_up_proj_scales" in name:
+                # Handle MLP gate and up projection weights scale
+                new_name = name.replace("gate_up_proj_scales",
+                                        "w13_weight_scale")
+                if use_ep:
+                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    narrow_weight = weight[:,
+                                           2 * tp_rank_start:2 * tp_rank_end,
+                                           ...]
+
+                param = params_dict[new_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param,
+                              narrow_weight,
+                              weight_name=new_name,
+                              shard_id=None,
+                              expert_id=None)
+                loaded_params.add(new_name)
+
+            elif "down_proj_scales" in name:
+                # Handle MLP down projection weights
+                new_name = name.replace("down_proj_scales", "w2_weight_scale")
+                if use_ep:
+                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    narrow_weight = weight[..., tp_rank_start //
+                                           mxfp4_block:tp_rank_end //
+                                           mxfp4_block]
+
+                param = params_dict[new_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param,
+                              narrow_weight,
+                              weight_name=new_name,
+                              shard_id=None,
+                              expert_id=None)
+                loaded_params.add(new_name)
+            elif "gate_up_proj_bias" in name:
+                # Handle MLP gate and up projection biases
+                new_name = name.replace("gate_up_proj_bias", "w13_bias")
+
+                # Extract gate and up projection bias parts
+                if use_ep:
+                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    narrow_weight = weight[:,
+                                           2 * tp_rank_start:2 * tp_rank_end]
+
+                param = params_dict[new_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param,
+                              narrow_weight,
+                              weight_name=new_name,
+                              shard_id=None,
+                              expert_id=None)
+                loaded_params.add(new_name)
+
+            elif "down_proj_bias" in name:
+                # Handle MLP down projection bias
+                new_name = name.replace("down_proj_bias", "w2_bias")
+                param = params_dict[new_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                if use_ep:
+                    weight = weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    # (only load on rank 0 to avoid duplication)
+                    if tp_rank != 0:
+                        weight.zero_()
+                weight_loader(param,
+                              weight,
+                              weight_name=new_name,
+                              shard_id=None,
+                              expert_id=None)
+                loaded_params.add(new_name)
+            elif "sinks" in name:
+                # Handle attention sinks (distributed across ranks)
+                name = name.replace("self_attn", "attn")
+                param = params_dict[name]
+                narrow_weight = weight.narrow(0, head_start, heads_per_rank)
+                param.data.copy_(narrow_weight)
+                loaded_params.add(name)
+            elif "q_proj" in name or "k_proj" in name or "v_proj" in name:
+                shard_id = ("q" if "q_proj" in name else
+                            "k" if "k_proj" in name else "v")
+                name = name.replace("self_attn", "attn")
+                param_name = name.replace(f"{shard_id}_proj", "qkv")
+                param = params_dict[param_name]
+                weight_loader = param.weight_loader
+                weight_loader(param, weight, loaded_shard_id=shard_id)
+                loaded_params.add(param_name)
+            else:
+                # Handle all other weights with potential renaming
+                renamed_name = maybe_rename(name)
+                if renamed_name not in params_dict:
+                    continue
+                param = params_dict[renamed_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, weight)
+                loaded_params.add(renamed_name)
+
+        return loaded_params
+
+    def _load_weights_other(
+            self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        rename_mapping = {
+            "self_attn": "attn",
+            "input_layernorm.weight": "attn.norm.weight",
+            "post_attention_layernorm.weight": "mlp.norm.weight",
+            "embed_tokens": "embedding",
+        }
+
+        def maybe_rename(name: str) -> str:
+            for remap_name, new_name in rename_mapping.items():
+                if remap_name in name:
+                    return name.replace(remap_name, new_name)
+            return name
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        tp_rank = get_tensor_model_parallel_rank()
+        tp_size = get_tensor_model_parallel_world_size()
+        intermediate_size = self.model_config.intermediate_size
+
+        per_rank_intermediate_size = cdiv(intermediate_size, tp_size)
+        # Calculate common slicing bounds for current rank
+        tp_rank_start = tp_rank * per_rank_intermediate_size
+        tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size,
+                          intermediate_size)
+
+        # Attention heads per rank
+        heads_per_rank = self.model_config.num_attention_heads // tp_size
+        head_start = tp_rank * heads_per_rank
+
+        use_ep = self.vllm_config.parallel_config.enable_expert_parallel
+        ep_size = get_ep_group().world_size
+        ep_rank = get_ep_group().rank
+        num_experts = self.model_config.num_local_experts
+        experts_per_rank = num_experts // ep_size
+        ep_rank_start = ep_rank * experts_per_rank
+        ep_rank_end = (ep_rank + 1) * experts_per_rank
+
+        for name, weight in weights:
+            if ".experts.gate_up_proj" in name and "bias" not in name:
+                # Handle MLP gate and up projection weights
+                new_name = name.replace(".experts.gate_up_proj",
+                                        ".experts.w13_weight")
+
+                # Extract gate and up projection parts
+                # since the weight is shuffled, we can slice directly
+                if use_ep:
+                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    narrow_weight = weight[:, :,
+                                           2 * tp_rank_start:2 * tp_rank_end]
+
+                narrow_weight = narrow_weight.permute(0, 2, 1).contiguous()
+                param = params_dict[new_name]
+
+                param.copy_(narrow_weight)
+                loaded_params.add(new_name)
+
+            elif ".experts.down_proj" in name and "bias" not in name:
+                # Handle MLP down projection weights
+                new_name = name.replace(".experts.down_proj",
+                                        ".experts.w2_weight")
+
+                if use_ep:
+                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    narrow_weight = weight[:, tp_rank_start:tp_rank_end, :]
+                narrow_weight = narrow_weight.permute(0, 2, 1).contiguous()
+                param = params_dict[new_name]
+
+                param.copy_(narrow_weight)
+                loaded_params.add(new_name)
+
+            elif "gate_up_proj_bias" in name:
+                # Handle MLP gate and up projection biases
+                new_name = name.replace("gate_up_proj_bias", "w13_bias")
+
+                # Extract gate and up projection bias parts
+                if use_ep:
+                    narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    narrow_weight = weight[:,
+                                           2 * tp_rank_start:2 * tp_rank_end]
+
+                param = params_dict[new_name]
+
+                param.copy_(narrow_weight)
+                loaded_params.add(new_name)
+
+            elif "down_proj_bias" in name:
+                # Handle MLP down projection bias
+                new_name = name.replace("down_proj_bias", "w2_bias")
+
+                if use_ep:
+                    weight = weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    # (only load on rank 0 to avoid duplication)
+                    if tp_rank != 0:
+                        weight.zero_()
+                param = params_dict[new_name]
+                param.copy_(weight)
+                loaded_params.add(new_name)
+            elif "sinks" in name:
+                # Handle attention sinks (distributed across ranks)
+                name = name.replace("self_attn", "attn")
+                param = params_dict[name]
+                narrow_weight = weight.narrow(0, head_start, heads_per_rank)
+                param.data.copy_(narrow_weight)
+                loaded_params.add(name)
+            elif "q_proj" in name or "k_proj" in name or "v_proj" in name:
+                shard_id = ("q" if "q_proj" in name else
+                            "k" if "k_proj" in name else "v")
+                name = name.replace("self_attn", "attn")
+                param_name = name.replace(f"{shard_id}_proj", "qkv")
+                param = params_dict[param_name]
+                weight_loader = param.weight_loader
+                weight_loader(param, weight, loaded_shard_id=shard_id)
+                loaded_params.add(param_name)
+            else:
+                # Handle all other weights with potential renaming
+
+                renamed_name = maybe_rename(name)
+                if renamed_name not in params_dict:
+                    continue
+                param = params_dict[renamed_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, weight)
+                loaded_params.add(renamed_name)
+
+        return loaded_params
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        quant_method = (self.model_config.quantization_config['quant_method']
+                        if hasattr(self.model_config, "quantization_config")
+                        else None)
+        if quant_method == "mxfp4":
+            return self._load_weights_mxfp4(weights)
+        else:
+            return self._load_weights_other(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/granite.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/granite.py
new file mode 100644
index 0000000000000000000000000000000000000000..507a9206c42814c53b53657663c3808bbd993bef
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/granite.py
@@ -0,0 +1,497 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only IBM Granite model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from transformers import GraniteConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
+                    make_layers, maybe_prefix)
+
+
+class GraniteMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=hidden_size,
+            output_sizes=[intermediate_size] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(input_size=intermediate_size,
+                                           output_size=hidden_size,
+                                           bias=bias,
+                                           quant_config=quant_config,
+                                           prefix=f"{prefix}.down_proj")
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class GraniteAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: GraniteConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
+        self.head_dim = getattr(config, "head_dim", None)
+        if self.head_dim is None:
+            self.head_dim = self.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = config.attention_multiplier
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            input_size=self.total_num_heads * self.head_dim,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class GraniteDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: GraniteConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.residual_multiplier = config.residual_multiplier
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        if rope_scaling is not None and getattr(
+                config, "original_max_position_embeddings", None):
+            rope_scaling["original_max_position_embeddings"] = (
+                config.original_max_position_embeddings)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        # Support abacusai/Smaug-72B-v0.1 with attention_bias
+        # Support internlm/internlm-7b with bias
+        attention_bias = getattr(config, "attention_bias", False) or getattr(
+            config, "bias", False)
+        self.self_attn = GraniteAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=getattr(config, "num_key_value_heads",
+                                 config.num_attention_heads),
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=attention_bias,
+            cache_config=cache_config,
+            prefix=f"{prefix}.self_attn",
+        )
+
+        self.mlp = GraniteMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            bias=getattr(config, "mlp_bias", False),
+            prefix=f"{prefix}.mlp",
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        hidden_states = residual + hidden_states * self.residual_multiplier
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states * self.residual_multiplier
+        return hidden_states
+
+
+@support_torch_compile
+class GraniteModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.quant_config = quant_config
+        lora_vocab = (lora_config.lora_extra_vocab_size *
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+        if get_pp_group().is_first_rank or (config.tie_word_embeddings
+                                            and get_pp_group().is_last_rank):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE
+                # We need bigger padding if using lora for kernel
+                # compatibility
+                if not lora_config else lora_config.lora_vocab_padding_size,
+                quant_config=quant_config,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: GraniteDecoderLayer(config=config,
+                                               cache_config=cache_config,
+                                               quant_config=quant_config,
+                                               prefix=prefix),
+            prefix=f"{prefix}.layers")
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+
+            hidden_states *= self.config.embedding_multiplier
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states = layer(positions, hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class GraniteForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.lora_config = lora_config
+        self.quant_config = quant_config
+
+        self.model = GraniteModel(vllm_config=vllm_config,
+                                  prefix=maybe_prefix(prefix, "model"))
+        if get_pp_group().is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            if lora_config:
+                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE
+                # We need bigger padding if using lora for kernel
+                # compatibility
+                if not lora_config else lora_config.lora_vocab_padding_size,
+                quant_config=quant_config,
+            )
+            if config.tie_word_embeddings:
+                self.lm_head.weight = self.model.embed_tokens.weight
+
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            if hasattr(config, "logits_scaling"):
+                logit_scale /= config.logits_scaling
+
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    config.vocab_size,
+                                                    scale=logit_scale)
+        else:
+            self.lm_head = PPMissingLayer()
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        model_output = self.model(input_ids, positions, intermediate_tensors,
+                                  inputs_embeds)
+        return model_output
+
+    def compute_logits(
+            self, hidden_states: torch.Tensor,
+            sampling_metadata: SamplingMetadata) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+            "residual":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        # With tie_word_embeddings, we can skip lm_head.weight
+        # The weight might appear unnecessarily in the files if the model is
+        # processed with quantization, LoRA, fine-tuning, etc.
+        skip_prefixes = (["lm_head."]
+                         if self.config.tie_word_embeddings else None)
+
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=skip_prefixes,
+        )
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/granitemoe.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/granitemoe.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d31854dce8d858452ff792c56ae9eb155358cae
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/granitemoe.py
@@ -0,0 +1,540 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only GraniteMoe model."""
+from collections.abc import Iterable
+from typing import Any, Optional
+
+import torch
+from torch import nn
+from transformers.models.granitemoe import GraniteMoeConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter, make_layers,
+                    maybe_prefix)
+
+
+class GraniteMoeMoE(nn.Module):
+    """A tensor-parallel MoE implementation for GraniteMoe that shards each
+    expert across all ranks.
+    Each expert's weights are sharded across all ranks and a fused MoE
+    kernel is used for the forward pass, and finally we reduce the outputs
+    across ranks.
+    """
+
+    def __init__(self,
+                 num_experts: int,
+                 top_k: int,
+                 hidden_size: int,
+                 intermediate_size: int,
+                 params_dtype: Optional[torch.dtype] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 tp_size: Optional[int] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.hidden_size = hidden_size
+
+        # Gate always runs at half / full precision for now.
+        self.gate = ReplicatedLinear(hidden_size,
+                                     num_experts,
+                                     bias=False,
+                                     params_dtype=params_dtype,
+                                     quant_config=None,
+                                     prefix=f"{prefix}.gate")
+
+        self.experts = FusedMoE(num_experts=num_experts,
+                                top_k=top_k,
+                                hidden_size=hidden_size,
+                                intermediate_size=intermediate_size,
+                                params_dtype=params_dtype,
+                                reduce_results=True,
+                                renormalize=True,
+                                quant_config=quant_config,
+                                tp_size=tp_size,
+                                prefix=f"{prefix}.experts")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # NOTE: hidden_states can have either 1D or 2D shape.
+        orig_shape = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+        final_hidden_states = self.experts(hidden_states, router_logits)
+        return final_hidden_states.view(orig_shape)
+
+
+class GraniteMoeAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position: int = 4096 * 32,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        attention_multiplier: Optional[float] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = (attention_multiplier if attention_multiplier
+                        is not None else self.head_dim**-1)
+        self.rope_theta = rope_theta
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class GraniteMoeDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: GraniteMoeConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        self.self_attn = GraniteMoeAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+            attention_multiplier=config.attention_multiplier)
+        self.block_sparse_moe = GraniteMoeMoE(
+            num_experts=config.num_local_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.block_sparse_moe")
+
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+        self.residual_multiplier = config.residual_multiplier
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        hidden_states = residual + hidden_states * self.residual_multiplier
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.block_sparse_moe(hidden_states)
+        hidden_states = residual + hidden_states * self.residual_multiplier
+
+        return hidden_states
+
+
+@support_torch_compile
+class GraniteMoeModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.quant_config = quant_config  # Required by MixtralModel
+        lora_vocab = (lora_config.lora_extra_vocab_size *
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+        self.embedding_multiplier = config.embedding_multiplier
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: GraniteMoeDecoderLayer(
+                config, cache_config, quant_config=quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers")
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            hidden_states *= self.embedding_multiplier
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states = layer(positions, hidden_states)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+    def _load_weights(self,
+                      weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        """
+        This function is copied from `MixtralModel.load_weights`, mainly to 
+        decouple from mixtral, avoiding impact on support like BNB  
+        quantization.
+        """
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="w1",
+            ckpt_down_proj_name="w2",
+            ckpt_up_proj_name="w3",
+            num_experts=self.config.num_local_experts)
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if ((name.endswith(".bias") or name.endswith("_bias"))
+                        and name not in params_dict):
+                    continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+                if name.endswith("scale"):
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param,
+                                  loaded_weight,
+                                  name,
+                                  shard_id=shard_id,
+                                  expert_id=expert_id)
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        new_weights = {}
+        for n, p in weights:
+            if n.endswith('.block_sparse_moe.input_linear.weight'):
+                for e in range(p.size(0)):
+                    w1_name = n.replace(
+                        '.block_sparse_moe.input_linear.weight',
+                        f".block_sparse_moe.experts.{e}.w1.weight")
+                    w3_name = n.replace(
+                        '.block_sparse_moe.input_linear.weight',
+                        f".block_sparse_moe.experts.{e}.w3.weight")
+                    w1_param, w3_param = p[e].chunk(2, dim=0)
+                    assert w1_name not in new_weights
+                    assert w3_name not in new_weights
+                    new_weights[w1_name] = w1_param
+                    new_weights[w3_name] = w3_param
+            elif n.endswith('.block_sparse_moe.output_linear.weight'):
+                for e in range(p.size(0)):
+                    w2_name = n.replace(
+                        '.block_sparse_moe.output_linear.weight',
+                        f".block_sparse_moe.experts.{e}.w2.weight")
+                    w2_param = p[e]
+                    assert w2_name not in new_weights
+                    new_weights[w2_name] = w2_param
+            elif n.endswith('.block_sparse_moe.router.layer.weight'):
+                gate_name = n.replace('.block_sparse_moe.router.layer.weight',
+                                      ".block_sparse_moe.gate.weight")
+                assert gate_name not in new_weights
+                new_weights[gate_name] = p
+            else:
+                new_weights[n] = p
+        return self._load_weights(new_weights.items())
+
+
+class GraniteMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    fall_back_to_pt_during_load = False
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.lora_config = lora_config
+
+        self.model = GraniteMoeModel(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(prefix, "model"))
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE
+            # We need bigger padding if using lora for kernel
+            # compatibility
+            if not lora_config else lora_config.lora_vocab_padding_size,
+            quant_config=quant_config,
+        )
+        if config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size,
+                                                scale=1 /
+                                                self.config.logits_scaling)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+            self, hidden_states: torch.Tensor,
+            sampling_metadata: SamplingMetadata) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+            "residual":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/granitemoeshared.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/granitemoeshared.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e2e8544179c7404e3132e8e11cc382c6e59d8a4
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/granitemoeshared.py
@@ -0,0 +1,342 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only GraniteMoeShared model.
+
+The architecture is the same as granitemoe but with the addition of shared
+experts.
+"""
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+from torch import nn
+from transformers.models.granitemoeshared import GraniteMoeSharedConfig
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .granitemoe import GraniteMoeAttention, GraniteMoeModel, GraniteMoeMoE
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import AutoWeightsLoader, make_layers, maybe_prefix
+
+
+class GraniteMoeSharedMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: GraniteMoeSharedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.input_size = config.hidden_size
+        self.hidden_size = config.shared_intermediate_size
+        self.input_linear = MergedColumnParallelLinear(
+            input_size=self.input_size,
+            output_sizes=[self.hidden_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.input_linear")
+        self.output_linear = RowParallelLinear(
+            self.hidden_size,
+            self.input_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.output_linear")
+        if config.hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {config.hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.input_linear(hidden_states)
+        hidden_states = self.act_fn(hidden_states)
+        hidden_states, _ = self.output_linear(hidden_states)
+        return hidden_states
+
+
+class GraniteMoeSharedDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: GraniteMoeSharedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        self.self_attn = GraniteMoeAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+            attention_multiplier=config.attention_multiplier)
+        self.block_sparse_moe = GraniteMoeMoE(
+            num_experts=config.num_local_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.block_sparse_moe")
+        self.shared_mlp = None if \
+            getattr(config, 'shared_intermediate_size', 0) == 0 \
+            else GraniteMoeSharedMLP(
+                config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.shared_mlp"
+            )
+
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+        self.residual_multiplier = config.residual_multiplier
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        hidden_states = residual + hidden_states * self.residual_multiplier
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        if self.shared_mlp is None:
+            hidden_states = self.block_sparse_moe(hidden_states)
+        else:
+            # create a copy since block_sparse_moe modifies in-place
+            moe_hidden_states = hidden_states.clone()
+            moe_hidden_states = self.block_sparse_moe(moe_hidden_states)
+            hidden_states = moe_hidden_states + self.shared_mlp(hidden_states)
+            del moe_hidden_states
+        hidden_states = residual + hidden_states * self.residual_multiplier
+
+        return hidden_states
+
+
+@support_torch_compile
+class GraniteMoeSharedModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.quant_config = quant_config  # Required by MixtralModel
+        self.padding_idx = config.pad_token_id
+        lora_vocab = (lora_config.lora_extra_vocab_size *
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            quant_config=quant_config,
+        )
+        self.embedding_multiplier = config.embedding_multiplier
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: GraniteMoeSharedDecoderLayer(
+                config, cache_config, quant_config=quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers")
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            hidden_states *= self.embedding_multiplier
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states = layer(positions, hidden_states)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        new_weights = {}
+        for n, p in weights:
+            if n.endswith('.block_sparse_moe.input_linear.weight'):
+                for e in range(p.size(0)):
+                    w1_name = n.replace(
+                        '.block_sparse_moe.input_linear.weight',
+                        f".block_sparse_moe.experts.{e}.w1.weight")
+                    w3_name = n.replace(
+                        '.block_sparse_moe.input_linear.weight',
+                        f".block_sparse_moe.experts.{e}.w3.weight")
+                    w1_param, w3_param = p[e].chunk(2, dim=0)
+                    assert w1_name not in new_weights
+                    assert w3_name not in new_weights
+                    new_weights[w1_name] = w1_param
+                    new_weights[w3_name] = w3_param
+            elif n.endswith('.block_sparse_moe.output_linear.weight'):
+                for e in range(p.size(0)):
+                    w2_name = n.replace(
+                        '.block_sparse_moe.output_linear.weight',
+                        f".block_sparse_moe.experts.{e}.w2.weight")
+                    w2_param = p[e]
+                    assert w2_name not in new_weights
+                    new_weights[w2_name] = w2_param
+            elif n.endswith('.block_sparse_moe.router.layer.weight'):
+                gate_name = n.replace('.block_sparse_moe.router.layer.weight',
+                                      ".block_sparse_moe.gate.weight")
+                assert gate_name not in new_weights
+                new_weights[gate_name] = p
+            else:
+                new_weights[n] = p
+        return GraniteMoeModel._load_weights(self, new_weights.items())
+
+
+class GraniteMoeSharedForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    fall_back_to_pt_during_load = False
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.lora_config = lora_config
+
+        self.model = GraniteMoeSharedModel(vllm_config=vllm_config,
+                                           prefix=maybe_prefix(
+                                               prefix, "model"))
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE
+            # We need bigger padding if using lora for kernel
+            # compatibility
+            if not lora_config else lora_config.lora_vocab_padding_size,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"))
+        if config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size,
+                                                scale=1 /
+                                                self.config.logits_scaling)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+            self, hidden_states: torch.Tensor,
+            sampling_metadata: SamplingMetadata) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+            "residual":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gritlm.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gritlm.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e7490e3c4f07dddbd229db757438ad9893185ad
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/gritlm.py
@@ -0,0 +1,262 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Set
+from typing import Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from vllm.config import ModelConfig, VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.pooler import (DispatchPooler, Pooler,
+                                               PoolerHead, PoolerNormalize,
+                                               PoolingParamsUpdate,
+                                               build_output, get_prompt_lens,
+                                               get_prompt_token_ids)
+from vllm.model_executor.models.llama import LlamaForCausalLM
+from vllm.model_executor.pooling_metadata import PoolingMetadata
+from vllm.sequence import PoolerOutput
+from vllm.tasks import PoolingTask
+from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
+
+from .interfaces import SupportsV0Only
+
+logger = init_logger(__name__)
+
+
+class GritLMMeanPool(nn.Module):
+    """As `MeanPool`, but only includes non-instruction tokens."""
+
+    def __init__(self, model_config: ModelConfig):
+        super().__init__()
+
+        self.model_config = model_config
+
+        tokenizer = cached_tokenizer_from_config(self.model_config)
+
+        # Collect the tokens needed for pattern matching.
+        # "▁<" is different from "_<". The former uses "▁" to indicate that
+        # the next token is the start of a word.
+        # "<0x0A>" is the newline token (i.e. "\n")."
+        self.token_ids = {
+            tok: tokenizer.convert_tokens_to_ids([tok])[0]
+            for tok in ["<s>", "▁<", "<", "|", "embed", ">", "<0x0A>", "user"]
+        }
+
+        def tokens_to_ids(tokens: list[str]) -> np.ndarray:
+            return np.array([self.token_ids[token] for token in tokens])
+
+        self.user_pattern_ids = tokens_to_ids(
+            ["▁<", "|", "user", "|", ">", "<0x0A>"])
+        self.embed_newline_pattern_ids = tokens_to_ids(
+            ["<0x0A>", "<", "|", "embed", "|", ">", "<0x0A>"])
+        self.embed_pattern_ids = tokens_to_ids(
+            ["▁<", "|", "embed", "|", ">", "<0x0A>"])
+
+    def _find_array(
+        self,
+        arr: np.ndarray,
+        target: np.ndarray,
+        start_idx: int = 0,
+        end_idx: Optional[int] = None,
+    ) -> int:
+        """
+        Find the first occurrence of `target` in `arr` starting from
+        `start_idx`.
+
+        Args:
+            arr: The array to search within.
+            target: The consecutive subsequence to find.
+            start_idx: The starting index to search from (inclusive).
+            end_idx: The ending index to search from (exclusive).
+
+        Returns:
+            The index of the first occurrence of `target` in `arr`.
+        """
+        if start_idx < 0:
+            raise ValueError("`start_idx` must be non-negative")
+        if len(arr) == 0 or len(target) == 0:
+            raise ValueError("Empty `arr` or `target` not allowed")
+
+        arr_len = len(arr)
+        target_len = len(target)
+
+        if end_idx is None:
+            end_idx = arr_len
+
+        for i in range(start_idx, min(end_idx, arr_len - target_len + 1)):
+            if (arr[i:i + target_len] == target).all():
+                return i
+
+        return -1
+
+    def _get_instruction_len(self, prompt_token_ids: np.ndarray) -> int:
+        """
+        Get the length of the instruction in the prompt.
+
+        We do a pattern matching to find the instruction in the prompt,
+        and then return the length of the instruction.
+
+        The pattern matching is done using integers instead of strings
+        because the prompt is given as a list of token IDs.
+        """
+        instruction_len = 0
+
+        # Return no instruction in case of missing BOS token.
+        if prompt_token_ids[0] != self.token_ids["<s>"]:
+            logger.warning("BOS token not found in prompt, "
+                           "thus using empty string for instruction. "
+                           "GritLM requires BOS token in prompt.")
+            return instruction_len
+
+        # If user pattern is found in the prompt, that means there should be
+        # a newline token before the embed pattern.
+        embed_pattern_ids = self.embed_pattern_ids
+        if self._find_array(prompt_token_ids,
+                            self.user_pattern_ids,
+                            start_idx=1,
+                            end_idx=2) == 1:
+            embed_pattern_ids = self.embed_newline_pattern_ids
+
+        # Find the embed pattern in the prompt.
+        found_embed_pattern_idx = self._find_array(prompt_token_ids,
+                                                   embed_pattern_ids,
+                                                   start_idx=1)
+
+        if found_embed_pattern_idx != -1:
+            instruction_len = found_embed_pattern_idx + len(embed_pattern_ids)
+        else:
+            logger.warning("Query instruction not found in prompt, "
+                           "thus using BOS token as instruction instead. "
+                           "GritLM requires query instruction in prompt.")
+            instruction_len = 1
+
+        return instruction_len
+
+    def get_supported_tasks(self) -> Set[PoolingTask]:
+        return {"encode", "embed"}
+
+    def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
+        return PoolingParamsUpdate(requires_token_ids=True)
+
+    def forward_one(
+        self,
+        hidden_states: torch.Tensor,
+        prompt_len: Optional[torch.Tensor] = None,
+        instr_len: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        assert prompt_len is None or prompt_len == hidden_states.shape[0], \
+            "partial prefill not supported with MEAN pooling"
+
+        return hidden_states[instr_len:].mean(dim=0, dtype=torch.float32)
+
+    def forward_all(
+        self,
+        hidden_states: torch.Tensor,
+        prompt_lens: torch.Tensor,
+        instr_lens: torch.Tensor,
+    ) -> Union[list[torch.Tensor], torch.Tensor]:
+        offset = 0
+        pooled_data = list[torch.Tensor]()
+
+        for prompt_len, instr_len in zip(prompt_lens, instr_lens):
+            pooled_data.append(hidden_states[offset + instr_len:offset +
+                                             prompt_len].mean(
+                                                 dim=0, dtype=torch.float32))
+            offset += prompt_len
+
+        return pooled_data
+
+    def forward(
+        self,
+        hidden_states: Union[torch.Tensor, list[torch.Tensor]],
+        pooling_metadata: PoolingMetadata,
+    ) -> Union[list[torch.Tensor], torch.Tensor]:
+        prompt_lens = get_prompt_lens(hidden_states, pooling_metadata)
+        instr_lens = torch.tensor(
+            [
+                self._get_instruction_len(token_ids.cpu().numpy())
+                for token_ids in get_prompt_token_ids(pooling_metadata)
+            ],
+            device=prompt_lens.device,
+        )
+
+        if isinstance(hidden_states, list):
+            return [
+                self.forward_one(h, prompt_len, instr_len) for h, prompt_len,
+                instr_len in zip(hidden_states, prompt_lens, instr_lens)
+            ]
+
+        return self.forward_all(hidden_states, prompt_lens, instr_lens)
+
+
+class GritLMPooler(Pooler):
+
+    def __init__(self, model_config: ModelConfig):
+        super().__init__()
+
+        self.pooling = GritLMMeanPool(model_config)
+        self.head = PoolerHead(PoolerNormalize())
+
+    def get_supported_tasks(self) -> Set[PoolingTask]:
+        return self.pooling.get_supported_tasks()
+
+    def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
+        return self.pooling.get_pooling_updates(task)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> PoolerOutput:
+        pooled_data = self.pooling(hidden_states, pooling_metadata)
+        pooled_data = self.head(pooled_data, pooling_metadata)
+        return build_output(pooled_data)
+
+
+class GritLM(LlamaForCausalLM, SupportsV0Only):
+    """This class implements the embedding model for parasail-ai/GritLM-7B-vllm.
+
+    The class inherits from LlamaForCausalLM and provides a custom pooling
+    layer.
+
+    The main difference between the pooling layer in GritLM and the one in
+    LlamaForCausalLM is that GritLM ignores the query instruction in the prompt
+    when pooling the hidden states.
+
+    Embedding prompts should be in the following format:
+    - With instruction: "<|user|>\nINSTRUCTION\n<|embed|>\nPROMPT".
+    - Without instruction: "<|embed|>\nPROMPT".
+
+    Generation prompts should be in the following format:
+    - "<|user|>\nPROMPT\n<|assistant|>\n"
+    """
+
+    is_pooling_model = True
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        **kwargs,
+    ) -> None:
+        # Use full attention for pooling (this is why V1 is not supported yet)
+        if vllm_config.model_config.runner_type == "pooling":
+            hf_config = vllm_config.model_config.hf_config
+            hf_config.is_causal = False
+
+            vllm_config.cache_config.sliding_window = None
+
+            hf_config.sliding_window = None
+
+        super().__init__(vllm_config=vllm_config, prefix=prefix, **kwargs)
+
+        pooler_config = vllm_config.model_config.pooler_config
+        if pooler_config is not None:
+            self.pooler = DispatchPooler({
+                "encode":
+                Pooler.for_encode(pooler_config),
+                "embed":
+                GritLMPooler(vllm_config.model_config),
+            })
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/grok1.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/grok1.py
new file mode 100644
index 0000000000000000000000000000000000000000..3659249cd8bd628f19b6189a5c9e0acf9aca8376
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/grok1.py
@@ -0,0 +1,550 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Adapted from
+# https://github.com/ROCm/vllm/blob/cea7419f151cc50293a05b7fac8547f8f887c9f6/vllm/model_executor/models/grok1.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Grok1 model."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+# Default Grok1-specific constants, overridden by config values if present
+DEFAULT_ATTN_OUTPUT_MULTIPLIER = 0.08838834764831845
+DEFAULT_OUTPUT_MULTIPLIER_SCALE = 0.5773502691896257
+DEFAULT_EMBEDDING_MULTIPLIER_SCALE = 78.38367176906169
+
+
+class Grok1MoE(nn.Module):
+    """A tensor-parallel MoE implementation for Grok1 that shards each expert
+    across all ranks.
+
+    Each expert's weights are sharded across all ranks and a fused MoE
+    kernel is used for the forward pass, and finally we reduce the outputs
+    across ranks.
+    """
+
+    def __init__(self,
+                 num_experts: int,
+                 top_k: int,
+                 hidden_size: int,
+                 intermediate_size: int,
+                 params_dtype: Optional[torch.dtype] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 tp_size: Optional[int] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.hidden_size = hidden_size
+
+        # Gate always runs at half / full precision for now.
+        self.gate = ReplicatedLinear(hidden_size,
+                                     num_experts,
+                                     bias=False,
+                                     params_dtype=params_dtype,
+                                     quant_config=None,
+                                     prefix=f"{prefix}.gate")
+
+        self.experts = FusedMoE(num_experts=num_experts,
+                                top_k=top_k,
+                                hidden_size=hidden_size,
+                                intermediate_size=intermediate_size,
+                                params_dtype=params_dtype,
+                                reduce_results=True,
+                                renormalize=True,
+                                quant_config=quant_config,
+                                tp_size=tp_size,
+                                activation="gelu",
+                                prefix=f"{prefix}.experts")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # NOTE: hidden_states can have either 1D or 2D shape.
+        orig_shape = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+        router_logits = 30.0 * F.tanh(router_logits / 30.0)
+        final_hidden_states = self.experts(hidden_states, router_logits)
+        return final_hidden_states.view(orig_shape)
+
+
+class Grok1Attention(nn.Module):
+
+    def __init__(
+            self,
+            hidden_size: int,
+            num_heads: int,
+            num_kv_heads: int,
+            max_position: int = 4096 * 32,
+            rope_theta: float = 10000,
+            cache_config: Optional[CacheConfig] = None,
+            quant_config: Optional[QuantizationConfig] = None,
+            prefix: str = "",
+            config=None,  # Added config parameter
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.config = config  # Store config reference
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+        )
+
+        attn_logits_soft_cap = max(
+            getattr(config, "attn_logit_softcapping", 30.0), 0.0)
+
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              logits_soft_cap=attn_logits_soft_cap,
+                              prefix=f"{prefix}.attn")
+        self.attn_multiplier = getattr(self.config, "attn_output_multiplier",
+                                       1.0) if self.config else 1.0
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        output *= self.attn_multiplier
+        return output
+
+
+class Grok1DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Check for fp8 quantization
+        self.use_fp8 = False
+        if quant_config is not None:
+            self.use_fp8 = getattr(quant_config, "is_fp8_w8a8",
+                                   lambda: False)()
+            if not self.use_fp8 and hasattr(quant_config, "is_fp8"):
+                self.use_fp8 = quant_config.is_fp8
+
+        # Requires transformers > 4.32.0
+        # Default rope_theta value if not in config
+        rope_theta = 10000
+        self.attn = Grok1Attention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+            config=config)  # Pass config to Grok1Attention
+
+        # Grok1 uses "num_experts" in its config
+        num_experts = getattr(config, "num_experts", 8)
+        num_experts_per_tok = getattr(config, "num_experts_per_tok", 2)
+
+        self.moe_block = Grok1MoE(num_experts=num_experts,
+                                  top_k=num_experts_per_tok,
+                                  hidden_size=config.hidden_size,
+                                  intermediate_size=config.intermediate_size,
+                                  quant_config=quant_config,
+                                  prefix=f"{prefix}.moe_block")
+
+        self.pre_attn_norm = RMSNorm(config.hidden_size,
+                                     eps=config.rms_norm_eps)
+        self.post_attn_norm = RMSNorm(config.hidden_size,
+                                      eps=config.rms_norm_eps)
+        self.pre_moe_norm = RMSNorm(config.hidden_size,
+                                    eps=config.rms_norm_eps)
+        self.post_moe_norm = RMSNorm(config.hidden_size,
+                                     eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.pre_attn_norm(hidden_states)
+        else:
+            hidden_states, residual = self.pre_attn_norm(
+                hidden_states, residual)
+
+        hidden_states = self.attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        # Post attention normalization
+        hidden_states = self.post_attn_norm(hidden_states)
+
+        # MoE block with normalization
+        hidden_states, residual = self.pre_moe_norm(hidden_states, residual)
+        hidden_states = self.moe_block(hidden_states)
+        hidden_states = self.post_moe_norm(hidden_states)
+
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Grok1Model(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.quant_config = quant_config
+        self.padding_idx = config.pad_token_id
+        lora_vocab = (lora_config.lora_extra_vocab_size *
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+        self.embedding_multiplier_scale = getattr(
+            config, "embedding_multiplier_scale",
+            DEFAULT_EMBEDDING_MULTIPLIER_SCALE)
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            quant_config=quant_config,
+        )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Grok1DecoderLayer(
+                config, cache_config, quant_config=quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers")
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        hidden_states = hidden_states * self.embedding_multiplier_scale
+        return hidden_states
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states, residual = layer(positions, hidden_states, residual)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Map Grok1's unique expert parameter names to standard names
+        # Grok1 uses "num_experts" in its config
+        num_experts = getattr(self.config, "num_experts", 8)
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="linear",  # Grok1 specific
+            ckpt_down_proj_name="linear_1",  # Grok1 specific
+            ckpt_up_proj_name="linear_v",  # Grok1 specific
+            num_experts=num_experts)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+        for name, loaded_weight in weights:
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if ((name.endswith(".bias") or name.endswith("_bias"))
+                        and name not in params_dict):
+                    continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+                if name.endswith("scale"):
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param,
+                                  loaded_weight,
+                                  name,
+                                  shard_id=shard_id,
+                                  expert_id=expert_id)
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                    # Handle Grok1-specific norm.scale naming
+                    if "norm.scale" in name:
+                        name = name.replace("scale", "weight")
+
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class Grok1ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    fall_back_to_pt_during_load = False
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.lora_config = lora_config
+        self.quant_config = quant_config
+
+        self.model = Grok1Model(vllm_config=vllm_config,
+                                prefix=maybe_prefix(prefix, "model"))
+
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
+        )
+
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+
+        self.output_multiplier_scale = getattr(
+            config, "output_multiplier_scale", DEFAULT_OUTPUT_MULTIPLIER_SCALE)
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size,
+                                                self.output_multiplier_scale)
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        # Skip lm_head when tie_word_embeddings is True
+        skip_prefixes = (["lm_head"]
+                         if self.config.tie_word_embeddings else None)
+
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=skip_prefixes,
+        )
+        return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/h2ovl.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/h2ovl.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3e4f81597adb6ea6a09e40e5e4c8a4c7c6e0c6a
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/h2ovl.py
@@ -0,0 +1,535 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://huggingface.co/h2oai/h2ovl-mississippi-2b/blob/main/modeling_h2ovl_chat.py
+# https://huggingface.co/h2oai/h2ovl-mississippi-2b/blob/main/image_process.py
+# --------------------------------------------------------
+# H2OVL-Mississippi
+# Copyright (c) 2024 H2O.AI
+# Licensed under Apache 2.0 License [see LICENSE for details]
+# --------------------------------------------------------
+from collections.abc import Mapping, Sequence
+from typing import Optional, Union
+
+import torch
+from PIL import Image
+from transformers import PretrainedConfig
+
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalKwargs
+from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
+                                   MultiModalDataItems)
+from vllm.multimodal.processing import (MultiModalHashes, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+
+from .intern_vit import InternVisionModel
+from .internvl import (IMG_CONTEXT, IMG_END, IMG_START,
+                       BaseInternVLDummyInputsBuilder,
+                       BaseInternVLMultiModalProcessor,
+                       BaseInternVLProcessingInfo, BaseInternVLProcessor,
+                       InternVLChatModel, build_transform,
+                       find_closest_aspect_ratio, get_internvl_target_ratios)
+
+
+def resolve_h2ovl_min_max_num(
+    *,
+    min_dynamic_patch: int,
+    max_dynamic_patch: int,
+    dynamic_image_size: bool,
+    use_thumbnail: bool,
+) -> tuple[int, int]:
+    min_dynamic_patch = min_dynamic_patch if dynamic_image_size else 1
+    max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1
+
+    if use_thumbnail and max_dynamic_patch != 1:
+        max_dynamic_patch += 1
+
+    return min_dynamic_patch, max_dynamic_patch
+
+
+def get_h2ovl_target_ratios(
+    min_num: int,
+    max_num: int,
+    *,
+    prior_aspect_ratio: Optional[tuple[int, int]],
+) -> list[tuple[int, int]]:
+    target_ratios = get_internvl_target_ratios(min_num, max_num)
+
+    # if prior_aspect_ratio is provided, filter the target ratios
+    if prior_aspect_ratio is not None:
+        target_ratios = [
+            ratio for ratio in target_ratios if prior_aspect_ratio[0] %
+            ratio[0] != 0 and prior_aspect_ratio[1] % ratio[1] != 0
+        ]
+
+    return target_ratios
+
+
+# modified to include blocks generated in second pass
+def calculate_h2ovl_targets(
+    *,
+    orig_width: int,
+    orig_height: int,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> tuple[int, int, int, tuple[int, int]]:
+    aspect_ratio = orig_width / orig_height
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio,
+        target_ratios,
+        width=orig_width,
+        height=orig_height,
+        image_size=image_size,
+    )
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # add thumbnail image if num_blocks != 1
+    if use_thumbnail and blocks != 1:
+        blocks += 1
+
+    return blocks, target_width, target_height, target_aspect_ratio
+
+
+# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
+# refactored to handle prior_aspect_ratio
+def dynamic_preprocess_h2ovl(
+    image: Image.Image,
+    *,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> tuple[list[Image.Image], tuple[int, int]]:
+    orig_width, orig_height = image.size
+
+    # calculate the number of blocks without thumbnail
+    (
+        blocks,
+        target_width,
+        target_height,
+        target_aspect_ratio,
+    ) = calculate_h2ovl_targets(
+        orig_width=orig_width,
+        orig_height=orig_height,
+        target_ratios=target_ratios,
+        image_size=image_size,
+        use_thumbnail=False,
+    )
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size,
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+
+    assert len(processed_images) == blocks
+
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+
+    return processed_images, target_aspect_ratio
+
+
+def _preprocess_image(
+    image: Image.Image,
+    *,
+    input_size: int,
+    min_num: int,
+    max_num: int,
+    use_thumbnail: bool,
+    prior_aspect_ratio: Optional[tuple[int, int]],
+) -> tuple[torch.Tensor, tuple[int, int]]:
+    target_ratios = get_h2ovl_target_ratios(
+        min_num,
+        max_num,
+        prior_aspect_ratio=prior_aspect_ratio,
+    )
+
+    transform = build_transform(input_size=input_size)
+    images, target_aspect_ratio = dynamic_preprocess_h2ovl(
+        image,
+        image_size=input_size,
+        use_thumbnail=use_thumbnail,
+        target_ratios=target_ratios,
+    )
+
+    pixel_values = torch.stack([transform(image) for image in images])
+    return pixel_values, target_aspect_ratio
+
+
+# refactored to use the _preprocess_image function
+def image_to_pixel_values_h2ovl(
+    image: Image.Image,
+    *,
+    input_size: int,
+    min_num: int,
+    max_num: int,
+    use_thumbnail: bool,
+    use_msac: bool,
+) -> torch.Tensor:
+    # when MSAC is turned on, we need to process the image twice
+    if use_msac:
+        # first pass
+        pixel_values1, aspect_ratio1 = _preprocess_image(
+            image,
+            input_size=input_size,
+            min_num=1,
+            max_num=max_num,
+            use_thumbnail=True,
+            prior_aspect_ratio=None,
+        )
+        # second pass
+        pixel_values2, _ = _preprocess_image(
+            image,
+            input_size=input_size,
+            min_num=3,
+            max_num=max_num,
+            use_thumbnail=True,
+            prior_aspect_ratio=aspect_ratio1,
+        )
+        # combine pixel values
+        pixel_values = torch.cat(
+            [pixel_values2[:-1], pixel_values1[:-1], pixel_values2[-1:]], 0)
+
+    else:
+        pixel_values, _ = _preprocess_image(
+            image,
+            input_size=input_size,
+            min_num=min_num,
+            max_num=max_num,
+            use_thumbnail=use_thumbnail,
+            prior_aspect_ratio=None,
+        )
+
+    return pixel_values
+
+
+class H2OVLProcessor(BaseInternVLProcessor):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        tokenizer: AnyTokenizer,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        use_msac: Optional[bool] = None,
+    ) -> None:
+        super().__init__(
+            config,
+            tokenizer,
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+        )
+
+        if use_msac is None:
+            use_msac = config.use_msac
+        assert isinstance(use_msac, bool)
+
+        self.use_msac = use_msac
+
+    @property
+    def image_token_id(self) -> int:
+        return self.tokenizer.get_vocab()[IMG_CONTEXT]
+
+    def get_image_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int],
+    ) -> PromptUpdateDetails[str]:
+        repl_features = IMG_CONTEXT * feature_size
+        repl_full = IMG_START + repl_features + IMG_END
+
+        return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
+
+    def resolve_min_max_num(
+        self,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        use_thumbnail: Optional[bool] = None,
+    ) -> tuple[int, int]:
+        min_dynamic_patch = (self.min_dynamic_patch if min_dynamic_patch
+                             is None else min_dynamic_patch)
+        max_dynamic_patch = (self.max_dynamic_patch if max_dynamic_patch
+                             is None else max_dynamic_patch)
+        dynamic_image_size = (self.dynamic_image_size if dynamic_image_size
+                              is None else dynamic_image_size)
+        use_thumbnail = (self.use_thumbnail
+                         if use_thumbnail is None else use_thumbnail)
+
+        return resolve_h2ovl_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=use_thumbnail,
+        )
+
+    def resolve_target_ratios(
+        self,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        use_thumbnail: Optional[bool] = None,
+        prior_aspect_ratio: Optional[tuple[int, int]] = None,
+        override_min_num: Optional[int] = None,
+    ) -> list[tuple[int, int]]:
+        min_num, max_num = self.resolve_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=use_thumbnail,
+        )
+        if override_min_num is not None:
+            min_num = override_min_num
+
+        return get_h2ovl_target_ratios(
+            min_num,
+            max_num,
+            prior_aspect_ratio=prior_aspect_ratio,
+        )
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        use_msac: Optional[bool] = None,
+    ) -> int:
+        use_msac = (self.use_msac if use_msac is None else use_msac)
+
+        use_thumbnail = self.use_thumbnail
+
+        if use_msac:
+            target_ratios_1 = self.resolve_target_ratios(
+                use_thumbnail=False,  # Applied in calculate_targets
+                override_min_num=1,
+            )
+            num_patches_1, _, _, aspect_ratio_1 = calculate_h2ovl_targets(
+                orig_width=image_width,
+                orig_height=image_height,
+                image_size=self.image_size,
+                target_ratios=target_ratios_1,
+                use_thumbnail=True,
+            )
+
+            target_ratios_2 = self.resolve_target_ratios(
+                use_thumbnail=False,  # Applied in calculate_targets
+                prior_aspect_ratio=aspect_ratio_1,
+                override_min_num=3,
+            )
+            num_patches_2, _, _, _ = calculate_h2ovl_targets(
+                orig_width=image_width,
+                orig_height=image_height,
+                image_size=self.image_size,
+                target_ratios=target_ratios_2,
+                use_thumbnail=True,
+            )
+
+            num_patches = num_patches_1 + num_patches_2 - 1
+        else:
+            target_ratios = self.resolve_target_ratios(
+                use_thumbnail=False,  # Applied in calculate_targets
+            )
+            num_patches, _, _, _ = calculate_h2ovl_targets(
+                orig_width=image_width,
+                orig_height=image_height,
+                image_size=self.image_size,
+                target_ratios=target_ratios,
+                use_thumbnail=use_thumbnail,
+            )
+
+        return num_patches * self.num_image_token
+
+    def _images_to_pixel_values_lst(
+        self,
+        images: list[Image.Image],
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> list[torch.Tensor]:
+        use_msac = self.use_msac if len(images) == 1 else False
+
+        min_num, max_num = self.resolve_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=False,  # Applied in image_to_pixel_values
+        )
+
+        return [
+            image_to_pixel_values_h2ovl(
+                image,
+                input_size=self.image_size,
+                min_num=min_num,
+                max_num=max_num,
+                use_thumbnail=self.use_thumbnail,
+                use_msac=use_msac,
+            ) for image in images
+        ]
+
+
+class H2OVLProcessingInfo(BaseInternVLProcessingInfo):
+
+    def get_hf_processor(self, **kwargs: object) -> H2OVLProcessor:
+        return self.ctx.init_processor(
+            H2OVLProcessor,
+            config=self.get_hf_config(),
+            tokenizer=self.get_tokenizer(),
+            **kwargs,
+        )
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[H2OVLProcessor],
+        use_msac: Optional[bool] = None,
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        return processor.get_num_image_tokens(
+            image_width=image_width,
+            image_height=image_height,
+            use_msac=use_msac,
+        )
+
+
+class H2OVLMultiModalProcessor(
+        BaseInternVLMultiModalProcessor[H2OVLProcessingInfo]):
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        if "image_num_patches" in out_mm_kwargs:
+            image_num_patches = out_mm_kwargs["image_num_patches"]
+            assert isinstance(image_num_patches, torch.Tensor)
+            image_num_patches = image_num_patches.tolist()
+        elif "image_embeds" in out_mm_kwargs:
+            # TODO: Use image size information in dictionary embedding inputs
+            # to compute num_patches (similar to Qwen2-VL)
+            image_num_patches = [None] * len(out_mm_kwargs["image_embeds"])
+        else:
+            image_num_patches = []
+
+        num_images = len(image_num_patches)
+
+        def get_replacement_internvl(item_idx: int):
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems))
+
+            if isinstance(images, ImageEmbeddingItems):
+                feature_size = images.get_feature_size(item_idx)
+            else:
+                image_size = images.get_image_size(item_idx)
+                feature_size = self.info.get_num_image_tokens(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                    processor=hf_processor,
+                    use_msac=None if num_images == 1 else False,
+                )
+
+            num_patches = image_num_patches[item_idx]
+            if num_patches is not None:
+                assert isinstance(num_patches, int)
+
+            return hf_processor.get_image_repl(feature_size, num_patches)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target="<image>",
+                replacement=get_replacement_internvl,
+            )
+        ]
+
+    def _cached_apply_hf_processor(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        tokenization_kwargs: Mapping[str, object],
+        *,
+        return_mm_hashes: bool,
+    ) -> tuple[list[int], MultiModalKwargs, Optional[MultiModalHashes], bool]:
+        # The processor logic is different for len(images) <= 1 vs > 1
+        # Since the processing cache assumes that the processor output is
+        # invariant of how many images are passed per prompt, we only
+        # perform caching for the most common case
+        if mm_data_items.get_count("image", strict=False) > 1:
+            return self._apply_hf_processor(
+                prompt=prompt,
+                mm_data_items=mm_data_items,
+                hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+                tokenization_kwargs=tokenization_kwargs,
+                return_mm_hashes=return_mm_hashes,
+            )
+
+        return super()._cached_apply_hf_processor(
+            prompt=prompt,
+            mm_data_items=mm_data_items,
+            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+            tokenization_kwargs=tokenization_kwargs,
+            return_mm_hashes=return_mm_hashes,
+        )
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    H2OVLMultiModalProcessor,
+    info=H2OVLProcessingInfo,
+    dummy_inputs=BaseInternVLDummyInputsBuilder)
+class H2OVLChatModel(InternVLChatModel):
+
+    def _init_vision_model(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig],
+        *,
+        is_mono: bool,
+        prefix: str,
+    ):
+        if not is_mono:
+            vision_feature_layer = config.select_layer
+            if vision_feature_layer < 0:
+                num_hidden_layers = (config.vision_config.num_hidden_layers +
+                                     vision_feature_layer + 1)
+            else:
+                num_hidden_layers = vision_feature_layer + 1
+
+            return InternVisionModel(
+                config.vision_config,
+                quant_config=quant_config,
+                num_hidden_layers_override=num_hidden_layers,
+                prefix=prefix,
+            )
+        else:
+            msg = "Monolith mode is not applicable to H2OVL"
+            raise NotImplementedError(msg)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/hyperclovax_vision.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/hyperclovax_vision.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5c94c7f3a7068fa15a9c02ce7e2b137182f3674
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/hyperclovax_vision.py
@@ -0,0 +1,1213 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# copied from : https://github.com/huggingface/transformers
+import ast
+import sys
+from collections import defaultdict
+from collections.abc import Iterable, Mapping, Sequence
+from functools import partial
+from itertools import chain
+from typing import Any, Literal, Optional, TypedDict, Union
+
+import numpy as np
+import PIL
+from einops import rearrange
+from PIL import Image
+
+if sys.version_info >= (3, 11):
+    import typing
+    Unpack = typing.Unpack
+else:
+    import typing_extensions
+    Unpack = typing_extensions.Unpack
+
+import torch
+import torch.nn as nn
+from timm.layers import LayerNorm, LayerNorm2d
+from timm.models.regnet import RegStage
+from transformers import BatchFeature, CLIPVisionConfig, SiglipVisionConfig
+from transformers.modeling_utils import no_init_weights
+
+from vllm.config import VllmConfig
+from vllm.inputs import InputProcessingContext
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import ImageSize, MultiModalDataItems
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, ProcessingCache,
+                                        PromptReplacement, PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+
+from .clip import CLIPVisionModel
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .siglip import SiglipVisionModel
+from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix
+from .vision import get_vision_encoder_info
+
+EOT = "<|endofturn|>"
+IMAGE_TOKEN: str = "<|dummy3|>"
+VIDEO_TOKEN: str = "<|_unuse_missing_100270|>"
+
+
+class HCXVisionMultimodalPixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values_images: list[torch.Tensor]
+    """
+    Shape: `[(num_grids, num_channels, height, width), ...]` if anyres
+    
+    Note that `height` or `width` may be different per batch and image,
+    in which case the data is passed as a list instead of a batched tensor.
+    """
+    image_sizes_images: list[tuple[Union[int, float]]]
+    """
+    Shape: `[(height, width), ...]`
+    """
+    vision_query_lengths_images: list[Union[int, float]]
+    pixel_values_videos: list[tuple[Union[int, float]]]
+    """
+    Shape: `[(num_grids, num_channels, height, width), ...]` if anyres
+    """
+    vision_query_lengths_videos: list[Union[int, float]]
+
+
+HCXVisionMultimodalInputs = Union[HCXVisionMultimodalPixelInputs]
+
+
+class HCXVisionProcessingInfo(BaseProcessingInfo):
+
+    def get_vision_encoder_info(self):
+        return get_vision_encoder_info(self.get_hf_config())
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None, "video": None}
+
+    def get_num_image_tokens(
+        self,
+        *,
+        vision_query_length: Union[int, list[int]],
+    ) -> int:
+        if isinstance(vision_query_length, int):
+            return vision_query_length
+        else:
+            return sum(vision_query_length)
+
+    def get_num_video_tokens(
+        self,
+        *,
+        vision_query_length: Union[int, list[int]],
+    ) -> int:
+        if isinstance(vision_query_length, int):
+            return vision_query_length
+        else:
+            return sum(vision_query_length)
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        vision_encoder_info = self.get_vision_encoder_info()
+        width = height = vision_encoder_info.get_image_size()
+        return ImageSize(width=width, height=height)
+
+    def get_max_image_tokens(self) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_image_tokens(
+            image_width=target_width,
+            image_height=target_height,
+        )
+
+
+class HCXVisionDummyInputsBuilder(
+        BaseDummyInputsBuilder[HCXVisionProcessingInfo]):
+
+    def get_dummy_text(
+        self,
+        mm_counts: Mapping[str, int],
+    ) -> str:
+        dummy_text = IMAGE_TOKEN * mm_counts.get(
+            "image", 0) + VIDEO_TOKEN * mm_counts.get("video", 0)
+        return dummy_text
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+        target_num_frames = 32
+        return {
+            "image":
+            self._get_dummy_images(
+                width=target_width,
+                height=target_height,
+                num_images=num_images,
+            ),
+            "video":
+            self._get_dummy_videos(
+                width=target_width - 1,
+                height=target_height - 1,
+                num_frames=target_num_frames,
+                num_videos=num_videos,
+            )
+        }
+
+
+class HCXVisionMultiModalProcessor(
+        BaseMultiModalProcessor[HCXVisionProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+
+        def replace_multimodal_token(
+            token_ids: torch.Tensor,
+            target_token: int,
+            repeats: list,
+        ):
+            output = list()
+            _repeats_idx = 0
+            for token_id in token_ids:
+                if token_id == target_token:
+                    output += [
+                        token_id.item(),
+                    ] * repeats[_repeats_idx]
+                    _repeats_idx += 1
+                else:
+                    output += [
+                        token_id.item(),
+                    ]
+            return torch.tensor(output, device=token_ids.device)
+
+        for video_idx, video_arr in enumerate(mm_data.get("videos", list())):
+            if video_arr.dtype == np.uint8:
+                continue
+            mm_data["videos"][video_idx] = video_arr.astype(np.uint8)
+
+        processed_outputs = self.info.ctx.call_hf_processor(
+            hf_processor=self.info.get_hf_processor(**mm_kwargs),
+            data=dict(
+                text=prompt,
+                images=None,
+                videos=None,
+            ),
+        )  # text-only
+
+        if len(mm_data) > 0:
+            # batchify input as a single item
+            images = mm_data.get("images", None)
+            num_images = 0
+            if images is not None:
+                num_images = len(images)
+                images = [
+                    images,
+                ]  # batchify
+
+            videos = mm_data.get("videos",
+                                 None)  # list of video in single conversation
+            num_videos = 0
+            if videos is not None:
+                num_videos = len(videos)
+                videos = [
+                    videos,
+                ]  # batchify
+
+            _processed_outputs = self.info.ctx.call_hf_processor(
+                hf_processor=self.info.get_hf_processor(**mm_kwargs),
+                data=dict(
+                    text=None,
+                    images=images,
+                    videos=videos,
+                ),
+            )  # mm-only
+
+            for k, v in _processed_outputs.items():
+                if len(v) < 1:
+                    continue
+                elif k.endswith("_images"):
+                    # list of list of 4D tensor -> list of 4D tensor
+                    _processed_outputs[k] = v[0]
+                elif k.endswith("_videos"):
+                    # list of list of 4D tensor -> list of 4D tensor
+                    v = v[0]
+                    if k == "pixel_values_videos":
+                        v = torch.cat(v, dim=0)
+                        _c, _w, _h = v.shape[-3:]
+                        v = v.reshape(num_videos, -1, _c, _w, _h)
+                        v = list(torch.unbind(v, dim=0))
+                    _processed_outputs[k] = v
+
+            if num_images > 0:
+                tokenizer = self.info.get_tokenizer()
+                processed_outputs["input_ids"] = torch.stack([
+                    replace_multimodal_token(
+                        token_ids=_input_ids,
+                        target_token=tokenizer.convert_tokens_to_ids(
+                            IMAGE_TOKEN),
+                        repeats=_processed_outputs[
+                            "vision_query_lengths_images"],
+                    ) for _input_ids in processed_outputs["input_ids"]
+                ],
+                                                             dim=0)
+
+            if num_videos > 0:
+                tokenizer = self.info.get_tokenizer()
+                processed_outputs["input_ids"] = torch.stack([
+                    replace_multimodal_token(
+                        token_ids=_input_ids,
+                        target_token=tokenizer.convert_tokens_to_ids(
+                            VIDEO_TOKEN),
+                        repeats=_processed_outputs[
+                            "vision_query_lengths_videos"],
+                    ) for _input_ids in processed_outputs["input_ids"]
+                ],
+                                                             dim=0)
+
+                _ratios = [
+                    len(_pixel_values) for _pixel_values in
+                    _processed_outputs["pixel_values_videos"]
+                ]
+                _num_per_videos = [
+                    int(_e / sum(_ratios) *
+                        len(_processed_outputs["vision_query_lengths_videos"]))
+                    for _e in _ratios
+                ]
+                _processed_outputs["vision_query_lengths_videos"] = [
+                    _processed_outputs["vision_query_lengths_videos"]
+                    [sum(_num_per_videos[:_i]):sum(_num_per_videos[:_i + 1])]
+                    for _i in range(0, num_videos)
+                ]
+
+            processed_outputs.update(_processed_outputs)
+
+        return processed_outputs
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_config = self.info.get_hf_config()
+        placeholder = {
+            "image": hf_config.image_token_id,
+            "video": hf_config.video_token_id,
+        }
+
+        def get_replacement_hyperclovax(
+            item_idx: int,
+            modality: str,
+            out_mm_kwargs: MultiModalKwargs,
+        ):
+            num_tokens = None
+            if modality == "image":
+                num_tokens = self.info.get_num_image_tokens(
+                    vision_query_length=out_mm_kwargs[
+                        "vision_query_lengths_images"][item_idx], )
+            if modality == "video":
+                num_tokens = self.info.get_num_video_tokens(
+                    vision_query_length=out_mm_kwargs[
+                        "vision_query_lengths_videos"][item_idx], )
+            assert isinstance(num_tokens, int)
+            return [
+                placeholder[modality],
+            ] * num_tokens
+
+        return [
+            PromptReplacement(
+                modality=modality,
+                target=[
+                    placeholder[modality],
+                ],
+                replacement=partial(
+                    get_replacement_hyperclovax,
+                    modality=modality,
+                    out_mm_kwargs=out_mm_kwargs,
+                ),
+            ) for modality in ("image", "video")
+        ]
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            # image
+            pixel_values_images=MultiModalFieldConfig.batched("image"),
+            image_sizes_images=MultiModalFieldConfig.batched("image"),
+            vision_query_lengths_images=MultiModalFieldConfig.batched("image"),
+            num_queries_vis_abstractors_images=MultiModalFieldConfig.batched(
+                "image"),
+            num_queries_vis_abstractors_slow_images=MultiModalFieldConfig.
+            batched("image"),
+            first_last_frames_slows_images=MultiModalFieldConfig.batched(
+                "image"),
+            # video
+            pixel_values_videos=MultiModalFieldConfig.batched("video"),
+            image_sizes_videos=MultiModalFieldConfig.batched("video"),
+            vision_query_lengths_videos=MultiModalFieldConfig.batched("video"),
+            num_queries_vis_abstractors_videos=MultiModalFieldConfig.batched(
+                "video"),
+            num_queries_vis_abstractors_slow_videos=MultiModalFieldConfig.
+            batched("video"),
+            first_last_frames_slows_videos=MultiModalFieldConfig.batched(
+                "video"),
+        )
+
+
+def _build_hcxvision_hf_info(
+    ctx: InputProcessingContext, ) -> HCXVisionProcessingInfo:
+    return HCXVisionProcessingInfo(ctx)
+
+
+def _build_hcxvision_hf_processor(
+    info: HCXVisionProcessingInfo,
+    dummy_inputs: BaseDummyInputsBuilder[HCXVisionProcessingInfo],
+    *,
+    cache: Optional[ProcessingCache] = None,
+) -> BaseMultiModalProcessor:
+    if isinstance(info, HCXVisionProcessingInfo):
+        return HCXVisionMultiModalProcessor(
+            info,
+            dummy_inputs,  # type: ignore
+            cache=cache,
+        )
+
+    raise NotImplementedError(type(info))
+
+
+def init_vision_tower_for_hcxvision(
+    vision_config,
+    quant_config: Optional[QuantizationConfig],
+    *,
+    use_nth_layer: Optional[int] = None,
+    require_post_norm: Optional[bool] = None,
+    prefix: str = "",
+) -> Union[CLIPVisionModel, SiglipVisionModel]:
+    num_hidden_layers = vision_config.num_hidden_layers
+    if not isinstance(use_nth_layer, int):
+        pass
+    elif use_nth_layer >= 0:
+        num_hidden_layers = use_nth_layer + 1
+    else:
+        num_hidden_layers = num_hidden_layers + use_nth_layer + 1
+
+    if isinstance(vision_config, CLIPVisionConfig):
+        return CLIPVisionModel(
+            vision_config,
+            quant_config=quant_config,
+            num_hidden_layers_override=num_hidden_layers,
+            require_post_norm=require_post_norm,
+            prefix=prefix,
+        )
+    elif isinstance(vision_config, SiglipVisionConfig):
+        return SiglipVisionModel(
+            vision_config,
+            quant_config=quant_config,
+            num_hidden_layers_override=num_hidden_layers,
+            require_post_norm=require_post_norm,
+            prefix=prefix,
+        )
+
+    msg = f"Unsupported vision config: {type(vision_config)}"
+    raise NotImplementedError(msg)
+
+
+class HCXVisionMlp(nn.Module):
+
+    def __init__(
+        self,
+        mm_projector_type,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        act_layer=nn.GELU,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.mm_projector_type = mm_projector_type
+        if self.mm_projector_type == "mlp":
+            self.fc1 = nn.Linear(in_features, hidden_features)
+            self.act = act_layer()
+            self.fc2 = nn.Linear(hidden_features, out_features)
+        elif self.mm_projector_type == "inverted_mlp":
+            self.fc1 = nn.Linear(in_features, 2 * hidden_features)
+            self.act = act_layer()
+            self.fc2 = nn.Linear(2 * hidden_features, out_features)
+        else:
+            raise NotImplementedError("{} is not implemented".format(
+                self.mm_projector_type))
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.fc2(x)
+        return x
+
+
+class HCXVisionCAbstractor(nn.Module):
+    """
+    This module is based on C-Abstractor, whose license is under apache-2.0.
+    You can check the original code at 
+    https://github.com/khanrc/honeybee/blob/main/honeybee/projectors/projectors.py
+    and we made necessary modifications.
+    """
+
+    def __init__(
+        self,
+        num_queries: int,
+        num_input_tokens: int,
+        encoder_hidden_size: int,
+        hidden_size: int,
+        output_hidden_size: int,
+        pos_emb: bool = True,
+        prenorm: bool = False,
+    ):
+        super().__init__()
+        self.num_input_tokens = num_input_tokens
+        self.output_hidden_size = output_hidden_size
+
+        # Positional embedding
+        if pos_emb:
+            self.pos_emb = torch.nn.Parameter(
+                torch.zeros(1, num_input_tokens, encoder_hidden_size))
+            self.pos_emb.data.normal_(mean=0.0, std=0.02)
+        else:
+            self.pos_emb = None
+
+        # (Optional) Pre-normalization layer
+        if prenorm:
+            self.prenorm = LayerNorm(encoder_hidden_size)
+        else:
+            self.prenorm = None
+
+        self.build_net(num_queries, encoder_hidden_size, hidden_size,
+                       output_hidden_size)
+        self.dtype = next(self.parameters()).dtype
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        num_queries_vis_abstractors: Optional[list[list[int]]] = None,
+        num_grids: Optional[list[int]] = None,
+    ) -> torch.Tensor:
+        if self.prenorm is not None:
+            x = self.prenorm(x)
+
+        if self.pos_emb is not None:
+            x = x + self.pos_emb
+
+        x = self._forward(
+            x,
+            num_queries_vis_abstractors=num_queries_vis_abstractors,
+            num_grids=num_grids,
+        )  # (B, L, output_hidden_size)
+
+        return x
+
+    def _forward(
+        self,
+        x: torch.Tensor,
+        num_queries_vis_abstractors: Optional[list[list[int]]] = None,
+        num_grids: Optional[list[int]] = None,
+    ) -> torch.Tensor:
+        # x: [B, L, dim]
+        B, L, dim = x.shape
+        hw = int(L**0.5)
+        x = rearrange(x, "b (h w) d -> b d h w", h=hw, w=hw)
+
+        if num_queries_vis_abstractors is not None:
+            assert num_grids is not None
+            return self._forward_adaptive_num_query(
+                x, num_queries_vis_abstractors, num_grids)
+
+        x = self.net(x)
+        x = rearrange(x, "b d h w -> b (h w) d")
+        x = self.readout(x)
+        return x
+
+    def _forward_adaptive_num_query(
+        self,
+        x: torch.Tensor,
+        num_queries_vis_abstractors: Optional[list[list[int]]] = None,
+        num_grids: Optional[list[int]] = None,
+    ) -> list[torch.Tensor]:
+        # self.net is consisted by 3 layers (s1, sampler, s2)
+        assert len(self.net) == 3
+
+        x = self.net[0](x)  # s1
+        new_x = []
+        for i, num_queries in enumerate(num_queries_vis_abstractors):
+            hw = int(num_queries**0.5)
+            sampler = nn.AdaptiveAvgPool2d((hw, hw))
+            out = sampler(x[num_grids[i]:num_grids[i + 1], :])
+            out = self.net[2](out)  # s2
+
+            out = rearrange(out, "b d h w -> b (h w) d")
+            out = self.readout(out)
+
+            new_x.append(out)
+        return new_x
+
+    def build_net(
+        self,
+        n_queries: int,
+        encoder_hidden_size: int,
+        hidden_size: int,
+        output_hidden_size: int,
+        depth: int = 3,
+        mlp_depth: int = 2,
+    ):
+        assert (n_queries**0.5).is_integer(
+        ), f"n_queries must be square number. n_queries: {n_queries}"
+        hw = int(n_queries**0.5)
+
+        # RegBlock = ResBlock + SE
+        RegBlock = partial(
+            RegStage,
+            stride=1,
+            dilation=1,
+            act_layer=nn.SiLU,
+            norm_layer=LayerNorm2d,
+        )
+
+        s1 = RegBlock(
+            depth,
+            encoder_hidden_size,
+            hidden_size,
+        )
+        sampler = nn.AdaptiveAvgPool2d((hw, hw))
+        s2 = RegBlock(
+            depth,
+            hidden_size,
+            hidden_size,
+        )
+
+        self.net = nn.Sequential(s1, sampler, s2)
+        self.readout = self.build_mlp(mlp_depth, hidden_size,
+                                      output_hidden_size)
+
+    def build_mlp(
+        self,
+        depth: int,
+        hidden_size: int,
+        output_hidden_size: int,
+    ):
+        layers = [nn.Linear(hidden_size, output_hidden_size)]
+        for _ in range(1, depth):
+            layers.append(nn.SiLU())
+            layers.append(nn.Linear(output_hidden_size, output_hidden_size))
+        return nn.Sequential(*layers)
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    _build_hcxvision_hf_processor,
+    info=_build_hcxvision_hf_info,
+    dummy_inputs=HCXVisionDummyInputsBuilder)
+class HCXVisionForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
+
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"]
+    }
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        **kwargs: Optional[Any],
+    ) -> None:
+        super().__init__()
+
+        # init configs
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        # text_config
+        text_config = config.text_config
+        if text_config.model_type in ["gpt2", "hyperclovax", "llama"]:
+            text_config._attn_implementation = "sdpa"
+        if text_config.model_type != "hyperclovax":
+            text_config.logits_scaling = 1.0
+        # vision_config
+        vision_config = config.vision_config
+        vision_config.auto_map = {}
+        vision_config.anyres = config.anyres
+        vision_config.max_num_grids = config.max_num_grids
+        self.dtype = vllm_config.model_config.dtype
+
+        ## possible_resolution should be matched with preprocessor_config.json
+        config.possible_resolutions = self._init_possible_resolutions(
+            config, vision_config)
+
+        # init models & parameters
+        with no_init_weights():  # weight will be loaded in from_pretrained
+            self.vision_model = init_vision_tower_for_hcxvision(
+                vision_config,
+                quant_config,
+                use_nth_layer=getattr(config, "use_nth_layer", -1),
+                require_post_norm=False,
+                prefix=maybe_prefix(prefix, "vision_model"),
+            )
+        self.mm_projector = self._init_mm_projector(config, text_config,
+                                                    vision_config)
+
+        self.lm_head_vocab_size = getattr(text_config, "padded_vocab_size",
+                                          text_config.vocab_size)
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        if config.anyres:
+            self.image_newline = nn.Parameter(
+                torch.empty(text_config.hidden_size, dtype=self.dtype))
+
+        self.config = config
+        self.vision_config = vision_config
+        self.text_config = text_config
+
+        # use_sum_loss = bool(kwargs.pop("use_sum_loss", False))
+        # self.reduction = self._init_reduction_type(use_sum_loss)
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return IMAGE_TOKEN
+        if modality.startswith("video"):
+            return VIDEO_TOKEN
+
+        raise ValueError("Only image or video modality is supported")
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(
+        self,
+        **kwargs: Unpack[HCXVisionMultimodalInputs],
+    ) -> Optional[MultiModalEmbeddings]:
+
+        multimodal_embeddings = list()
+        if kwargs.get("pixel_values_images") is not None:
+            for _pixel_values_images, _image_sizes_images in zip(
+                    kwargs["pixel_values_images"],
+                    kwargs["image_sizes_images"]):
+                _pixel_values_images = _pixel_values_images.unsqueeze(dim=0)
+                _image_sizes_images = _image_sizes_images.unsqueeze(dim=0)
+                _len_pixel_values_images = [
+                    len(pixel_value) for pixel_value in _pixel_values_images
+                ]
+                if isinstance(_image_sizes_images, torch.Tensor):
+                    _image_sizes_images = _image_sizes_images.detach().cpu(
+                    ).tolist()
+                _multimodal_embeddings_images = self.forward_images(
+                    pixel_values_images=_pixel_values_images,
+                    image_sizes_images=_image_sizes_images,
+                    len_pixel_values_images=_len_pixel_values_images,
+                )
+                _multimodal_embeddings_images = torch.cat(
+                    _multimodal_embeddings_images, dim=0)
+                multimodal_embeddings.append(_multimodal_embeddings_images)
+
+        if kwargs.get("pixel_values_videos") is not None:
+            for _pixel_values_videos, _vision_query_lengths_videos in zip(
+                    kwargs["pixel_values_videos"],
+                    kwargs["vision_query_lengths_videos"]):
+                _len_pixel_values_videos = [
+                    len(_vision_query_lengths)
+                    for _vision_query_lengths in _vision_query_lengths_videos
+                ]
+                _c, _w, _h = _pixel_values_videos.shape[-3:]
+                _pixel_values_videos = _pixel_values_videos.reshape(
+                    sum(_len_pixel_values_videos), -1, _c, _w,
+                    _h).unsqueeze(dim=0)
+                _multimodal_embeddings_videos = self.forward_videos(
+                    pixel_values_videos=_pixel_values_videos,
+                    len_pixel_values_videos=_len_pixel_values_videos,
+                )
+                _multimodal_embeddings_videos = torch.cat(
+                    _multimodal_embeddings_videos, dim=0)
+                multimodal_embeddings.append(_multimodal_embeddings_videos)
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if (kwargs.get("pixel_values_images") is not None
+                or kwargs.get("pixel_values_videos")
+                is not None):  # v0 compatibility
+            multimodal_embeddings = self.get_multimodal_embeddings(**kwargs)
+        if multimodal_embeddings is not None:
+            multimodal_embeddings = torch.cat(multimodal_embeddings, dim=0)
+            _mask_image = input_ids == self.config.image_token_id
+            _mask_video = input_ids == self.config.video_token_id
+            assert _mask_image.sum() + _mask_video.sum() == len(
+                multimodal_embeddings)
+
+            if multimodal_embeddings.dtype != inputs_embeds.dtype:
+                multimodal_embeddings = multimodal_embeddings.to(
+                    dtype=inputs_embeds.dtype)
+            if multimodal_embeddings.device != inputs_embeds.device:
+                multimodal_embeddings = multimodal_embeddings.to(
+                    device=inputs_embeds.device)
+
+            if _mask_image.sum() > 0:
+                inputs_embeds[
+                    _mask_image] = multimodal_embeddings[:sum(_mask_image)]
+            if _mask_video.sum() > 0:
+                inputs_embeds[_mask_video] = multimodal_embeddings[
+                    -sum(_mask_video):]
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings(input_ids=input_ids,
+                                                      **kwargs)
+            input_ids = None
+        hidden_states = self.language_model.model(input_ids,
+                                                  positions,
+                                                  intermediate_tensors,
+                                                  inputs_embeds=inputs_embeds)
+        return hidden_states
+
+    def forward_images(
+        self,
+        pixel_values_images: list[list[torch.FloatTensor]],
+        image_sizes_images: list[list[tuple[int, int]]],
+        len_pixel_values_images: list[int],
+    ) -> list[list[torch.Tensor]]:
+        if sum(len_pixel_values_images) == 0:
+            return None
+
+        concat_pixel_values_images = torch.cat(list(
+            chain(*pixel_values_images)),
+                                               dim=0)
+
+        visual_token_idx = 0 if "siglip" in self.vision_config.model_type else 1
+        image_forward_outs = self.vision_model(
+            concat_pixel_values_images)[:, visual_token_idx:]
+
+        image_forward_outs = image_forward_outs.to(
+            dtype=self.mm_projector.dtype)
+        image_forward_outs = self.mm_projector(image_forward_outs)  # b (h w) d
+
+        split_sizes = [
+            pixel_value.shape[0] for pixel_value in chain(*pixel_values_images)
+        ]
+        image_forward_outs = torch.split(image_forward_outs,
+                                         split_sizes,
+                                         dim=0)
+
+        # newline for anyres postprocessing
+        image_features = anyres_postprocessing(
+            image_forward_outs=image_forward_outs,
+            image_sizes=[
+                image_size for image_sizes in image_sizes_images
+                for image_size in image_sizes
+            ],
+            num_queries_vis_abstractor=self.config.
+            num_queries_vis_abstractor_image,
+            unpad=self.config.unpad,
+            patch_size=self.vision_config.patch_size,
+            grid_size=self.vision_config.image_size,
+            image_newline=self.image_newline,
+            possible_resolutions=self.config.possible_resolutions,
+        )
+        return image_features
+
+    def forward_videos(
+        self,
+        pixel_values_videos: list[list[torch.FloatTensor]],
+        len_pixel_values_videos: list[int],
+    ) -> list[torch.Tensor]:
+
+        len_video_grids = sum(len_pixel_values_videos)
+        if len_video_grids == 0:
+            return None
+
+        # Run Vision Model
+        concat_pixel_values_videos = torch.cat(list(
+            chain(*pixel_values_videos)),
+                                               dim=0)
+
+        visual_token_idx = 0 if "siglip" in self.vision_config.model_type else 1
+        video_forward_outs = self.vision_model(
+            concat_pixel_values_videos)[:, visual_token_idx:]
+
+        video_forward_outs = video_forward_outs.to(
+            dtype=self.mm_projector.dtype)
+
+        # Run MM-Projector
+        # len(num_grids) == len(num_queries_vis_abstractors) + 1
+        grid_idx = 0
+        num_grids = [
+            grid_idx
+        ]  # e.g. [0, 9, 18, 19, 27, 28, 36, 37, 45, 46, 54, 55, 56]
+        num_queries_vis_abstractors = [
+        ]  # e.g. [81, 81, 81, 9, 81, 9, 81, 9, 81, 9, 81, 9]
+        len_total_frames = video_forward_outs.shape[0]
+
+        if self.config.first_last_frames_slow:
+            # slowfast (first_last_frames_slow)
+            assert len_total_frames != 0
+            if len_total_frames <= 2:
+                num_queries_vis_abstractors.append(
+                    self.config.num_queries_vis_abstractor_video_slow)
+                grid_idx += len_total_frames
+                num_grids.append(grid_idx)
+            else:
+                num_queries_vis_abstractors.append(
+                    self.config.num_queries_vis_abstractor_video_slow)
+                grid_idx += 1
+                num_grids.append(grid_idx)
+
+                num_queries_vis_abstractors.append(
+                    self.config.num_queries_vis_abstractor_video_fast)
+                grid_idx += len_total_frames - 2
+                num_grids.append(grid_idx)
+
+                num_queries_vis_abstractors.append(
+                    self.config.num_queries_vis_abstractor_video_slow)
+                grid_idx += 1
+                num_grids.append(grid_idx)
+        else:
+            # slowfast
+            for pixel_values_frames in pixel_values_videos:
+                for pixel_values_frame in pixel_values_frames:
+                    if len(pixel_values_frame) > 0:
+                        num_queries_vis_abstractors.append(
+                            self.config.num_queries_vis_abstractor_video_slow)
+                        grid_idx += 1
+                        num_grids.append(grid_idx)
+                        num_queries_vis_abstractors.append(
+                            self.config.num_queries_vis_abstractor_video_fast)
+                        grid_idx = grid_idx + len(pixel_values_frame) - 1
+                        num_grids.append(grid_idx)
+
+        video_forward_outs = self.mm_projector(video_forward_outs,
+                                               num_queries_vis_abstractors,
+                                               num_grids)
+
+        video_features = []  # what we want to return
+        target_features = []
+        target_group_size = 0
+        group_counter = 0
+        video_groups = [
+            len(frame) for frames in pixel_values_videos for frame in frames
+        ]  # for concat video features after projector
+
+        for forward_out in video_forward_outs:
+            target_group_size += len(forward_out)
+            target_features.append(forward_out.flatten(0, 1))
+
+            video_group_size = video_groups[group_counter]
+            if video_group_size == target_group_size:
+                video_features.append(torch.cat(target_features, dim=0))
+                target_features = []
+                group_counter += 1
+                target_group_size = 0
+
+            elif video_group_size < target_group_size:
+                raise RuntimeError(f"video_group_size < target_group_size!! \
+                        [{video_group_size} < {target_group_size}]")
+
+        assert len(target_features
+                   ) == 0, f"target_features is not empty!! {target_features}"
+        assert len(video_groups) == len(video_features)
+
+        return video_features
+
+    def _prepare_multimodal_kwargs(self, **kwargs: object):
+        output = defaultdict(list)
+        for k, v in kwargs.items():
+            if len(v) < 1 or len(v[0]) < 1:
+                continue  # if empty batch of empty sample
+
+            new_k, is_video = k, False
+            if (not k.endswith("_images") and not k.endswith("_videos")):
+                pass
+            else:
+                new_k, is_video = k.split("_")[:-1], k.split("_")[-1]
+                new_k = "_".join(new_k)
+                is_video = is_video == "videos"
+
+            for _sample_idx, _v in enumerate(v):  # batch -> sample
+                if new_k not in ["pixel_values"]:
+                    if len(output[new_k]) < _sample_idx + 1:
+                        output[new_k].append(list())
+                    _v = _v.detach().cpu().numpy().tolist()
+                    output[new_k][_sample_idx] += _v
+                elif isinstance(_v, torch.Tensor):
+                    if len(output[new_k]) < _sample_idx + 1:
+                        output[new_k].append(list())
+                        output["is_videos"].append(list())
+                    _v = list(torch.unbind(_v, dim=0))
+                    output[new_k][_sample_idx] += _v
+                    output["is_videos"][_sample_idx] += [
+                        is_video,
+                    ] * len(_v)
+        return dict(output)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(
+        self,
+        weights: Iterable[tuple[str, torch.Tensor]],
+    ) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def _init_possible_resolutions(
+        self,
+        config,
+        vision_config,
+    ):
+        if not getattr(config, "possible_resolutions", []):
+            possible_resolutions = []
+            if config.anyres:
+                assert config.max_num_grids > 0
+                for i in range(1, config.max_num_grids + 1):
+                    for j in range(1, config.max_num_grids + 1):
+                        if i == 1 and j == 1 and not config.use_1x1_grid:
+                            continue
+                        if i * j <= config.max_num_grids:
+                            possible_resolutions.append([i, j])
+
+                possible_resolutions = [[
+                    ys * vision_config.image_size,
+                    xs * vision_config.image_size
+                ] for ys, xs in possible_resolutions]
+            return possible_resolutions
+        else:
+            return config.possible_resolutions
+
+    def _init_mm_projector(
+        self,
+        config,
+        text_config,
+        vision_config,
+    ):
+        input_hidden_size = vision_config.hidden_size
+        if config.mm_projector_type == "linear":
+            mm_projector = nn.Linear(input_hidden_size,
+                                     text_config.hidden_size)
+            mm_projector.dtype = next(mm_projector.parameters()).dtype
+        elif config.mm_projector_type == "cabstractor":
+            mm_projector = HCXVisionCAbstractor(
+                num_queries=config.num_queries_vis_abstractor_image,
+                num_input_tokens=(vision_config.image_size //
+                                  vision_config.patch_size)**2,
+                encoder_hidden_size=input_hidden_size,
+                hidden_size=input_hidden_size,
+                output_hidden_size=text_config.hidden_size,
+                pos_emb=config.proj_pos_emb,
+                prenorm=config.proj_prenorm,
+            )
+        else:
+            mm_projector = HCXVisionMlp(
+                config.mm_projector_type,
+                input_hidden_size,
+                hidden_features=input_hidden_size,
+                out_features=self.text_config.hidden_size,
+            )
+        return mm_projector
+
+
+def unpad_image(tensor: torch.Tensor,
+                original_size: tuple[int, int]) -> torch.Tensor:
+    original_width, original_height = original_size
+    current_height, current_width = tensor.shape[1:]
+
+    original_aspect_ratio = original_width / original_height
+    current_aspect_ratio = current_width / current_height
+
+    if original_aspect_ratio > current_aspect_ratio:
+        scale_factor = current_width / original_width
+        new_height = int(original_height * scale_factor)
+        padding = (current_height - new_height) // 2
+        unpadded_tensor = tensor[:, padding:current_height - padding, :]
+    else:
+        scale_factor = current_height / original_height
+        new_width = int(original_width * scale_factor)
+        padding = (current_width - new_width) // 2
+        unpadded_tensor = tensor[:, :, padding:current_width - padding]
+
+    return unpadded_tensor
+
+
+def select_best_resolution(original_size: tuple,
+                           possible_resolutions: list) -> tuple:
+    original_height, original_width = original_size
+    best_fit = None
+    max_effective_resolution = 0
+    min_wasted_resolution = float("inf")
+
+    for height, width in possible_resolutions:
+        scale = min(width / original_width, height / original_height)
+        downscaled_width, downscaled_height = int(original_width * scale), int(
+            original_height * scale)
+        effective_resolution = min(downscaled_width * downscaled_height,
+                                   original_width * original_height)
+        wasted_resolution = (width * height) - effective_resolution
+
+        if effective_resolution > max_effective_resolution or (
+                effective_resolution == max_effective_resolution
+                and wasted_resolution < min_wasted_resolution):
+            max_effective_resolution = effective_resolution
+            min_wasted_resolution = wasted_resolution
+            best_fit = (height, width)
+
+    return best_fit
+
+
+def get_anyres_image_grid_shape(
+    image_size: tuple[int, int],
+    grid_pinpoints: Union[str, list[tuple[int, int]]],
+    patch_size: int,
+) -> tuple[int, int]:
+    possible_resolutions = grid_pinpoints if isinstance(
+        grid_pinpoints, list) else ast.literal_eval(grid_pinpoints)
+
+    original_width, original_height = image_size
+    height, width = select_best_resolution((original_height, original_width),
+                                           possible_resolutions)
+    return width // patch_size, height // patch_size
+
+
+def reshape_and_unpad_image_features(
+    image_feature: torch.Tensor,
+    height: int,
+    width: int,
+    image_size: tuple[int, int],
+    possible_resolutions: list[tuple[int, int]],
+    grid_size: int,
+    unpad: bool,
+    image_newline: torch.Tensor,
+) -> torch.Tensor:
+    base_image_feature = image_feature[0]
+    image_feature = image_feature[1:]
+
+    assert (height * width == base_image_feature.shape[0]
+            ), f"height: {height}, width: {width}, \
+        base_image_feature.shape[0]: {base_image_feature.shape[0]}"
+
+    num_patch_width, num_patch_height = get_anyres_image_grid_shape(
+        image_size, possible_resolutions, grid_size)
+    image_feature = image_feature.view(num_patch_height, num_patch_width,
+                                       height, width, -1)
+
+    if unpad:
+        image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
+        image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+        image_feature = unpad_image(image_feature, image_size)
+        image_feature = torch.cat(
+            (
+                image_feature,
+                image_newline[:, None, None].expand(
+                    *image_feature.shape[:-1], 1).to(image_feature.device),
+            ),
+            dim=-1,
+        )
+        image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+    else:
+        image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
+        image_feature = image_feature.flatten(0, 3)
+    image_feature = torch.cat((base_image_feature, image_feature), dim=0)
+
+    return image_feature
+
+
+def anyres_postprocessing(
+    image_forward_outs: list[torch.FloatTensor],
+    image_sizes: list[list[int]],
+    possible_resolutions: list[tuple[int, int]],
+    patch_size: int,
+    grid_size: int,
+    image_newline: torch.FloatTensor,
+    num_queries_vis_abstractor: int = -1,
+    unpad: bool = False,
+) -> list[torch.FloatTensor]:
+    height = width = grid_size // patch_size
+
+    if num_queries_vis_abstractor > 0:
+        assert (num_queries_vis_abstractor**0.5
+                ).is_integer(), "n_queries must be square number"
+        height = width = int(num_queries_vis_abstractor**0.5)
+
+    # post-processing (unpad, add newline)
+    new_image_features = []
+    for image_idx, image_feature in enumerate(image_forward_outs):
+        if image_feature.shape[0] > 1:
+            image_feature = reshape_and_unpad_image_features(
+                image_feature=image_feature,
+                height=height,
+                width=width,
+                image_size=image_sizes[image_idx],
+                possible_resolutions=possible_resolutions,
+                grid_size=grid_size,  # Pass grid info if needed by helper
+                unpad=unpad,
+                image_newline=image_newline,
+            )
+        else:
+            image_feature = image_feature[0]
+            image_feature = torch.cat(
+                (image_feature, image_newline[None].to(image_feature.device)),
+                dim=0)
+        new_image_features.append(image_feature)
+    image_features = new_image_features
+    return image_features
+
+
+def resize_image(
+    image: Union[np.ndarray, PIL.Image.Image],
+    max_side: int = 378,
+) -> np.ndarray:
+    image_arr = image
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+
+    width, height = image.size
+    cur_max_size = max(width, height)
+    if cur_max_size <= max_side:
+        return image_arr
+
+    scale = max_side / cur_max_size
+    width = int(width * scale)
+    height = int(height * scale)
+    image = image.resize((width, height), Image.LANCZOS)
+    image_arr = np.array(image)
+    return image_arr
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/idefics2_vision_model.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/idefics2_vision_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e27200fb1c8982da8c1ab06e758684458a8a847
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/idefics2_vision_model.py
@@ -0,0 +1,389 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://github.com/huggingface/transformers/blob/v4.43.2/src/transformers/models/idefics2/modeling_idefics2.py
+# Copyright 2024 The vLLM team.
+# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Idefics2 model."""
+
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+from torch import nn
+from transformers.models.idefics2.configuration_idefics2 import (
+    Idefics2Config, Idefics2VisionConfig)
+
+from vllm.attention.layer import MultiHeadAttention
+from vllm.distributed import divide, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+
+class Idefics2VisionEmbeddings(nn.Module):
+    """
+    This is a modified version of `siglip.modelign_siglip.SiglipVisionEmbeddings
+    ` to enable images of variable
+    resolution.
+
+    The modifications are adapted from [Patch n' Pack: NaViT, a Vision
+    Transformer for any Aspect Ratio and Resolution](https://arxiv.org/abs/2307.06304)
+    which allows treating images in their native aspect ratio and without the
+    need to resize them to the same fixed size. In particular, we start from the
+    original pre-trained SigLIP model(which uses images of fixed-size square
+    images) and adapt it by training on images of variable resolutions.
+    """
+
+    def __init__(self, config: Idefics2VisionConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+        self.num_patches_per_side = self.image_size // self.patch_size
+        self.num_patches = self.num_patches_per_side**2
+        self.num_positions = self.num_patches
+        self.position_embedding = nn.Embedding(self.num_positions,
+                                               self.embed_dim)
+
+    def forward(self,
+                pixel_values: torch.FloatTensor,
+                patch_attention_mask: torch.BoolTensor,
+                tgt_sizes: Optional[torch.IntTensor] = None) -> torch.Tensor:
+        batch_size, _, max_im_h, max_im_w = pixel_values.shape
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values.to(target_dtype))
+        embeddings = patch_embeds.flatten(2).transpose(1, 2)
+        max_nb_patches_h, max_nb_patches_w = (
+            max_im_h // self.patch_size,
+            max_im_w // self.patch_size,
+        )
+        boundaries = torch.arange(1 / self.num_patches_per_side, 1.0,
+                                  1 / self.num_patches_per_side)
+        position_ids = torch.full(size=(batch_size,
+                                        max_nb_patches_h * max_nb_patches_w),
+                                  fill_value=0)
+
+        for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
+
+            if tgt_sizes is not None:
+                nb_patches_h = tgt_sizes[batch_idx][0]
+                nb_patches_w = tgt_sizes[batch_idx][1]
+            else:
+                nb_patches_h = p_attn_mask[:, 0].sum()
+                nb_patches_w = p_attn_mask[0].sum()
+            fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
+            fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
+            bucket_coords_h = torch.bucketize(fractional_coords_h,
+                                              boundaries,
+                                              right=True)
+            bucket_coords_w = torch.bucketize(fractional_coords_w,
+                                              boundaries,
+                                              right=True)
+            pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side +
+                       bucket_coords_w).flatten()
+            position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
+        position_ids = position_ids.to(self.position_embedding.weight.device)
+        embeddings = embeddings + self.position_embedding(position_ids)
+        return embeddings
+
+
+class Idefics2VisionAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        config: Idefics2VisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"  # noqa: E501
+                f" {self.num_heads}).")
+        self.scale = self.head_dim**-0.5
+        self.dropout = config.attention_dropout
+        self.qkv_proj = QKVParallelLinear(
+            self.embed_dim,
+            self.head_dim,
+            self.num_heads,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.out_proj = RowParallelLinear(
+            self.embed_dim,
+            self.embed_dim,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
+        self.attn = MultiHeadAttention(self.num_heads_per_partition,
+                                       self.head_dim, self.scale)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(
+            hidden_states
+        )  # batch_size, q_len, 3 * num_heads_per_partition * head_dim
+        query_states, key_states, value_states = qkv.chunk(3, dim=-1)
+        out = self.attn(query_states, key_states, value_states)
+        attn_output, _ = self.out_proj(out)
+        return attn_output
+
+
+class Idefics2VisionMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: Idefics2VisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.activation_fn = get_act_fn(config.hidden_act)
+        self.fc1 = ColumnParallelLinear(
+            config.hidden_size,
+            config.intermediate_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+        )
+        self.fc2 = RowParallelLinear(
+            config.intermediate_size,
+            config.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        return hidden_states
+
+
+class Idefics2EncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Idefics2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.self_attn = Idefics2VisionAttention(config,
+                                                 quant_config=quant_config,
+                                                 prefix=f"{prefix}.self_attn")
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim,
+                                        eps=config.layer_norm_eps)
+        self.mlp = Idefics2VisionMLP(config,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.mlp")
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim,
+                                        eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`):
+                Input to the layer of shape `(batch, seq_len, embed_dim)`.
+
+        """
+        residual = hidden_states
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states = self.self_attn(hidden_states)
+        hidden_states = residual + hidden_states
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class Idefics2Encoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention
+    layers. Each layer is a
+    [`Idefics2EncoderLayer`].
+
+    Args:
+        config: Idefics2Config
+    """
+
+    def __init__(
+        self,
+        config: Idefics2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+
+        if num_hidden_layers_override is None:
+            num_hidden_layers = config.num_hidden_layers
+        else:
+            num_hidden_layers = num_hidden_layers_override
+
+        self.layers = nn.ModuleList([
+            Idefics2EncoderLayer(config,
+                                 quant_config=quant_config,
+                                 prefix=f"{prefix}.layers.{layer_idx}")
+            for layer_idx in range(num_hidden_layers)
+        ])
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            inputs_embeds (torch.Tensor):
+                Optionally, instead of passing `input_ids` you can choose to
+                directly pass an embedded representation.
+                This is useful if you want more control over how to convert
+                `input_ids` indices into associated vectorsthan the model's
+                internal embedding lookup matrix.
+        """
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layers:
+            layer_outputs = encoder_layer(hidden_states)
+            hidden_states = layer_outputs
+        return hidden_states
+
+
+class Idefics2VisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        config: Idefics2VisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        require_post_norm: bool = True,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        embed_dim = config.hidden_size
+        self.config = config
+        self.embeddings = Idefics2VisionEmbeddings(config)
+        self.encoder = Idefics2Encoder(
+            config,
+            quant_config=quant_config,
+            num_hidden_layers_override=num_hidden_layers_override,
+            prefix=f"{prefix}.encoder")
+
+        num_hidden_layers = config.num_hidden_layers
+        if len(self.encoder.layers) > config.num_hidden_layers:
+            raise ValueError(
+                f"The original encoder only has {num_hidden_layers} "
+                f"layers, but you requested {len(self.encoder.layers)} layers."
+            )
+
+        self.require_post_norm = require_post_norm
+        self.post_layernorm = nn.LayerNorm(
+            embed_dim,
+            eps=config.layer_norm_eps,
+        ) if require_post_norm else nn.Identity()
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def forward(
+        self,
+        pixel_values,
+        patch_attention_mask: Optional[torch.BoolTensor] = None,
+        tgt_sizes: Optional[torch.IntTensor] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.embeddings(
+            pixel_values=pixel_values,
+            patch_attention_mask=patch_attention_mask,
+            tgt_sizes=tgt_sizes,
+        )
+        encoder_outputs = self.encoder(hidden_states)
+        last_hidden_state = self.post_layernorm(encoder_outputs)
+        return last_hidden_state
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        layer_count = len(self.encoder.layers)
+
+        for name, loaded_weight in weights:
+            # skip pooling header
+            if name.startswith("head."):
+                continue
+
+            # post_layernorm is optional
+            if (name.startswith("post_layernorm.")
+                    and not self.require_post_norm):
+                continue
+
+            # omit layers when num_hidden_layers_override is set
+            if name.startswith("encoder.layers."):
+                layer_idx = int(name.split(".")[2])
+                if layer_idx >= layer_count:
+                    continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/idefics3.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/idefics3.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c01789b900667a89c39c1fe4cf507473270a085
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/idefics3.py
@@ -0,0 +1,758 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Idefics3 model compatible with HuggingFace weights."""
+
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Literal, Optional, Union
+
+import torch
+from torch import nn
+from transformers import (BatchFeature, Idefics3Config, Idefics3ImageProcessor,
+                          Idefics3Processor)
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import ImageProcessorItems, ImageSize
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo,
+                                        MultiModalDataItems, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+# yapf: enable
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+# yapf: disable
+from .idefics2_vision_model import (
+    Idefics2VisionTransformer as Idefics3VisionTransformer)
+# yapf: enable
+from .interfaces import MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal
+from .llama import LlamaModel
+from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+
+class Idefics3ImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - bn: Batch size * number of images
+        - bnp: Batch size * number of images * number of patches
+        - c: Number of channels (3)
+        - h: Height
+        - w: Width
+    """
+    type: Literal["pixel_values"]
+    pixel_values: Annotated[torch.Tensor, TensorShape("bnp", 3, "h", "w")]
+    pixel_attention_mask: torch.Tensor
+    num_patches: Annotated[torch.Tensor, TensorShape("bn")]
+
+
+class Idefics3ImageEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - bn: Batch size * number of images
+        - f: Image feature size
+        - h: Hidden size (must match the hidden size of language model backbone)
+    """
+    type: Literal["image_embeds"]
+    data: Annotated[torch.Tensor, TensorShape("bn", "f", "h")]
+
+
+ImageInputs = Union[Idefics3ImagePixelInputs, Idefics3ImageEmbeddingInputs]
+
+
+class Idefics3ProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_processor(self, **kwargs: object) -> Idefics3Processor:
+        return self.ctx.get_hf_processor(Idefics3Processor, **kwargs)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def _resize_output_size(self,
+                            *,
+                            height: int,
+                            width: int,
+                            max_len: Optional[int] = None,
+                            min_len: int = 1,
+                            max_size: Optional[int] = None) -> tuple[int, int]:
+        # Set default value for max_len if not provided
+        max_len = max(height, width) if max_len is None else max_len
+        aspect_ratio = width / height
+
+        # Handle the maximum size constraint
+        if max_size is not None:
+            max_len = min(max_len, max_size)
+
+        # Adjust dimensions according to the aspect ratio
+        if width >= height:
+            width = max_len
+            height = int(width / aspect_ratio)
+        else:
+            height = max_len
+            width = int(height * aspect_ratio)
+
+        # Ensure both width and height are even (if needed)
+        height += height % 2
+        width += width % 2
+
+        # Ensure dimensions are not smaller than the minimum length
+        height = max(height, min_len)
+        width = max(width, min_len)
+
+        return height, width
+
+    def _get_resize_output_image_size(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        resolution_max_side: int,
+    ) -> tuple[int, int]:
+        hf_processor = self.get_hf_processor()
+        image_processor: Idefics3ImageProcessor = hf_processor.image_processor
+        max_image_size = image_processor.size['longest_edge']
+        if resolution_max_side > max_image_size:
+            raise ValueError(
+                "`resolution_max_side` cannot be larger than `max_image_size`")
+
+        height, width = image_height, image_width
+
+        # Find the output size, when rescaling the longest edge to max_len and
+        # preserving the aspect ratio
+        height, width = self._resize_output_size(height=height,
+                                                 width=width,
+                                                 max_len=resolution_max_side)
+        return height, width
+
+    def _get_image_feature_grid_size(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Idefics3Processor],
+    ) -> tuple[int, int]:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        image_processor: Idefics3ImageProcessor = processor.image_processor
+
+        max_image_size = image_processor.max_image_size['longest_edge']
+        size = image_processor.size['longest_edge']
+        assert size % max_image_size == 0, (
+            "`longest_edge` in image_processor's `size` must be divisible by "
+            "`longest_edge` in `max_image_size`, this may be caused by "
+            "incorrect mm_kwargs override.")
+
+        resized_height, resized_width = self._get_resize_output_image_size(
+            image_width=image_width,
+            image_height=image_height,
+            resolution_max_side=size,
+        )
+        if resized_height > max_image_size or resized_width > max_image_size:
+            grid_h = math.ceil(resized_height / max_image_size)
+            grid_w = math.ceil(resized_width / max_image_size)
+        else:
+            grid_h = grid_w = 0
+        return grid_w, grid_h
+
+    def get_num_patches(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Idefics3Processor],
+    ) -> int:
+        grid_w, grid_h = self._get_image_feature_grid_size(
+            image_width=image_width,
+            image_height=image_height,
+            processor=processor,
+        )
+
+        return grid_w * grid_h + 1
+
+    def _get_image_token(
+            self,
+            processor: Optional[Idefics3Processor]) -> tuple[str, str, str]:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        image_token = processor.image_token
+        fake_image_token = processor.fake_image_token
+        global_image_token = processor.global_image_tag
+        return image_token, fake_image_token, global_image_token
+
+    def get_image_repl(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Idefics3Processor],
+    ) -> str:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        image_token, fake_image_token, global_img_token = self._get_image_token(
+            processor)
+        image_seq_len = processor.image_seq_len
+        grid_placeholder = "<row_{n_h}_col_{n_w}>"
+
+        p_img = image_token * image_seq_len
+        global_img_placeholder = fake_image_token + global_img_token + p_img
+        tile_img_placeholder = fake_image_token + grid_placeholder + p_img
+
+        grid_w, grid_h = self._get_image_feature_grid_size(
+            image_width=image_width,
+            image_height=image_height,
+            processor=processor,
+        )
+        if grid_w == 0 and grid_h == 0:
+            return global_img_placeholder + fake_image_token
+
+        tiles_placeholder = list[str]()
+        for i in range(grid_h):
+            for j in range(grid_w):
+                placeholder_per_tile = tile_img_placeholder.format(n_h=i + 1,
+                                                                   n_w=j + 1)
+                tiles_placeholder.append(placeholder_per_tile)
+                # Add line break if it is the last tile in the row
+                if j == grid_w - 1:
+                    tiles_placeholder.append("\n")
+
+        return "".join([
+            *tiles_placeholder,
+            "\n",
+            global_img_placeholder,
+            fake_image_token,
+        ])
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Idefics3Processor],
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        num_patches = self.get_num_patches(
+            image_width=image_width,
+            image_height=image_height,
+            processor=processor,
+        )
+
+        return num_patches * processor.image_seq_len
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        processor = self.get_hf_processor()
+        image_processor: Idefics3ImageProcessor = processor.image_processor
+
+        return ImageSize(
+            width=image_processor.size["longest_edge"],
+            height=image_processor.size["longest_edge"],
+        )
+
+
+class Idefics3DummyInputsBuilder(BaseDummyInputsBuilder[Idefics3ProcessingInfo]
+                                 ):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        processor = self.info.get_hf_processor()
+        image_token, _, _ = self.info._get_image_token(processor)
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        hf_processor = self.info.get_hf_processor()
+        image_processor: Idefics3ImageProcessor = hf_processor.image_processor
+        longest_edge = image_processor.max_image_size['longest_edge']
+
+        return {
+            "image":
+            self._get_dummy_images(width=longest_edge,
+                                   height=longest_edge,
+                                   num_images=num_images)
+        }
+
+
+class Idefics3MultiModalProcessor(
+        BaseMultiModalProcessor[Idefics3ProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        # Text-only input not supported in composite processor
+        if not (images := mm_data.get("images", [])):
+            prompt_ids = self.info.get_tokenizer().encode(prompt)
+            prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
+            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
+
+        processed_outputs = super()._call_hf_processor(
+            prompt,
+            mm_data,
+            mm_kwargs,
+            tok_kwargs,
+        )
+
+        parsed_images = (self._get_data_parser().parse_mm_data({
+            "image": images
+        }).get_items("image", ImageProcessorItems))
+        image_sizes = [
+            parsed_images.get_image_size(i) for i in range(len(parsed_images))
+        ]
+        hf_processor = self.info.get_hf_processor(**mm_kwargs)
+
+        num_patches = [
+            self.info.get_num_patches(
+                image_width=size.width,
+                image_height=size.height,
+                processor=hf_processor,
+            ) for size in image_sizes
+        ]
+        processed_outputs["num_patches"] = torch.tensor(num_patches)
+
+        # Remove the extra batch dimension
+        processed_outputs["pixel_values"].squeeze_(0)
+        processed_outputs["pixel_attention_mask"].squeeze_(0)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        num_patches = hf_inputs.get("num_patches", torch.empty(0))
+
+        return dict(
+            pixel_values=MultiModalFieldConfig.flat_from_sizes(
+                "image", num_patches),
+            pixel_attention_mask=MultiModalFieldConfig.flat_from_sizes(
+                "image", num_patches),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+            num_patches=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_token, _, _ = self.info._get_image_token(hf_processor)
+
+        def get_replacement_idefics3(item_idx: int) -> PromptUpdateDetails:
+            images = mm_items.get_items("image", ImageProcessorItems)
+
+            image_size = images.get_image_size(item_idx)
+
+            image_repl = self.info.get_image_repl(
+                image_width=image_size.width,
+                image_height=image_size.height,
+                processor=hf_processor,
+            )
+
+            return PromptUpdateDetails.select_text(
+                image_repl,
+                embed_text=image_token,
+            )
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=image_token,
+                replacement=get_replacement_idefics3,
+            )
+        ]
+
+
+class Idefics3SimpleMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: Idefics3Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        input_size = config.vision_config.hidden_size * (config.scale_factor**
+                                                         2)
+        output_size = config.text_config.hidden_size
+        self.proj = ReplicatedLinear(
+            input_size,
+            output_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "proj"),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out, _ = self.proj(x)
+        return out
+
+
+class Idefics3Connector(nn.Module):
+
+    def __init__(
+        self,
+        config: Idefics3Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.scale_factor = config.scale_factor
+        self.modality_projection = Idefics3SimpleMLP(
+            config,
+            quant_config,
+            prefix=maybe_prefix(prefix, "modality_projection"),
+        )
+
+    def pixel_shuffle(self,
+                      x: torch.Tensor,
+                      scale_factor: int = 2) -> torch.Tensor:
+        bsz, seq, embed_dim = x.size()
+        height = width = int(seq**0.5)
+        x = x.view(bsz, height, width, embed_dim)
+        x = x.view(bsz, height, int(width / scale_factor),
+                   embed_dim * scale_factor)
+        x = x.permute(0, 2, 1, 3)
+        x = x.reshape(
+            bsz,
+            int(width / scale_factor),
+            int(height / scale_factor),
+            embed_dim * (scale_factor**2),
+        )
+        x = x.permute(0, 2, 1, 3)
+        x = x.reshape(bsz, int(seq / (scale_factor**2)),
+                      embed_dim * (scale_factor**2))
+        return x
+
+    def forward(self, image_hidden_states: torch.Tensor) -> torch.Tensor:
+        image_hidden_states = self.pixel_shuffle(image_hidden_states,
+                                                 self.scale_factor)
+        image_hidden_states = self.modality_projection(image_hidden_states)
+        return image_hidden_states
+
+
+class Idefics3Model(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config: Idefics3Config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.vocab_size = self.config.text_config.vocab_size
+        self.vision_model = Idefics3VisionTransformer(
+            config.vision_config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "vision_model"))
+        self.connector = Idefics3Connector(
+            config,
+            quant_config,
+            prefix=maybe_prefix(prefix, "connector"),
+        )
+        self.text_model = LlamaModel(
+            vllm_config=vllm_config.with_hf_config(config.text_config),
+            prefix=maybe_prefix(prefix, "text_model"),
+        )
+
+        self.image_seq_len = int(
+            ((config.vision_config.image_size //
+              config.vision_config.patch_size)**2) / (config.scale_factor**2))
+        self.image_token_id = self.config.image_token_id
+
+    def image_pixels_to_features(
+        self,
+        pixel_values: torch.Tensor,
+        pixel_attention_mask: torch.Tensor,
+    ) -> torch.Tensor:
+        # NOTE: we skip the step to select the vision feature layer since
+        # this is already done inside the vision tower
+        pixel_values = pixel_values.to(
+            dtype=self.vision_model.embeddings.patch_embedding.weight.dtype
+        )  # fp16 compatibility
+
+        # Remove padding images - padding images are full 0.
+        nb_values_per_image = pixel_values.shape[1:].numel()
+        real_images_inds = (pixel_values == 0.0).sum(
+            dim=(-1, -2, -3)) != nb_values_per_image
+        pixel_values = pixel_values[real_images_inds].contiguous()
+
+        # Handle the vision attention mask
+        # Remove padding images from the mask
+        pixel_attention_mask = pixel_attention_mask[
+            real_images_inds].contiguous()
+
+        patch_size = self.config.vision_config.patch_size
+        patches_subgrid = pixel_attention_mask.unfold(dimension=1,
+                                                      size=patch_size,
+                                                      step=patch_size)
+        patches_subgrid = patches_subgrid.unfold(dimension=2,
+                                                 size=patch_size,
+                                                 step=patch_size)
+        patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
+
+        # Get sequence from the vision encoder
+        image_hidden_states = self.vision_model(
+            pixel_values=pixel_values,
+            patch_attention_mask=patch_attention_mask,
+        )
+
+        return image_hidden_states
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.text_model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+
+        hidden_states = self.text_model(
+            input_ids,
+            positions,
+            intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Idefics3MultiModalProcessor,
+    info=Idefics3ProcessingInfo,
+    dummy_inputs=Idefics3DummyInputsBuilder)
+class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                       SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<image>"
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.model = Idefics3Model(vllm_config=vllm_config,
+                                   prefix=maybe_prefix(prefix, "model"))
+        self.image_token_id = self.config.image_token_id
+
+        self.lm_head = ParallelLMHead(
+            config.text_config.vocab_size,
+            config.text_config.hidden_size,
+            quant_config=quant_config,
+        )
+        if self.config.text_config.tie_word_embeddings:
+            self.lm_head.weight = self.model.text_model.wte.weight
+        self.logits_processor = LogitsProcessor(config.text_config.vocab_size)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[ImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+
+            return Idefics3ImageEmbeddingInputs(
+                type="image_embeds",
+                data=flatten_bn(image_embeds, concat=True),
+            )
+
+        if pixel_values is not None:
+            if not isinstance(pixel_values, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values)}")
+
+            pixel_attention_mask = kwargs.pop("pixel_attention_mask")
+            if not isinstance(pixel_attention_mask, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel_attention_mask. "
+                                 f"Got type: {type(pixel_attention_mask)}")
+
+            num_patches = kwargs.pop("num_patches")
+            if not isinstance(num_patches, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of num_patches. "
+                                 f"Got type: {type(num_patches)}")
+
+            expected_h = expected_w = self.config.vision_config.image_size
+            return Idefics3ImagePixelInputs(
+                type="pixel_values",
+                pixel_values=flatten_bn(pixel_values, concat=True),
+                pixel_attention_mask=flatten_bn(pixel_attention_mask,
+                                                concat=True),
+                num_patches=flatten_bn(num_patches, concat=True),
+                resolve_bindings={
+                    "h": expected_h,
+                    "w": expected_w
+                },
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _process_image_pixels(
+            self, inputs: Idefics3ImagePixelInputs) -> torch.Tensor:
+        pixel_values = inputs["pixel_values"]
+        pixel_attention_mask = inputs["pixel_attention_mask"]
+
+        return self.model.image_pixels_to_features(
+            pixel_values,
+            pixel_attention_mask=pixel_attention_mask,
+        )
+
+    def _process_image_input(
+        self,
+        image_input: ImageInputs,
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
+        image_features = self._process_image_pixels(image_input)
+        image_features = self.model.connector(image_features)
+
+        num_patches = image_input["num_patches"]
+        return [
+            e.flatten(0, 1) for e in image_features.split(num_patches.tolist())
+        ]
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+
+        return self._process_image_input(image_input)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                self.config.image_token_id,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.model.text_model(input_ids,
+                                              positions,
+                                              intermediate_tensors,
+                                              inputs_embeds=inputs_embeds)
+
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="model.text_model",
+            connector="model.connector",
+            tower_model="model.vision_model")
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/interfaces.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/interfaces.py
new file mode 100644
index 0000000000000000000000000000000000000000..c425488f834b57f279149a370bc1622e65e23ad1
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/interfaces.py
@@ -0,0 +1,878 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable, Mapping, MutableSequence
+from typing import (TYPE_CHECKING, ClassVar, Literal, Optional, Protocol,
+                    Union, overload, runtime_checkable)
+
+import numpy as np
+import torch
+from torch import Tensor
+from transformers.models.whisper.tokenization_whisper import LANGUAGES
+from typing_extensions import Self, TypeIs
+
+from vllm.config import ModelConfig, SpeechToTextConfig
+from vllm.inputs import TokensPrompt
+from vllm.inputs.data import PromptType
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.utils import supports_kw
+
+from .interfaces_base import is_pooling_model
+
+if TYPE_CHECKING:
+    from vllm.attention import AttentionMetadata
+    from vllm.config import VllmConfig
+    from vllm.model_executor.models.utils import WeightsMapper
+    from vllm.sequence import IntermediateTensors
+
+logger = init_logger(__name__)
+
+MultiModalEmbeddings = Union[list[Tensor], Tensor, tuple[Tensor, ...]]
+"""
+The output embeddings must be one of the following formats:
+
+- A list or tuple of 2D tensors, where each tensor corresponds to
+    each input multimodal data item (e.g, image).
+- A single 3D tensor, with the batch dimension grouping the 2D tensors.
+"""
+
+
+@runtime_checkable
+class SupportsMultiModal(Protocol):
+    """The interface required for all multi-modal models."""
+
+    supports_multimodal: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports multi-modal inputs.
+
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        """
+        Get the placeholder text for the `i`th `modality` item in the prompt.
+        """
+        ...
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        """
+        Returns multimodal embeddings generated from multimodal kwargs 
+        to be merged with text embeddings.
+
+        Note:
+            The returned multimodal embeddings must be in the same order as
+            the appearances of their corresponding multimodal data item in the
+            input prompt.
+        """
+        ...
+
+    def get_language_model(self) -> torch.nn.Module:
+        """
+        Returns the underlying language model used for text generation.
+
+        This is typically the `torch.nn.Module` instance responsible for 
+        processing the merged multimodal embeddings and producing hidden states
+
+        Returns:
+            torch.nn.Module: The core language model component.
+        """
+        ...
+
+    # Only for models that support v0 chunked prefill
+    # TODO(ywang96): Remove this overload once v0 is deprecated
+    @overload
+    def get_input_embeddings(
+        self,
+        input_ids: Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+        attn_metadata: Optional["AttentionMetadata"] = None,
+    ) -> Tensor:
+        ...
+
+    # TODO: Remove this overload once v0 is deprecated
+    @overload
+    def get_input_embeddings(
+        self,
+        input_ids: Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> Tensor:
+        ...
+
+    def get_input_embeddings(
+        self,
+        input_ids: Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+        # Only necessary so that the v0 overload is valid
+        # TODO: Remove attn_metadata once v0 is deprecated
+        attn_metadata: Optional["AttentionMetadata"] = None,
+    ) -> Tensor:
+        """
+        Returns the input embeddings merged from the text embeddings from 
+        input_ids and the multimodal embeddings generated from multimodal 
+        kwargs.
+        """
+        ...
+
+
+@overload
+def supports_multimodal(
+        model: type[object]) -> TypeIs[type[SupportsMultiModal]]:
+    ...
+
+
+@overload
+def supports_multimodal(model: object) -> TypeIs[SupportsMultiModal]:
+    ...
+
+
+def supports_multimodal(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsMultiModal]], TypeIs[SupportsMultiModal]]:
+    return getattr(model, "supports_multimodal", False)
+
+
+@runtime_checkable
+class SupportsMultiModalWithRawInput(SupportsMultiModal, Protocol):
+    """The interface required for all multi-modal models."""
+
+    supports_multimodal_raw_input: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports multi-modal inputs and processes
+    them in their raw form and not embeddings.
+
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+
+
+@overload
+def supports_multimodal_raw_input(
+        model: object) -> TypeIs[SupportsMultiModalWithRawInput]:
+    ...
+
+
+@overload
+def supports_multimodal_raw_input(
+        model: type[object]) -> TypeIs[type[SupportsMultiModalWithRawInput]]:
+    ...
+
+
+def supports_multimodal_raw_input(
+    model: Union[type[object], object]
+) -> Union[TypeIs[type[SupportsMultiModalWithRawInput]],
+           TypeIs[SupportsMultiModalWithRawInput]]:
+    return getattr(model, "supports_multimodal_raw_input", False)
+
+
+@runtime_checkable
+class SupportsScoreTemplate(Protocol):
+    """The interface required for all models that support score template."""
+
+    supports_score_template: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports score template.
+
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+
+    @classmethod
+    def get_score_template(cls, query: str, document: str) -> Optional[str]:
+        """
+        Generate a full prompt by populating the score template with query and document content.
+        """ # noqa: E501
+        ...
+
+    @classmethod
+    def post_process_tokens(cls, prompt: TokensPrompt) -> None:
+        """
+        Perform architecture-specific manipulations on the input tokens.
+        """
+        ...
+
+
+@overload
+def supports_score_template(
+        model: type[object]) -> TypeIs[type[SupportsScoreTemplate]]:
+    ...
+
+
+@overload
+def supports_score_template(model: object) -> TypeIs[SupportsScoreTemplate]:
+    ...
+
+
+def supports_score_template(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsScoreTemplate]], TypeIs[SupportsScoreTemplate]]:
+    return getattr(model, "supports_score_template", False)
+
+
+@runtime_checkable
+class SupportsLoRA(Protocol):
+    """The interface required for all models that support LoRA."""
+
+    supports_lora: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports LoRA.
+
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+    # The `embedding_module` and `embedding_padding_modules`
+    # are empty by default.
+    embedding_modules: ClassVar[dict[str, str]] = {}
+    embedding_padding_modules: ClassVar[list[str]] = []
+    packed_modules_mapping: ClassVar[dict[str, list[str]]] = {}
+
+
+# We can't use runtime_checkable with ClassVar for issubclass checks
+# so we need to treat the class as an instance and use isinstance instead
+@runtime_checkable
+class _SupportsLoRAType(Protocol):
+    supports_lora: Literal[True]
+
+    packed_modules_mapping: dict[str, list[str]]
+    embedding_modules: dict[str, str]
+    embedding_padding_modules: list[str]
+
+
+@overload
+def supports_lora(model: type[object]) -> TypeIs[type[SupportsLoRA]]:
+    ...
+
+
+@overload
+def supports_lora(model: object) -> TypeIs[SupportsLoRA]:
+    ...
+
+
+def supports_lora(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsLoRA]], TypeIs[SupportsLoRA]]:
+    result = _supports_lora(model)
+
+    if not result:
+        lora_attrs = (
+            "packed_modules_mapping",
+            "embedding_modules",
+            "embedding_padding_modules",
+        )
+        missing_attrs = tuple(attr for attr in lora_attrs
+                              if not hasattr(model, attr))
+
+        if getattr(model, "supports_lora", False):
+            if missing_attrs:
+                logger.warning(
+                    "The model (%s) sets `supports_lora=True`, "
+                    "but is missing LoRA-specific attributes: %s",
+                    model,
+                    missing_attrs,
+                )
+        else:
+            if not missing_attrs:
+                logger.warning(
+                    "The model (%s) contains all LoRA-specific attributes, "
+                    "but does not set `supports_lora=True`.", model)
+
+    return result
+
+
+def _supports_lora(model: Union[type[object], object]) -> bool:
+    if isinstance(model, type):
+        return isinstance(model, _SupportsLoRAType)
+
+    return isinstance(model, SupportsLoRA)
+
+
+@runtime_checkable
+class SupportsPP(Protocol):
+    """The interface required for all models that support pipeline parallel."""
+
+    supports_pp: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports pipeline parallel.
+
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+
+    def make_empty_intermediate_tensors(
+        self,
+        batch_size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> "IntermediateTensors":
+        """Called when PP rank > 0 for profiling purposes."""
+        ...
+
+    def forward(
+        self,
+        *,
+        intermediate_tensors: Optional["IntermediateTensors"],
+    ) -> Union[Tensor, "IntermediateTensors"]:
+        """
+        Accept [`IntermediateTensors`][vllm.sequence.IntermediateTensors] when
+        PP rank > 0.
+
+        Return [`IntermediateTensors`][vllm.sequence.IntermediateTensors] only
+        for the last PP rank.
+        """
+        ...
+
+
+# We can't use runtime_checkable with ClassVar for issubclass checks
+# so we need to treat the class as an instance and use isinstance instead
+@runtime_checkable
+class _SupportsPPType(Protocol):
+    supports_pp: Literal[True]
+
+    def make_empty_intermediate_tensors(
+        self,
+        batch_size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> "IntermediateTensors":
+        ...
+
+    def forward(
+        self,
+        *,
+        intermediate_tensors: Optional["IntermediateTensors"],
+    ) -> Union[Tensor, "IntermediateTensors"]:
+        ...
+
+
+@overload
+def supports_pp(model: type[object]) -> TypeIs[type[SupportsPP]]:
+    ...
+
+
+@overload
+def supports_pp(model: object) -> TypeIs[SupportsPP]:
+    ...
+
+
+def supports_pp(
+    model: Union[type[object], object],
+) -> Union[bool, TypeIs[type[SupportsPP]], TypeIs[SupportsPP]]:
+    supports_attributes = _supports_pp_attributes(model)
+    supports_inspect = _supports_pp_inspect(model)
+
+    if supports_attributes and not supports_inspect:
+        logger.warning(
+            "The model (%s) sets `supports_pp=True`, but does not accept "
+            "`intermediate_tensors` in its `forward` method", model)
+
+    if not supports_attributes:
+        pp_attrs = ("make_empty_intermediate_tensors", )
+        missing_attrs = tuple(attr for attr in pp_attrs
+                              if not hasattr(model, attr))
+
+        if getattr(model, "supports_pp", False):
+            if missing_attrs:
+                logger.warning(
+                    "The model (%s) sets `supports_pp=True`, "
+                    "but is missing PP-specific attributes: %s",
+                    model,
+                    missing_attrs,
+                )
+        else:
+            if not missing_attrs:
+                logger.warning(
+                    "The model (%s) contains all PP-specific attributes, "
+                    "but does not set `supports_pp=True`.", model)
+
+    return supports_attributes and supports_inspect
+
+
+def _supports_pp_attributes(model: Union[type[object], object]) -> bool:
+    if isinstance(model, type):
+        return isinstance(model, _SupportsPPType)
+
+    return isinstance(model, SupportsPP)
+
+
+def _supports_pp_inspect(model: Union[type[object], object]) -> bool:
+    model_forward = getattr(model, "forward", None)
+    if not callable(model_forward):
+        return False
+
+    return supports_kw(model_forward, "intermediate_tensors")
+
+
+@runtime_checkable
+class HasInnerState(Protocol):
+    """The interface required for all models that has inner state."""
+
+    has_inner_state: ClassVar[Literal[True]] = True
+    """
+        A flag that indicates this model has inner state.
+        Models that has inner state usually need access to the scheduler_config
+        for max_num_seqs, etc. True for e.g. both Mamba and Jamba.
+    """
+
+
+@overload
+def has_inner_state(model: object) -> TypeIs[HasInnerState]:
+    ...
+
+
+@overload
+def has_inner_state(model: type[object]) -> TypeIs[type[HasInnerState]]:
+    ...
+
+
+def has_inner_state(
+    model: Union[type[object], object]
+) -> Union[TypeIs[type[HasInnerState]], TypeIs[HasInnerState]]:
+    return getattr(model, "has_inner_state", False)
+
+
+@runtime_checkable
+class IsAttentionFree(Protocol):
+    """The interface required for all models like Mamba that lack attention,
+    but do have state whose size is constant wrt the number of tokens."""
+
+    is_attention_free: ClassVar[Literal[True]] = True
+    """
+        A flag that indicates this model has no attention.
+        Used for block manager and attention backend selection.
+        True for Mamba but not Jamba.
+    """
+
+
+@overload
+def is_attention_free(model: object) -> TypeIs[IsAttentionFree]:
+    ...
+
+
+@overload
+def is_attention_free(model: type[object]) -> TypeIs[type[IsAttentionFree]]:
+    ...
+
+
+def is_attention_free(
+    model: Union[type[object], object]
+) -> Union[TypeIs[type[IsAttentionFree]], TypeIs[IsAttentionFree]]:
+    return getattr(model, "is_attention_free", False)
+
+
+@runtime_checkable
+class IsHybrid(Protocol):
+    """The interface required for all models like Jamba that have both
+    attention and mamba blocks, indicates that 
+    hf_config has 'layers_block_type'"""
+
+    is_hybrid: ClassVar[Literal[True]] = True
+    """
+        A flag that indicates this model has both mamba and attention blocks
+        , also indicates that the model's hf_config has 
+        'layers_block_type' """
+
+    @classmethod
+    def get_mamba_state_shape_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+        use_v1: bool = True,
+    ) -> tuple[tuple[int, int], tuple[int, int, int]]:
+        """Calculate shapes for Mamba's convolutional and state caches.
+
+        Args:
+            vllm_config: vLLM config
+            use_v1: Get shapes for V1 (or V0)
+
+        Returns:
+            Tuple containing:
+            - conv_state_shape: Shape for convolutional state cache
+            - temporal_state_shape: Shape for state space model cache
+        """
+        ...
+
+
+@overload
+def is_hybrid(model: object) -> TypeIs[IsHybrid]:
+    ...
+
+
+@overload
+def is_hybrid(model: type[object]) -> TypeIs[type[IsHybrid]]:
+    ...
+
+
+def is_hybrid(
+    model: Union[type[object], object]
+) -> Union[TypeIs[type[IsHybrid]], TypeIs[IsHybrid]]:
+    return getattr(model, "is_hybrid", False)
+
+
+@runtime_checkable
+class MixtureOfExperts(Protocol):
+    """
+    Check if the model is a mixture of experts (MoE) model.
+    """
+
+    expert_weights: MutableSequence[Iterable[Tensor]]
+    """
+    Expert weights saved in this rank.
+
+    The first dimension is the layer, and the second dimension is different
+    parameters in the layer, e.g. up/down projection weights.
+    """
+
+    num_moe_layers: int
+    """Number of MoE layers in this model."""
+
+    num_expert_groups: int
+    """Number of expert groups in this model."""
+
+    num_logical_experts: int
+    """Number of logical experts in this model."""
+
+    num_physical_experts: int
+    """Number of physical experts in this model."""
+
+    num_local_physical_experts: int
+    """Number of local physical experts in this model."""
+
+    num_routed_experts: int
+    """Number of routed experts in this model."""
+
+    num_shared_experts: int
+    """Number of shared experts in this model."""
+
+    num_redundant_experts: int
+    """Number of redundant experts in this model."""
+
+    def set_eplb_state(
+        self,
+        expert_load_view: Tensor,
+        logical_to_physical_map: Tensor,
+        logical_replica_count: Tensor,
+    ) -> None:
+        """
+        Register the EPLB state in the MoE model.
+        
+        Since these are views of the actual EPLB state, any changes made by
+        the EPLB algorithm are automatically reflected in the model's behavior
+        without requiring additional method calls to set new states.
+
+        You should also collect model's `expert_weights` here instead of in
+        the weight loader, since after initial weight loading, further
+        processing like quantization may be applied to the weights.
+
+        Args:
+            expert_load_view: A view of the expert load metrics tensor.
+            logical_to_physical_map: Mapping from logical to physical experts.
+            logical_replica_count: Count of replicas for each logical expert.
+        """
+        ...
+
+    def update_physical_experts_metadata(
+        self,
+        num_physical_experts: int,
+        num_local_physical_experts: int,
+    ) -> None:
+        ...
+
+
+def is_mixture_of_experts(model: object) -> TypeIs[MixtureOfExperts]:
+    return isinstance(model, MixtureOfExperts)
+
+
+@runtime_checkable
+class HasNoOps(Protocol):
+    has_noops: ClassVar[Literal[True]] = True
+
+
+@overload
+def has_noops(model: object) -> TypeIs[HasNoOps]:
+    ...
+
+
+@overload
+def has_noops(model: type[object]) -> TypeIs[type[HasNoOps]]:
+    ...
+
+
+def has_noops(
+    model: Union[type[object], object]
+) -> Union[TypeIs[type[HasNoOps]], TypeIs[HasNoOps]]:
+    return getattr(model, "has_noops", False)
+
+
+@runtime_checkable
+class SupportsCrossEncoding(Protocol):
+    """The interface required for all models that support cross encoding."""
+
+    supports_cross_encoding: ClassVar[Literal[True]] = True
+
+
+@overload
+def supports_cross_encoding(
+        model: type[object]) -> TypeIs[type[SupportsCrossEncoding]]:
+    ...
+
+
+@overload
+def supports_cross_encoding(model: object) -> TypeIs[SupportsCrossEncoding]:
+    ...
+
+
+def _supports_cross_encoding(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsCrossEncoding]], TypeIs[SupportsCrossEncoding]]:
+    return getattr(model, "supports_cross_encoding", False)
+
+
+def supports_cross_encoding(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsCrossEncoding]], TypeIs[SupportsCrossEncoding]]:
+    return is_pooling_model(model) and _supports_cross_encoding(model)
+
+
+def default_pooling_type(pooling_type: str) -> object:
+    """Set default_pooling_type decorator. """
+
+    def func(model: object):
+        model.default_pooling_type = pooling_type
+        return model
+
+    return func
+
+
+def get_default_pooling_type(model: Union[type[object], object]) -> str:
+    return getattr(model, "default_pooling_type", "LAST")
+
+
+class SupportsQuant:
+    """The interface required for all models that support quantization."""
+
+    hf_to_vllm_mapper: ClassVar[Optional["WeightsMapper"]] = None
+    packed_modules_mapping: ClassVar[Optional[dict[str, list[str]]]] = None
+    quant_config: Optional[QuantizationConfig] = None
+
+    def __new__(cls, *args, **kwargs) -> Self:
+        instance = super().__new__(cls)
+
+        # find config passed in arguments
+        quant_config = cls._find_quant_config(*args, **kwargs)
+        if quant_config is not None:
+
+            # attach config to model for general use
+            instance.quant_config = quant_config
+
+            # apply model mappings to config for proper config-model matching
+            if (hf_to_vllm_mapper := instance.hf_to_vllm_mapper) is not None:
+                instance.quant_config.apply_vllm_mapper(hf_to_vllm_mapper)
+            if instance.packed_modules_mapping is not None:
+                instance.quant_config.packed_modules_mapping.update(
+                    instance.packed_modules_mapping)
+
+        return instance
+
+    @staticmethod
+    def _find_quant_config(*args, **kwargs) -> Optional[QuantizationConfig]:
+        """Find quant config passed through model constructor args"""
+        from vllm.config import VllmConfig  # avoid circular import
+
+        args_values = list(args) + list(kwargs.values())
+        for arg in args_values:
+            if isinstance(arg, VllmConfig):
+                return arg.quant_config
+
+            if isinstance(arg, QuantizationConfig):
+                return arg
+
+        return None
+
+
+@runtime_checkable
+class SupportsTranscription(Protocol):
+    """The interface required for all models that support transcription."""
+    # Mapping from ISO639_1 language codes: language names
+    supported_languages: ClassVar[Mapping[str, str]]
+
+    supports_transcription: ClassVar[Literal[True]] = True
+
+    supports_transcription_only: ClassVar[bool] = False
+    """
+    Transcription models can opt out of text generation by setting this to
+    `True`.
+    """
+
+    def __init_subclass__(cls, **kwargs):
+        super().__init_subclass__(**kwargs)
+        # language codes in supported_languages
+        # that don't exist in the full language map
+        invalid = set(cls.supported_languages) - set(LANGUAGES.keys())
+        if invalid:
+            raise ValueError(
+                f"{cls.__name__}.supported_languages contains invalid "
+                f"language codes: {sorted(invalid)}\n. "
+                f"Valid choices are: {sorted(LANGUAGES.keys())}")
+
+    @classmethod
+    def get_generation_prompt(cls, audio: np.ndarray,
+                              stt_config: SpeechToTextConfig,
+                              model_config: ModelConfig,
+                              language: Optional[str], task_type: str,
+                              request_prompt: str) -> PromptType:
+        """Get the prompt for the ASR model.
+        The model has control over the construction, as long as it
+        returns a valid PromptType."""
+        ...
+
+    @classmethod
+    def get_other_languages(cls) -> Mapping[str, str]:
+        # other possible language codes from the whisper map
+        return {
+            k: v
+            for k, v in LANGUAGES.items() if k not in cls.supported_languages
+        }
+
+    @classmethod
+    def validate_language(cls, language: Optional[str]) -> Optional[str]:
+        """
+        Ensure the language specified in the transcription request 
+        is a valid ISO 639-1 language code. If the request language is 
+        valid, but not natively supported by the model, trigger a 
+        warning (but not an exception).
+        """
+        if language is None or language in cls.supported_languages:
+            return language
+        elif language in cls.get_other_languages():
+            logger.warning(
+                "Language %r is not natively supported by %s; "
+                "results may be less accurate. Supported languages: %r",
+                language,
+                cls.__name__,
+                list(cls.supported_languages.keys()),
+            )
+            return language
+        else:
+            raise ValueError(
+                f"Unsupported language: {language!r}.  Must be one of "
+                f"{list(cls.supported_languages.keys())}.")
+
+    @classmethod
+    def get_speech_to_text_config(
+            cls, model_config: ModelConfig,
+            task_type: Literal["transcribe",
+                               "translate"]) -> SpeechToTextConfig:
+        """Get the speech to text config for the ASR model."""
+        ...
+
+    @classmethod
+    def get_num_audio_tokens(cls, audio_duration_s: float,
+                             stt_config: SpeechToTextConfig,
+                             model_config: ModelConfig) -> Optional[int]:
+        """
+        Map from audio duration to number of audio tokens produced by the ASR 
+        model, without running a forward pass.
+        This is used for estimating the amount of processing for this audio.
+        """
+        return None
+
+
+@overload
+def supports_transcription(
+        model: type[object]) -> TypeIs[type[SupportsTranscription]]:
+    ...
+
+
+@overload
+def supports_transcription(model: object) -> TypeIs[SupportsTranscription]:
+    ...
+
+
+def supports_transcription(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsTranscription]], TypeIs[SupportsTranscription]]:
+    return getattr(model, "supports_transcription", False)
+
+
+@runtime_checkable
+class SupportsV0Only(Protocol):
+    """Models with this interface are not compatible with V1 vLLM."""
+
+    supports_v0_only: ClassVar[Literal[True]] = True
+
+
+@overload
+def supports_v0_only(model: type[object]) -> TypeIs[type[SupportsV0Only]]:
+    ...
+
+
+@overload
+def supports_v0_only(model: object) -> TypeIs[SupportsV0Only]:
+    ...
+
+
+def supports_v0_only(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsV0Only]], TypeIs[SupportsV0Only]]:
+    return getattr(model, "supports_v0_only", False)
+
+
+@runtime_checkable
+class SupportsEagle3(Protocol):
+    """The interface required for models that support 
+    EAGLE3 speculative decoding."""
+
+    supports_eagle3: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports EAGLE3 
+    speculative decoding.
+
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+
+    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
+        """
+        Set which layers should output auxiliary
+        hidden states for EAGLE3.
+        
+        Args:
+            layers: Tuple of layer indices that should output auxiliary
+              hidden states.
+        """
+        ...
+
+    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
+        """
+        Get the layer indices that should output auxiliary hidden states
+        for EAGLE3.
+        
+        Returns:
+            Tuple of layer indices for auxiliary hidden state outputs.
+        """
+        ...
+
+
+@overload
+def supports_eagle3(model: type[object]) -> TypeIs[type[SupportsEagle3]]:
+    ...
+
+
+@overload
+def supports_eagle3(model: object) -> TypeIs[SupportsEagle3]:
+    ...
+
+
+def supports_eagle3(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsEagle3]], TypeIs[SupportsEagle3]]:
+    return isinstance(model, SupportsEagle3)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/interfaces_base.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/interfaces_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..697fa020deb46d484d0bf6fd7dc4b7fa422c5a6a
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/interfaces_base.py
@@ -0,0 +1,167 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import (TYPE_CHECKING, Any, ClassVar, Literal, Optional, Protocol,
+                    Union, overload, runtime_checkable)
+
+import torch
+import torch.nn as nn
+from typing_extensions import TypeIs, TypeVar
+
+from vllm.logger import init_logger
+from vllm.utils import supports_kw
+
+if TYPE_CHECKING:
+    from vllm.config import VllmConfig
+    from vllm.model_executor.layers.pooler import Pooler
+    from vllm.model_executor.sampling_metadata import SamplingMetadata
+else:
+    VllmConfig = Any
+    Pooler = Any
+    SamplingMetadata = Any
+
+logger = init_logger(__name__)
+
+# The type of hidden states
+# Currently, T = torch.Tensor for all models except for Medusa
+# which has T = list[torch.Tensor]
+T = TypeVar("T", default=torch.Tensor)
+T_co = TypeVar("T_co", default=torch.Tensor, covariant=True)
+
+# NOTE: Unlike those in `interfaces.py`, we don't define `ClassVar` tags
+# for the base interfaces to avoid breaking OOT registration for existing models
+# that don't inherit from the base interface classes
+
+
+@runtime_checkable
+class VllmModel(Protocol[T_co]):
+    """The interface required for all models in vLLM."""
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> None:
+        ...
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+    ) -> T_co:
+        ...
+
+
+def _check_vllm_model_init(model: Union[type[object], object]) -> bool:
+    model_init = model.__init__
+    return supports_kw(model_init, "vllm_config")
+
+
+def _check_vllm_model_forward(model: Union[type[object], object]) -> bool:
+    model_forward = getattr(model, "forward", None)
+    if not callable(model_forward):
+        return False
+
+    vllm_kws = ("input_ids", "positions")
+    missing_kws = tuple(kw for kw in vllm_kws
+                        if not supports_kw(model_forward, kw))
+
+    if missing_kws and (isinstance(model, type)
+                        and issubclass(model, nn.Module)):
+        logger.warning(
+            "The model (%s) is missing "
+            "vLLM-specific keywords from its `forward` method: %s",
+            model,
+            missing_kws,
+        )
+
+    return len(missing_kws) == 0
+
+
+@overload
+def is_vllm_model(model: type[object]) -> TypeIs[type[VllmModel]]:
+    ...
+
+
+@overload
+def is_vllm_model(model: object) -> TypeIs[VllmModel]:
+    ...
+
+
+def is_vllm_model(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[VllmModel]], TypeIs[VllmModel]]:
+    return _check_vllm_model_init(model) and _check_vllm_model_forward(model)
+
+
+@runtime_checkable
+class VllmModelForTextGeneration(VllmModel[T], Protocol[T]):
+    """The interface required for all generative models in vLLM."""
+
+    def compute_logits(
+        self,
+        hidden_states: T,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[T]:
+        """Return `None` if TP rank > 0."""
+        ...
+
+
+@overload
+def is_text_generation_model(
+        model: type[object]) -> TypeIs[type[VllmModelForTextGeneration]]:
+    ...
+
+
+@overload
+def is_text_generation_model(
+        model: object) -> TypeIs[VllmModelForTextGeneration]:
+    ...
+
+
+def is_text_generation_model(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[VllmModelForTextGeneration]],
+           TypeIs[VllmModelForTextGeneration]]:
+    if not is_vllm_model(model):
+        return False
+
+    if isinstance(model, type):
+        return isinstance(model, VllmModelForTextGeneration)
+
+    return isinstance(model, VllmModelForTextGeneration)
+
+
+@runtime_checkable
+class VllmModelForPooling(VllmModel[T_co], Protocol[T_co]):
+    """The interface required for all pooling models in vLLM."""
+
+    is_pooling_model: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports pooling.
+
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+
+    pooler: Pooler
+    """The pooler is only called on TP rank 0."""
+
+
+@overload
+def is_pooling_model(model: type[object]) -> TypeIs[type[VllmModelForPooling]]:
+    ...
+
+
+@overload
+def is_pooling_model(model: object) -> TypeIs[VllmModelForPooling]:
+    ...
+
+
+def is_pooling_model(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[VllmModelForPooling]], TypeIs[VllmModelForPooling]]:
+    if not is_vllm_model(model):
+        return False
+
+    return getattr(model, "is_pooling_model", False)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/interns1_vit.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/interns1_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..300ed17ecaabccd03e6f058f95e3e7ba85d0911a
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/interns1_vit.py
@@ -0,0 +1,421 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://huggingface.co/OpenGVLab/InternVL2-4B/blob/main/modeling_intern_vit.py
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PretrainedConfig
+from transformers.utils import torch_int
+
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+NORM2FN = {
+    'rms_norm': RMSNorm,
+    'layer_norm': nn.LayerNorm,
+}
+
+
+class InternS1VisionPatchEmbeddings(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        image_size, patch_size = config.image_size, config.patch_size
+        num_channels, hidden_size = config.num_channels, config.hidden_size
+
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] //
+                                                          patch_size[0])
+        patch_shape = (image_size[0] // patch_size[0],
+                       image_size[1] // patch_size[1])
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.num_patches = num_patches
+        self.patch_shape = patch_shape
+
+        self.projection = nn.Conv2d(num_channels,
+                                    hidden_size,
+                                    kernel_size=patch_size,
+                                    stride=patch_size)
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        batch_size, num_channels, height, width = pixel_values.shape
+        if num_channels != self.num_channels:
+            raise ValueError(
+                "Make sure that the channel dimension of the pixel values "
+                "match with the one set in the configuration.")
+
+        embeddings = self.projection(
+            pixel_values.to(self.projection.weight.dtype))
+        patch_height, patch_width = embeddings.shape[2], embeddings.shape[3]
+        embeddings = embeddings.flatten(2).transpose(1, 2)
+
+        return embeddings, (patch_height, patch_width)
+
+
+class InternS1VisionEmbeddings(nn.Module):
+
+    def __init__(self, config: PretrainedConfig):
+        super().__init__()
+        self.config = config
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+        if config.use_mask_token:
+            self.mask_token = nn.Parameter(
+                torch.zeros(1, 1, config.hidden_size))
+        else:
+            self.mask_token = None
+        self.patch_embeddings = InternS1VisionPatchEmbeddings(config)
+        self.patch_size = config.patch_size
+        self.image_size = (config.image_size if isinstance(
+            config.image_size, Iterable) else
+                           (config.image_size, config.image_size))
+        num_patches = self.patch_embeddings.num_patches
+        if config.use_absolute_position_embeddings:
+            self.position_embeddings = nn.Parameter(
+                torch.zeros(1, num_patches + 1, config.hidden_size))
+        else:
+            self.position_embeddings = None
+
+    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int,
+                                 width: int) -> torch.Tensor:
+        """
+        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
+        images. This method is also adapted to support torch.jit tracing.
+
+        Adapted from:
+        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
+        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
+        """  # noqa: E501
+
+        num_patches = embeddings.shape[1] - 1
+        num_positions = self.position_embeddings.shape[1] - 1
+
+        # always interpolate when tracing to ensure the exported model
+        # works for dynamic input shapes
+        if not torch.jit.is_tracing(
+        ) and num_patches == num_positions and height == width:
+            return self.position_embeddings
+
+        class_pos_embed = self.position_embeddings[:, :1]
+        patch_pos_embed = self.position_embeddings[:, 1:]
+
+        dim = embeddings.shape[-1]
+
+        new_height = height // self.patch_size[0]
+        new_width = width // self.patch_size[1]
+
+        sqrt_num_positions = torch_int(num_positions**0.5)
+        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions,
+                                                  sqrt_num_positions, dim)
+        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed,
+            size=(new_height, new_width),
+            mode="bicubic",
+            align_corners=False,
+        )
+
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+
+        return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        bool_masked_pos: Optional[torch.BoolTensor] = None,
+    ) -> torch.Tensor:
+        _, _, height, width = pixel_values.shape
+        embeddings, (patch_height,
+                     patch_width) = self.patch_embeddings(pixel_values)
+        batch_size, seq_len, _ = embeddings.size()
+
+        if bool_masked_pos is not None:
+            mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
+            # replace the masked visual tokens by mask_tokens
+            w = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
+            embeddings = embeddings * (1 - w) + mask_tokens * w
+
+        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+        embeddings = torch.cat((cls_tokens, embeddings), dim=1)
+
+        if self.position_embeddings is not None:
+            embeddings = embeddings + self.interpolate_pos_encoding(
+                embeddings, height, width)
+
+        return embeddings, (patch_height, patch_width)
+
+
+class InternSdpaAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        *,
+        num_dummy_heads: int = 0,
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f'embed_dim must be divisible by num_heads '
+                f'(got `embed_dim`: {self.embed_dim} and `num_heads`:'
+                f' {self.num_heads}).')
+
+        # Additional dummy heads are used to enable TP for common GPU counts.
+        self.dummy_dim = (num_dummy_heads + self.num_heads) * self.head_dim
+
+        self.scale = self.head_dim**-0.5
+
+        self.q_proj = nn.Linear(self.embed_dim,
+                                self.num_heads * self.head_dim,
+                                bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.embed_dim,
+                                self.num_heads * self.head_dim,
+                                bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.embed_dim,
+                                self.num_heads * self.head_dim,
+                                bias=config.attention_bias)
+
+        self.qk_normalization = config.use_qk_norm
+        if self.qk_normalization:
+            self.q_norm = RMSNorm(self.dummy_dim,
+                                  eps=config.layer_norm_eps,
+                                  var_hidden_size=self.embed_dim)
+            self.k_norm = RMSNorm(self.dummy_dim,
+                                  eps=config.layer_norm_eps,
+                                  var_hidden_size=self.embed_dim)
+
+        self.projection_layer = nn.Linear(self.dummy_dim, self.embed_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, N, C = x.shape
+
+        q = self.q_proj(x)
+        k = self.k_proj(x)
+        v = self.v_proj(x)
+
+        q = q.view(B, N, self.num_heads, self.head_dim)
+        k = k.view(B, N, self.num_heads, self.head_dim)
+        v = v.view(B, N, self.num_heads, self.head_dim)
+
+        if self.qk_normalization:
+            B_, N_, H_, D_ = q.shape
+            q = self.q_norm(q.flatten(-2, -1)).view(B_, N_, H_, D_)
+            k = self.k_norm(k.flatten(-2, -1)).view(B_, N_, H_, D_)
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+
+        x = F.scaled_dot_product_attention(q, k, v, scale=self.scale)
+        x = x.transpose(1, 2).reshape(B, N, -1)
+
+        x = self.projection_layer(x)
+        return x
+
+
+class InternS1VisionMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.activation_fn = get_act_fn(config.hidden_act)
+        self.fc1 = ColumnParallelLinear(config.hidden_size,
+                                        config.intermediate_size,
+                                        bias=True,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.fc1")
+        self.fc2 = RowParallelLinear(config.intermediate_size,
+                                     config.hidden_size,
+                                     bias=True,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.fc2")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+
+        return hidden_states
+
+
+class InternS1VisionLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_dummy_heads: int = 0,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.attention = self._init_attn(config,
+                                         quant_config,
+                                         num_dummy_heads=num_dummy_heads,
+                                         prefix=f"{prefix}.attention")
+
+        self.mlp = InternS1VisionMLP(config,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.mlp")
+        self.layernorm_before = NORM2FN[config.norm_type](
+            config.hidden_size, eps=config.layer_norm_eps)
+        self.layernorm_after = NORM2FN[config.norm_type](
+            config.hidden_size, eps=config.layer_norm_eps)
+
+        init_values = config.layer_scale_init_value
+        self.lambda_1 = nn.Parameter(init_values *
+                                     torch.ones(config.hidden_size),
+                                     requires_grad=True)
+        self.lambda_2 = nn.Parameter(init_values *
+                                     torch.ones(config.hidden_size),
+                                     requires_grad=True)
+
+    def _init_attn(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig],
+        *,
+        num_dummy_heads: int,
+        prefix: str = "",
+    ):
+        return InternSdpaAttention(config, num_dummy_heads=num_dummy_heads)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ):
+        hidden_states = hidden_states + self.attention(
+            self.layernorm_before(hidden_states)) * self.lambda_1
+
+        hidden_states = hidden_states + self.mlp(
+            self.layernorm_after(hidden_states)) * self.lambda_2
+
+        return hidden_states
+
+
+class InternS1VisionEncoder(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        num_dummy_heads: int = 0,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.config = config
+
+        if num_hidden_layers_override is None:
+            num_hidden_layers = config.num_hidden_layers
+        else:
+            num_hidden_layers = num_hidden_layers_override
+
+        self.layer = nn.ModuleList([
+            InternS1VisionLayer(config,
+                                quant_config,
+                                num_dummy_heads=num_dummy_heads,
+                                prefix=f"{prefix}.layer.{layer_idx}")
+            for layer_idx in range(num_hidden_layers)
+        ])
+
+    def forward(self, inputs_embeds: torch.Tensor):
+
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layer:
+            hidden_states = encoder_layer(hidden_states)
+
+        return hidden_states
+
+
+class InternS1VisionModel(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        num_dummy_heads: int = 0,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+
+        self.embeddings = InternS1VisionEmbeddings(config)
+        self.encoder = InternS1VisionEncoder(
+            config=config,
+            num_hidden_layers_override=num_hidden_layers_override,
+            num_dummy_heads=num_dummy_heads,
+            prefix=f"{prefix}.encoder",
+        )
+        self.layernorm = (nn.Identity() if config.use_mean_pooling else
+                          nn.LayerNorm(config.hidden_size,
+                                       eps=config.layer_norm_eps))
+
+    def get_input_embeddings(self):
+        return self.embeddings.patch_embeddings
+
+    def forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        pixel_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        if pixel_values is None and pixel_embeds is None:
+            raise ValueError(
+                'You have to specify pixel_values or pixel_embeds')
+
+        if pixel_embeds is not None:
+            hidden_states = pixel_embeds
+        elif pixel_values is not None:
+            if pixel_values.ndim == 4:
+                hidden_states, _ = self.embeddings(pixel_values)
+            else:
+                raise ValueError(
+                    f'wrong pixel_values size: {pixel_values.shape}')
+
+        encoder_outputs = self.encoder(inputs_embeds=hidden_states)
+        encoder_outputs = self.layernorm(encoder_outputs)
+
+        return encoder_outputs
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/internvl.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/internvl.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e766dd4c476892e5c3c00ec8114e60a281a52b6
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/internvl.py
@@ -0,0 +1,1396 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://huggingface.co/OpenGVLab/InternVL2-4B/blob/main/modeling_internvl_chat.py
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from abc import ABC, abstractmethod
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Any, Literal, Optional, TypeVar, Union
+
+import numpy.typing as npt
+import torch
+import torch.nn as nn
+import torchvision.transforms as T
+from PIL import Image
+from transformers import BatchEncoding, PretrainedConfig, TensorType
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.awq import AWQConfig
+from vllm.model_executor.models.intern_vit import (InternVisionModel,
+                                                   InternVisionPatchModel)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.image import convert_image_mode
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs, NestedTensors)
+from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
+                                   ImageSize, MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
+                    maybe_prefix, merge_multimodal_embeddings)
+
+IMG_START = '<img>'
+IMG_END = '</img>'
+IMG_CONTEXT = '<IMG_CONTEXT>'
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+class InternVLImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - bn: Batch size * number of images
+        - bnp: Batch size * number of images * (1 + num_patches)
+        - c: Number of channels (3)
+        - h: Height of each image patch
+        - w: Width of each image patch
+    """
+    type: Literal["pixel_values"]
+    pixel_values_flat: Annotated[torch.Tensor, TensorShape("bnp", 3, "h", "w")]
+    num_patches: Annotated[torch.Tensor, TensorShape("bn")]
+
+
+class InternVLImageEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - n: Number of images
+        - f: Total image feature size
+        - h: Hidden size (must match the hidden size of language model backbone)
+    """
+    type: Literal["image_embeds"]
+    data: Annotated[Union[torch.Tensor, list[torch.Tensor]],
+                    TensorShape("n", "f", "h")]
+
+
+InternVLImageInputs = Union[InternVLImagePixelInputs,
+                            InternVLImageEmbeddingInputs]
+
+
+class InternVLVideoPixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - bvf: Batch size * number of videos * num_frames
+        - bn: Batch size * number of images
+        - c: Number of channels (3)
+        - h: Height of each video frame
+        - w: Width of each video frame
+    """
+    type: Literal["pixel_values_videos"]
+    pixel_values_flat: Annotated[torch.Tensor, TensorShape("bvf", 3, "h", "w")]
+    num_patches: Annotated[torch.Tensor, TensorShape("bn")]
+
+
+class InternVLVideoEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - n: Number of videos
+        - f: Total video feature size
+        - h: Hidden size (must match the hidden size of language model backbone)
+    """
+    type: Literal["video_embeds"]
+    data: Annotated[Union[torch.Tensor, list[torch.Tensor]],
+                    TensorShape("n", "f", "h")]
+
+
+InternVLVideoInputs = Union[InternVLVideoPixelInputs,
+                            InternVLVideoEmbeddingInputs]
+
+
+# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
+def build_transform(input_size: int):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    return T.Compose([
+        T.Lambda(lambda img: convert_image_mode(img, 'RGB')),
+        T.Resize((input_size, input_size),
+                 interpolation=T.InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+
+
+# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
+def find_closest_aspect_ratio(
+    aspect_ratio: float,
+    target_ratios: list[tuple[int, int]],
+    *,
+    width: int,
+    height: int,
+    image_size: int,
+) -> tuple[int, int]:
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+
+def resolve_internvl_min_max_num(
+    *,
+    min_dynamic_patch: int,
+    max_dynamic_patch: int,
+    dynamic_image_size: bool,
+    use_thumbnail: bool,
+) -> tuple[int, int]:
+    min_dynamic_patch = min_dynamic_patch if dynamic_image_size else 1
+    max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1
+
+    if use_thumbnail and max_dynamic_patch != 1:
+        max_dynamic_patch += 1
+
+    return min_dynamic_patch, max_dynamic_patch
+
+
+def get_internvl_target_ratios(
+    min_num: int,
+    max_num: int,
+) -> list[tuple[int, int]]:
+    target_ratios = {(i, j)
+                     for n in range(min_num, max_num + 1)
+                     for i in range(1, n + 1)
+                     for j in range(1, n + 1) if min_num <= i * j <= max_num}
+    return sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+
+def calculate_internvl_targets(
+    *,
+    orig_width: int,
+    orig_height: int,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> tuple[int, int, int]:
+    aspect_ratio = orig_width / orig_height
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio,
+        target_ratios,
+        width=orig_width,
+        height=orig_height,
+        image_size=image_size,
+    )
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # add thumbnail image if num_blocks != 1
+    if use_thumbnail and blocks != 1:
+        blocks += 1
+
+    return blocks, target_width, target_height
+
+
+# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
+def dynamic_preprocess_internvl(
+    image: Image.Image,
+    *,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> list[Image.Image]:
+    orig_width, orig_height = image.size
+
+    # calculate the number of blocks without thumbnail
+    blocks, target_width, target_height = calculate_internvl_targets(
+        orig_width=orig_width,
+        orig_height=orig_height,
+        target_ratios=target_ratios,
+        image_size=image_size,
+        use_thumbnail=False,
+    )
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = ((i % (target_width // image_size)) * image_size,
+               (i // (target_width // image_size)) * image_size,
+               ((i % (target_width // image_size)) + 1) * image_size,
+               ((i // (target_width // image_size)) + 1) * image_size)
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+
+    assert len(processed_images) == blocks
+
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+
+    return processed_images
+
+
+# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
+def image_to_pixel_values_internvl(
+    image: Image.Image,
+    *,
+    input_size: int,
+    min_num: int,
+    max_num: int,
+    use_thumbnail: bool,
+) -> torch.Tensor:
+    target_ratios = get_internvl_target_ratios(min_num, max_num)
+
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess_internvl(
+        image,
+        target_ratios=target_ratios,
+        image_size=input_size,
+        use_thumbnail=use_thumbnail,
+    )
+
+    pixel_values = torch.stack([transform(image) for image in images])
+    return pixel_values
+
+
+# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
+def video_to_pixel_values_internvl(
+    video: npt.NDArray,
+    *,
+    input_size: int,
+    min_num: int,
+    max_num: int,
+    use_thumbnail: bool,
+) -> torch.Tensor:
+    target_ratios = get_internvl_target_ratios(min_num, max_num)
+
+    transform = build_transform(input_size=input_size)
+    frames_list = list[Image.Image]()
+    for frame in video:
+        pil_frame = dynamic_preprocess_internvl(
+            Image.fromarray(frame, mode="RGB"),
+            target_ratios=target_ratios,
+            image_size=input_size,
+            use_thumbnail=use_thumbnail,
+        )
+        assert len(pil_frame) == 1
+        frames_list.extend(pil_frame)
+
+    pixel_values = torch.stack([transform(image) for image in frames_list])
+    return pixel_values
+
+
+class BaseInternVLProcessor(ABC):
+    """
+    This model doesn't define its own HF processor,
+    so we implement our own one here.
+
+    The code to insert image tokens is based on:
+    https://huggingface.co/OpenGVLab/InternVL2-1B/blob/main/modeling_internvl_chat.py#L252
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        tokenizer: AnyTokenizer,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.tokenizer = tokenizer
+
+        image_size: int = config.vision_config.image_size
+        patch_size: int = config.vision_config.patch_size
+
+        if min_dynamic_patch is None:
+            min_dynamic_patch = config.min_dynamic_patch
+        assert isinstance(min_dynamic_patch, int)
+
+        if max_dynamic_patch is None:
+            max_dynamic_patch = config.max_dynamic_patch
+        assert isinstance(max_dynamic_patch, int)
+
+        if dynamic_image_size is None:
+            dynamic_image_size = config.dynamic_image_size
+        assert isinstance(dynamic_image_size, bool)
+
+        self.num_image_token = int(
+            (image_size // patch_size)**2 * (config.downsample_ratio**2))
+        self.image_size = image_size
+        self.min_dynamic_patch = min_dynamic_patch
+        self.max_dynamic_patch = max_dynamic_patch
+        self.dynamic_image_size = dynamic_image_size
+        self.use_thumbnail: bool = config.use_thumbnail
+
+    @property
+    @abstractmethod
+    def image_token_id(self) -> int:
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_image_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int],
+    ) -> PromptUpdateDetails[str]:
+        raise NotImplementedError
+
+    def resolve_min_max_num(
+        self,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        use_thumbnail: Optional[bool] = None,
+    ) -> tuple[int, int]:
+        min_dynamic_patch = (self.min_dynamic_patch if min_dynamic_patch
+                             is None else min_dynamic_patch)
+        max_dynamic_patch = (self.max_dynamic_patch if max_dynamic_patch
+                             is None else max_dynamic_patch)
+        dynamic_image_size = (self.dynamic_image_size if dynamic_image_size
+                              is None else dynamic_image_size)
+        use_thumbnail = (self.use_thumbnail
+                         if use_thumbnail is None else use_thumbnail)
+
+        return resolve_internvl_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=use_thumbnail,
+        )
+
+    def resolve_target_ratios(
+        self,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        use_thumbnail: Optional[bool] = None,
+    ) -> list[tuple[int, int]]:
+        min_num, max_num = self.resolve_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=use_thumbnail,
+        )
+
+        return get_internvl_target_ratios(min_num, max_num)
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        target_ratios = self.resolve_target_ratios(
+            use_thumbnail=False,  # Applied in calculate_targets
+        )
+
+        num_patches, _, _ = calculate_internvl_targets(
+            orig_width=image_width,
+            orig_height=image_height,
+            image_size=self.image_size,
+            target_ratios=target_ratios,
+            use_thumbnail=self.use_thumbnail,
+        )
+
+        return num_patches * self.num_image_token
+
+    def _images_to_pixel_values_lst(
+        self,
+        images: list[Image.Image],
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> list[torch.Tensor]:
+        min_num, max_num = self.resolve_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=False,  # Applied in image_to_pixel_values
+        )
+
+        return [
+            image_to_pixel_values_internvl(
+                image,
+                input_size=self.image_size,
+                min_num=min_num,
+                max_num=max_num,
+                use_thumbnail=self.use_thumbnail,
+            ) for image in images
+        ]
+
+    def _preprocess_image(
+        self,
+        text: list[str],
+        images: list[Image.Image],
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> tuple[list[str], dict[str, torch.Tensor]]:
+        if len(images) == 0:
+            image_inputs = {}
+        else:
+            pixel_values_lst = self._images_to_pixel_values_lst(
+                images,
+                min_dynamic_patch=min_dynamic_patch,
+                max_dynamic_patch=max_dynamic_patch,
+                dynamic_image_size=dynamic_image_size,
+            )
+            image_inputs: dict[str, NestedTensors] = {
+                "pixel_values_flat":
+                torch.cat(pixel_values_lst),
+                "image_num_patches":
+                torch.tensor([len(item) for item in pixel_values_lst]),
+            }
+
+            for pixel_values in pixel_values_lst:
+                num_patches = pixel_values.shape[0]
+                feature_size = num_patches * self.num_image_token
+
+                image_repl = self.get_image_repl(feature_size, num_patches)
+                text = [t.replace('<image>', image_repl.full, 1) for t in text]
+        return text, image_inputs
+
+    def _make_batch_input(self,
+                          input_item: Optional[Union[Any, list[Any]]] = None):
+        if input_item is None:
+            input_item = []
+        if not isinstance(input_item, list):
+            input_item = [input_item]
+        return input_item
+
+    def __call__(
+        self,
+        text: Optional[Union[str, list[str]]] = None,
+        images: Optional[Union[Image.Image, list[Image.Image]]] = None,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+    ) -> Mapping[str, NestedTensors]:
+        text, images = [self._make_batch_input(x) for x in (text, images)]
+
+        text, image_inputs = self._preprocess_image(
+            text=text,
+            images=images,
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+        )
+
+        text_inputs = self.tokenizer(text)
+
+        return {
+            **BatchEncoding(text_inputs, tensor_type=return_tensors),
+            **image_inputs,
+        }
+
+
+class InternVLProcessor(BaseInternVLProcessor):
+    """
+    HF Processor for InternVLChatModel with extended video processing logic.
+
+    Code for video processing is adapted from video example:
+    https://huggingface.co/OpenGVLab/InternVL3-1B#inference-with-transformers
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        tokenizer: AnyTokenizer,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        video_token: Optional[str] = None,
+    ) -> None:
+        super().__init__(
+            config=config,
+            tokenizer=tokenizer,
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+        )
+        # add extra video token for video processing
+        self.video_token = video_token
+
+    @property
+    def image_token_id(self) -> int:
+        return self.tokenizer.get_vocab()[IMG_CONTEXT]
+
+    @property
+    def video_token_id(self) -> Optional[int]:
+        if self.video_token is None:
+            return None
+        return self.tokenizer.get_vocab().get(self.video_token, None)
+
+    @property
+    def supports_video(self) -> bool:
+        return self.video_token_id is not None
+
+    def _videos_to_pixel_values_lst(
+        self,
+        videos: list[npt.NDArray],
+        dynamic_image_size: Optional[bool] = None,
+    ) -> list[torch.Tensor]:
+        min_num, max_num = self.resolve_min_max_num(
+            min_dynamic_patch=1,
+            max_dynamic_patch=1,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=False,  # Applied in image_to_pixel_values
+        )
+
+        return [
+            video_to_pixel_values_internvl(
+                video,
+                input_size=self.image_size,
+                min_num=min_num,
+                max_num=max_num,
+                use_thumbnail=False,
+            ) for video in videos
+        ]
+
+    def _preprocess_video(
+        self,
+        text: list[str],
+        videos: list[npt.NDArray],
+        dynamic_image_size: Optional[bool] = None,
+    ):
+        if len(videos) == 0 or not self.supports_video:
+            video_inputs = {}
+        else:
+            pixel_values_lst_video = self._videos_to_pixel_values_lst(
+                videos,
+                dynamic_image_size=dynamic_image_size,
+            )
+            video_inputs: dict[str, NestedTensors] = {
+                "pixel_values_flat_video":
+                torch.cat(pixel_values_lst_video),
+                "video_num_patches":
+                torch.tensor([len(item) for item in pixel_values_lst_video]),
+            }
+
+            for pixel_values in pixel_values_lst_video:
+                num_patches = pixel_values.shape[0]
+
+                video_repl = self.get_video_repl(self.num_image_token,
+                                                 num_patches, self.video_token)
+                text = [t.replace('<video>', video_repl.full, 1) for t in text]
+        return text, video_inputs
+
+    def __call__(
+        self,
+        text: Optional[Union[str, list[str]]] = None,
+        images: Optional[Union[Image.Image, list[Image.Image]]] = None,
+        videos: Optional[Union[npt.NDArray, list[npt.NDArray]]] = None,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+    ) -> Mapping[str, NestedTensors]:
+        text, images, videos = [
+            self._make_batch_input(x) for x in (text, images, videos)
+        ]
+
+        text, image_inputs = self._preprocess_image(
+            text=text,
+            images=images,
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+        )
+
+        text, video_inputs = self._preprocess_video(
+            text=text,
+            videos=videos,
+            dynamic_image_size=dynamic_image_size,
+        )
+
+        text_inputs = self.tokenizer(text)
+
+        return {
+            **BatchEncoding(text_inputs, tensor_type=return_tensors),
+            **image_inputs,
+            **video_inputs,
+        }
+
+    def get_image_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int],
+    ) -> PromptUpdateDetails[str]:
+        repl_features = IMG_CONTEXT * feature_size
+        repl_full = IMG_START + repl_features + IMG_END
+
+        return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
+
+    def get_video_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int] = None,
+        video_context_token: str = IMG_CONTEXT,
+    ) -> PromptUpdateDetails[str]:
+        repl_features = video_context_token * self.num_image_token
+        repl_features_with_sep = IMG_START + repl_features + IMG_END
+        # num_patches is equal to num_frames
+        repl_full = ''.join([
+            f'Frame{i+1}: {repl_features_with_sep}' for i in range(num_patches)
+        ])
+
+        return PromptUpdateDetails.select_text(repl_full, video_context_token)
+
+
+class BaseInternVLProcessingInfo(BaseProcessingInfo):
+    """Basic image-only ProcessingInfo for InternVL-style models."""
+
+    @abstractmethod
+    def get_hf_processor(self, **kwargs: object) -> BaseInternVLProcessor:
+        raise NotImplementedError
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[BaseInternVLProcessor],
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        return processor.get_num_image_tokens(
+            image_width=image_width,
+            image_height=image_height,
+        )
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        processor = self.get_hf_processor()
+
+        base_size = processor.image_size
+        target_ratios = processor.resolve_target_ratios()
+
+        largest_feature_size, largest_feature_pinpoint = 0, None
+        for wr, hr in target_ratios:
+            width, height = base_size * wr, base_size * hr
+
+            feat_size = self.get_num_image_tokens(
+                image_width=width,
+                image_height=height,
+                processor=processor,
+            )
+            if feat_size > largest_feature_size:
+                largest_feature_size = feat_size
+                largest_feature_pinpoint = ImageSize(width=width,
+                                                     height=height)
+
+        if largest_feature_size == 0 or largest_feature_pinpoint is None:
+            raise ValueError("Cannot have a largest feature size of 0!")
+
+        return largest_feature_pinpoint
+
+    def get_max_image_tokens(self) -> int:
+        processor = self.get_hf_processor()
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_image_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            processor=processor,
+        )
+
+
+_I = TypeVar("_I", bound=BaseInternVLProcessingInfo)
+
+
+class BaseInternVLDummyInputsBuilder(BaseDummyInputsBuilder[_I]):
+    """Basic image-only DummyInputsBuilder for InternVL-style models."""
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        return "<image>" * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+        num_images = mm_counts.get("image", 0)
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+
+class BaseInternVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
+    """ Basic image-only MultiModalProcessor for InternVL-style models."""
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> Mapping[str, NestedTensors]:
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+
+        hf_processor = self.info.get_hf_processor(**mm_kwargs)
+        image_token_id = hf_processor.image_token_id
+
+        # Since there may be extra tokens in the feature placeholders,
+        # we need to pass the image token ID to the model to select the
+        # tokens to merge from the vision encoder outputs
+        processed_outputs["image_token_id"] = torch.tensor(image_token_id)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: Mapping[str, NestedTensors],
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        image_num_patches = hf_inputs.get("image_num_patches", torch.empty(0))
+        num_images = len(image_num_patches)
+
+        return dict(
+            pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
+                "image", image_num_patches),
+            image_num_patches=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+            image_token_id=MultiModalFieldConfig.shared("image", num_images),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        if "image_num_patches" in out_mm_kwargs:
+            image_num_patches = out_mm_kwargs["image_num_patches"]
+            assert isinstance(image_num_patches, torch.Tensor)
+            image_num_patches = image_num_patches.tolist()
+        elif "image_embeds" in out_mm_kwargs:
+            # TODO: Use image size information in dictionary embedding inputs
+            # to compute num_patches (similar to Qwen2-VL)
+            image_num_patches = [None] * len(out_mm_kwargs["image_embeds"])
+        else:
+            image_num_patches = []
+
+        def get_replacement_internvl(item_idx: int):
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems))
+
+            if isinstance(images, ImageEmbeddingItems):
+                feature_size = images.get_feature_size(item_idx)
+            else:
+                image_size = images.get_image_size(item_idx)
+                feature_size = self.info.get_num_image_tokens(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                    processor=hf_processor,
+                )
+
+            num_patches = image_num_patches[item_idx]
+            if num_patches is not None:
+                assert isinstance(num_patches, int)
+
+            return hf_processor.get_image_repl(feature_size, num_patches)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target="<image>",
+                replacement=get_replacement_internvl,
+            )
+        ]
+
+
+class InternVLProcessingInfo(BaseInternVLProcessingInfo):
+    """InternVL ProcessingInfo extended for video processing"""
+
+    @property
+    def supports_video(self):
+        return self.get_hf_processor().supports_video
+
+    def get_supported_mm_limits(self):
+        video_limit = {"video": None} if self.supports_video else {}
+        return {**super().get_supported_mm_limits(), **video_limit}
+
+    def get_video_token(self) -> Optional[str]:
+        text_model_type = self.get_hf_config().get_text_config().model_type
+        if text_model_type == "qwen2":
+            return "<|video_pad|>"
+        return None
+
+    def get_num_frames_with_most_features(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        max_images = mm_counts.get("image", 0)
+        max_videos = mm_counts.get("video", 0)
+
+        processor = self.get_hf_processor()
+
+        max_image_tokens = self.get_max_image_tokens() * max_images
+        max_total_frames = (seq_len -
+                            max_image_tokens) // processor.num_image_token
+        max_frames_per_video = max_total_frames // max(max_videos, 1)
+
+        return max(max_frames_per_video, 1)
+
+    def get_hf_processor(self, **kwargs: object) -> InternVLProcessor:
+        return self.ctx.init_processor(
+            InternVLProcessor,
+            config=self.get_hf_config(),
+            tokenizer=self.get_tokenizer(),
+            video_token=self.get_video_token(),
+            **kwargs,
+        )
+
+
+class InternVLDummyInputsBuilder(
+        BaseInternVLDummyInputsBuilder[InternVLProcessingInfo]):
+    """InternVL DummyInputsBuilder extended for video support"""
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_videos = mm_counts.get("video", 0)
+
+        return super().get_dummy_text(mm_counts) + "<video>" * num_videos
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        dummy_image = super().get_dummy_mm_data(seq_len=seq_len,
+                                                mm_counts=mm_counts)
+        if self.info.supports_video:
+            config = self.info.get_hf_config()
+            image_size: int = config.vision_config.image_size
+            target_num_frames = \
+                self.info.get_num_frames_with_most_features(seq_len, mm_counts)
+            num_videos = mm_counts.get("video", 0)
+            dummy_video = {
+                "video":
+                self._get_dummy_videos(width=image_size,
+                                       height=image_size,
+                                       num_frames=target_num_frames,
+                                       num_videos=num_videos)
+            }
+        else:
+            dummy_video = {}
+        return {**dummy_image, **dummy_video}
+
+
+class InternVLMultiModalProcessor(
+        BaseInternVLMultiModalProcessor[InternVLProcessingInfo]):
+    """InternVL MultiModalProcessor extended for video support"""
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> Mapping[str, NestedTensors]:
+        processed_outputs = super()._call_hf_processor(prompt, mm_data,
+                                                       mm_kwargs, tok_kwargs)
+
+        hf_processor = self.info.get_hf_processor(**mm_kwargs)
+        if self.info.supports_video and (
+                video_token_id := hf_processor.video_token_id) is not None:
+            processed_outputs["video_token_id"] = torch.tensor(video_token_id)
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: Mapping[str, NestedTensors],
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        image_fields = super()._get_mm_fields_config(hf_inputs,
+                                                     hf_processor_mm_kwargs)
+        if self.info.supports_video:
+            video_num_patches = hf_inputs.get("video_num_patches",
+                                              torch.empty(0))
+            num_videos = len(video_num_patches)
+            video_fields = dict(
+                pixel_values_flat_video=MultiModalFieldConfig.flat_from_sizes(
+                    "video", video_num_patches),
+                video_num_patches=MultiModalFieldConfig.batched("video"),
+                video_token_id=MultiModalFieldConfig.shared(
+                    "video", num_videos),
+            )
+        else:
+            video_fields = {}
+
+        return image_fields | video_fields
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        prompt_repl: list[PromptUpdate] = super()._get_prompt_updates(
+            mm_items, hf_processor_mm_kwargs, out_mm_kwargs)
+
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        if "video_num_patches" in out_mm_kwargs:
+            video_num_patches = out_mm_kwargs["video_num_patches"]
+            assert isinstance(video_num_patches, torch.Tensor)
+            video_num_patches = video_num_patches.tolist()
+        else:
+            video_num_patches = []
+
+        def get_video_replacement_internvl(item_idx: int):
+            feature_size = hf_processor.num_image_token
+            num_patches = video_num_patches[item_idx]
+            if num_patches is not None:
+                assert isinstance(num_patches, int)
+
+            return hf_processor.get_video_repl(
+                feature_size,
+                num_patches,
+                video_context_token=hf_processor.video_token)
+
+        if self.info.supports_video:
+            prompt_repl.append(
+                PromptReplacement(
+                    modality="video",
+                    target="<video>",
+                    replacement=get_video_replacement_internvl,
+                ))
+        return prompt_repl
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    InternVLMultiModalProcessor,
+    info=InternVLProcessingInfo,
+    dummy_inputs=InternVLDummyInputsBuilder)
+class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP,
+                        SupportsLoRA):
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<image>"
+        if modality.startswith("video"):
+            return "<video>"
+
+        raise ValueError("Only image or video modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+        self._patch_quant_config(config, quant_config)
+
+        image_size = config.force_image_size or config.vision_config.image_size
+        patch_size = config.vision_config.patch_size
+        self.patch_size = patch_size
+        self.num_image_token = int(
+            (image_size // patch_size)**2 * (config.downsample_ratio**2))
+        self.downsample_ratio = config.downsample_ratio
+        self.ps_version = config.ps_version
+
+        self.llm_arch_name = config.text_config.architectures[0]
+        self.is_mono = self.llm_arch_name == 'InternLM2VEForCausalLM'
+        self.vision_model = self._init_vision_model(
+            config,
+            quant_config=quant_config,
+            is_mono=self.is_mono,
+            prefix=maybe_prefix(prefix, "vision_model"),
+        )
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        self.mlp1 = self._init_mlp1(config)
+
+        self.img_context_token_id = None
+        self.video_context_token_id = None
+
+        self.visual_token_mask = None
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _patch_quant_config(self, config: PretrainedConfig,
+                            quant_config: QuantizationConfig):
+        # the awq models from OpenGVLab missing `modules_to_not_convert`
+        # patch the quant_config to add `modules_to_not_convert` back
+        if isinstance(quant_config, AWQConfig):
+            text_config = config.text_config
+            llm_quant_config = getattr(text_config, "quantization_config",
+                                       None)
+            if (not quant_config.modules_to_not_convert) and \
+                (llm_quant_config is not None):
+                quant_config.modules_to_not_convert.append("vision_model")
+
+    def _init_vision_model(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig],
+        *,
+        is_mono: bool,
+        prefix: str,
+    ):
+        if not is_mono:
+            vision_feature_layer = config.select_layer
+            if vision_feature_layer < 0:
+                num_hidden_layers = config.vision_config.num_hidden_layers \
+                    + vision_feature_layer + 1
+            else:
+                num_hidden_layers = vision_feature_layer + 1
+
+            return InternVisionModel(
+                config.vision_config,
+                quant_config=quant_config,
+                num_hidden_layers_override=num_hidden_layers,
+                prefix=prefix,
+            )
+        else:
+            return InternVisionPatchModel(config.vision_config)
+
+    def _init_mlp1(self, config: PretrainedConfig) -> nn.Sequential:
+        vit_hidden_size = config.vision_config.hidden_size
+        llm_hidden_size = config.text_config.hidden_size
+
+        return nn.Sequential(
+            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio)**2),
+            nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio)**2,
+                      llm_hidden_size),
+            nn.GELU(),
+            nn.Linear(llm_hidden_size, llm_hidden_size),
+        )
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        x = x.view(n, int(h * scale_factor), int(w * scale_factor),
+                   int(c / (scale_factor * scale_factor)))
+        if self.ps_version == 'v1':
+            pass
+        else:
+            x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        vit_embeds = self.vision_model(pixel_values=pixel_values)
+        vit_embeds = vit_embeds[:, 1:, :]
+
+        h = w = int(vit_embeds.shape[1]**0.5)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.pixel_shuffle(vit_embeds,
+                                        scale_factor=self.downsample_ratio)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1,
+                                        vit_embeds.shape[-1])
+        vit_embeds = self.mlp1(vit_embeds)
+        return vit_embeds
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[InternVLImageInputs]:
+        pixel_values_flat = kwargs.pop("pixel_values_flat", None)
+        image_num_patches = kwargs.pop("image_num_patches", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values_flat is None and image_embeds is None:
+            return None
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+
+            return InternVLImageEmbeddingInputs(
+                type="image_embeds",
+                data=flatten_bn(image_embeds),
+            )
+
+        image_token_id = kwargs["image_token_id"]
+        assert isinstance(image_token_id, torch.Tensor)
+        self.img_context_token_id = image_token_id.flatten().unique().item()
+
+        if pixel_values_flat is not None:
+            if not isinstance(pixel_values_flat, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values_flat)}")
+
+            if not isinstance(image_num_patches, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image_num_patches. "
+                                 f"Got type: {type(image_num_patches)}")
+
+            pixel_values_flat = flatten_bn(pixel_values_flat, concat=True)
+            image_num_patches = flatten_bn(image_num_patches, concat=True)
+            expected_h = expected_w = self.config.vision_config.image_size
+            resolve_bindings = {"h": expected_h, "w": expected_w}
+
+            return InternVLImagePixelInputs(
+                type="pixel_values",
+                pixel_values_flat=pixel_values_flat,
+                num_patches=image_num_patches,
+                resolve_bindings=resolve_bindings,
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _parse_and_validate_video_input(
+            self, **kwargs: object) -> Optional[InternVLVideoPixelInputs]:
+        pixel_values_flat_video = kwargs.pop("pixel_values_flat_video", None)
+        video_num_patches = kwargs.pop("video_num_patches", None)
+        video_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values_flat_video is None and video_embeds is None:
+            return None
+
+        if video_embeds is not None:
+            return InternVLVideoEmbeddingInputs(
+                type="video_embeds",
+                data=flatten_bn(video_embeds),
+            )
+
+        video_token_id = kwargs["video_token_id"]
+        assert isinstance(video_token_id, torch.Tensor)
+        self.video_context_token_id = video_token_id.flatten().unique().item()
+
+        if pixel_values_flat_video is not None:
+            if not isinstance(pixel_values_flat_video, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values_flat_video)}")
+
+            if not isinstance(video_num_patches, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image_num_patches. "
+                                 f"Got type: {type(video_num_patches)}")
+
+            pixel_values_flat_video = flatten_bn(pixel_values_flat_video,
+                                                 concat=True)
+            video_num_patches = flatten_bn(video_num_patches, concat=True)
+            expected_h = expected_w = self.config.vision_config.image_size
+            resolve_bindings = {"h": expected_h, "w": expected_w}
+
+            return InternVLVideoPixelInputs(
+                type="pixel_values_videos",
+                pixel_values_flat=pixel_values_flat_video,
+                num_patches=video_num_patches,
+                resolve_bindings=resolve_bindings,
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _process_image_input(
+        self,
+        image_input: Union[InternVLImageInputs, InternVLVideoPixelInputs],
+    ) -> tuple[torch.Tensor, ...]:
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
+        assert self.vision_model is not None
+
+        image_embeds = self.extract_feature(image_input["pixel_values_flat"])
+
+        num_patches = image_input["num_patches"]
+
+        # Only one image in the current batch
+        if len(num_patches) == 1:
+            return (image_embeds.view(-1,
+                                      self.config.text_config.hidden_size), )
+
+        # NOTE: Image embeddings are split into separate tensors for each image
+        # by the size of each embedding.
+        feature_size = image_embeds.shape[1]
+        image_embeds = image_embeds.view(-1,
+                                         self.config.text_config.hidden_size)
+        image_feature_sizes = [
+            num_patches * feature_size for num_patches in num_patches
+        ]
+        return image_embeds.split(image_feature_sizes)
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        modalities = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if input_key in ("pixel_values_flat",
+                             "image_embeds") and "images" not in modalities:
+                modalities["images"] = self._parse_and_validate_image_input(
+                    **kwargs)
+            if input_key in ("pixel_values_flat_video",
+                             ) and "videos" not in modalities:
+                modalities["videos"] = self._parse_and_validate_video_input(
+                    **kwargs)
+
+        return modalities
+
+    def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None:
+        if self.is_mono:
+            assert self.img_context_token_id is not None
+            self.visual_token_mask = (
+                input_ids == self.img_context_token_id).reshape(-1, 1)
+        else:
+            self.visual_token_mask = None
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+
+        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not modalities:
+            return []
+
+        # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor correspoending to a multimodal data item (image or video).
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in modalities:
+            if modality == "images":
+                image_input = modalities["images"]
+                vision_embeddings = self._process_image_input(image_input)
+                multimodal_embeddings += vision_embeddings
+            if modality == "videos":
+                video_input = modalities["videos"]
+                video_embeddings = self._process_image_input(video_input)
+                multimodal_embeddings += video_embeddings
+
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            context_token_ids = [
+                token_id for token_id in (self.img_context_token_id,
+                                          self.video_context_token_id)
+                if token_id is not None
+            ]
+            assert len(context_token_ids) >= 1
+            self._set_visual_token_mask(input_ids)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                context_token_ids,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> IntermediateTensors:
+
+        if intermediate_tensors is not None:
+            input_ids = None
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        forward_kwargs = {
+            "input_ids": input_ids,
+            "positions": positions,
+            "intermediate_tensors": intermediate_tensors,
+            "inputs_embeds": inputs_embeds,
+        }
+
+        # Only required if the model is mono-architecture
+        if self.visual_token_mask is not None:
+            forward_kwargs.update(
+                {"visual_token_mask": self.visual_token_mask})
+            self.visual_token_mask = None
+
+        hidden_states = self.language_model.model(**forward_kwargs)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        # unused modules appear in OpenGVLab/InternVideo2_5_Chat_8B
+        skip_prefixes = [
+            "action_embed", "temporal_embed", "track_embed",
+            "track_embed_decoder", "box_token", "cg_criterion", "cg_model",
+            "loc_encoder", "loc_decoder", "sam", "temporal_token",
+            "track_token"
+        ]
+        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
+        return loader.load_weights(weights)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="mlp1",
+            tower_model="vision_model")
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/jais.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/jais.py
new file mode 100644
index 0000000000000000000000000000000000000000..bed4a5dff2efae1a10d25b10c709e2150fa8eccc
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/jais.py
@@ -0,0 +1,373 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://huggingface.co/inceptionai/jais-30b-chat-v3/blob/main/modeling_jais.py
+# Copyright 2023 The vLLM team.
+# Copyright 2023 the Jais authors and HuggingFace Inc. team.  All rights
+# reserved.
+# Copyright 2023 Cerebras Systems.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Jais model compatible with HuggingFace weights."""
+
+import math
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs import JAISConfig
+
+from .interfaces import SupportsPP
+from .utils import (is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class SwiGLUActivation(nn.Module):
+
+    def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
+        return x1 * nn.functional.silu(x2)
+
+
+def _get_alibi_slopes(n):
+
+    def get_slopes_power_of_2(n):
+        start = 2**(-(2**-(math.log2(n) - 3)))
+        ratio = start
+        return [start * ratio**i for i in range(n)]
+
+    if math.log2(n).is_integer():
+        return get_slopes_power_of_2(n)
+    else:
+        closest_power_of_2 = 2**math.floor(math.log2(n))
+        return (get_slopes_power_of_2(closest_power_of_2) + _get_alibi_slopes(
+            2 * closest_power_of_2)[0::2][:n - closest_power_of_2])
+
+
+class JAISAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: JAISConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        total_num_heads = config.num_attention_heads
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        assert total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = total_num_heads // tensor_model_parallel_world_size
+        self.head_dim = self.hidden_size // total_num_heads
+        if hasattr(config, "scale_qk_dot_by_d"):
+            config.mup_scale_qk_dot_by_d = config.scale_qk_dot_by_d
+        self.attn_scale_power = 1.0 if config.mup_scale_qk_dot_by_d else 0.5
+        self.scale = self.head_dim**-self.attn_scale_power
+
+        self.c_attn = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            total_num_heads,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.c_proj = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+        )
+
+        tp_rank = get_tensor_model_parallel_rank()
+        head_start = tp_rank * self.num_heads
+        head_end = (tp_rank + 1) * self.num_heads
+        alibi_slopes = _get_alibi_slopes(total_num_heads)
+        alibi_slopes = alibi_slopes[head_start:head_end]
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              scale=self.scale,
+                              alibi_slopes=alibi_slopes,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.c_attn(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        attn_output = self.attn(q, k, v)
+        attn_output, _ = self.c_proj(attn_output)
+        return attn_output
+
+
+class JAISMLP(nn.Module):
+
+    def __init__(
+        self,
+        intermediate_size: int,
+        config: JAISConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.swiglu = config.activation_function == "swiglu"
+        self.c_fc = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.c_fc2 = (ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=True,
+            quant_config=quant_config,
+        ) if self.swiglu else None)
+        self.c_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=True,
+            quant_config=quant_config,
+        )
+
+        self.act = SwiGLUActivation()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.swiglu:
+            hidden_states2, _ = self.c_fc2(hidden_states)
+        hidden_states, _ = self.c_fc(hidden_states)
+        hidden_states = (self.act(hidden_states, hidden_states2)
+                         if self.swiglu else self.act(hidden_states))
+        hidden_states, _ = self.c_proj(hidden_states)
+        return hidden_states
+
+
+class JAISBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: JAISConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        inner_dim = (config.n_inner if config.n_inner is not None else 4 *
+                     hidden_size)
+
+        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.attn = JAISAttention(config,
+                                  cache_config,
+                                  quant_config,
+                                  prefix=f"{prefix}.attn")
+        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.mlp = JAISMLP(inner_dim, config, quant_config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_output = self.attn(hidden_states=hidden_states, )
+        # residual connection
+        hidden_states = attn_output + residual
+
+        residual = hidden_states
+        hidden_states = self.ln_2(hidden_states)
+        feed_forward_hidden_states = self.mlp(hidden_states)
+        # residual connection
+        hidden_states = residual + feed_forward_hidden_states
+        return hidden_states
+
+
+@support_torch_compile
+class JAISModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        assert not config.add_cross_attention
+        assert not config.scale_attn_by_inverse_layer_idx
+        assert not config.reorder_and_upcast_attn
+        self.embed_dim = config.hidden_size
+        self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
+        self.wpe = (nn.Embedding(config.max_position_embeddings,
+                                 self.embed_dim)
+                    if config.position_embedding_type != "alibi" else None)
+        if hasattr(config, "embeddings_scale"):
+            self.embeddings_scale = config.embeddings_scale
+        else:
+            self.embeddings_scale = config.mup_embeddings_scale
+
+        self.start_layer, self.end_layer, self.h = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: JAISBlock(config=config,
+                                     cache_config=cache_config,
+                                     quant_config=quant_config,
+                                     prefix=prefix),
+            prefix=f"{prefix}.h",
+        )
+
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.n_embd))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.wte(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[IntermediateTensors, torch.Tensor]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is None:
+                inputs_embeds = self.get_input_embeddings(input_ids)
+            if self.wpe is not None:
+                position_embeds = self.wpe(position_ids)
+                hidden_states = inputs_embeds + position_embeds
+            else:
+                hidden_states = inputs_embeds
+            hidden_states *= torch.tensor(float(self.embeddings_scale),
+                                          dtype=hidden_states.dtype)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+
+        for layer in self.h[self.start_layer:self.end_layer]:
+            hidden_states = layer(hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class JAISLMHeadModel(nn.Module, SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.transformer = JAISModel(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(
+                                         prefix, "transformer"))
+        if self.config.tie_word_embeddings:
+            self.lm_head = self.transformer.wte
+        else:
+            self.lm_head = ParallelLMHead(self.config.vocab_size,
+                                          self.config.hidden_size)
+        if hasattr(config, "width_scale"):
+            self.output_logits_scale = config.width_scale
+        else:
+            self.output_logits_scale = (config.mup_output_alpha *
+                                        config.mup_width_scale)
+        self.logits_processor = LogitsProcessor(vocab_size=config.vocab_size,
+                                                scale=self.output_logits_scale)
+        self.make_empty_intermediate_tensors = (
+            self.transformer.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[IntermediateTensors, torch.Tensor]:
+        hidden_states = self.transformer(input_ids, positions,
+                                         intermediate_tensors, inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "lm_head.weight" in name:
+                # GPT-2 ties the weights of the embedding layer and the final
+                # linear layer.
+                continue
+            if ".attn.bias" in name or ".attn.masked_bias" in name:
+                # Skip attention mask.
+                # NOTE: "c_attn.bias" should not be skipped.
+                continue
+            if "relative_pe" in name:
+                continue
+            if not name.startswith("transformer."):
+                name = "transformer." + name
+
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            param = params_dict[name]
+            # The HF's GPT-2 implementation uses Conv1D instead of Linear.
+            # Because of this, we need to transpose the weights.
+            # Note(zhuohan): the logic below might break quantized models.
+            for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
+                if conv1d_weight_name not in name:
+                    continue
+                if not name.endswith(".weight"):
+                    continue
+                loaded_weight = loaded_weight.t()
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/jamba.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/jamba.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b32d6f256590ea1b52920b2cd130377dadcfa38
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/jamba.py
@@ -0,0 +1,627 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only Jamba model."""
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+from torch import nn
+from transformers import JambaConfig
+
+from vllm import envs
+from vllm.attention.layer import Attention
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.distributed.parallel_state import get_pp_group
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer
+from vllm.model_executor.layers.mamba.mamba_utils import (
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
+from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.llama import LlamaMLP as JambaMLP
+from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
+                                                    MambaCacheParams)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+from vllm.utils import LayerBlockType
+
+from .interfaces import HasInnerState, IsHybrid, SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class JambaMoE(nn.Module):
+
+    def __init__(self,
+                 config: JambaConfig,
+                 num_experts: Optional[int] = None,
+                 top_k: Optional[int] = None,
+                 params_dtype: Optional[torch.dtype] = None,
+                 tp_size: Optional[int] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.num_total_experts = num_experts or config.num_experts
+        self.top_k = top_k or config.num_experts_per_tok
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+
+        if self.num_total_experts > 1:
+            self.router = ReplicatedLinear(self.hidden_size,
+                                           self.num_total_experts,
+                                           bias=False,
+                                           quant_config=None,
+                                           params_dtype=params_dtype)
+
+        self.experts = FusedMoE(self.num_total_experts,
+                                self.top_k,
+                                self.hidden_size,
+                                self.intermediate_size,
+                                tp_size=tp_size,
+                                params_dtype=params_dtype,
+                                reduce_results=True,
+                                renormalize=False,
+                                use_grouped_topk=False,
+                                quant_config=quant_config,
+                                prefix=f"{prefix}.experts")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        orig_shape = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        # router_logits: (batch * sequence_length, n_experts)
+        if self.num_total_experts > 1:
+            router_logits, _ = self.router(hidden_states)
+        else:
+            router_logits = torch.ones((hidden_states.shape[0], 1),
+                                       device=hidden_states.device,
+                                       dtype=hidden_states.dtype)
+        hidden_states = self.experts(hidden_states, router_logits)
+        return hidden_states.view(orig_shape)
+
+
+class JambaMambaDecoderLayer(nn.Module):
+
+    def __init__(self,
+                 config: JambaConfig,
+                 layer_idx: int,
+                 model_config: Optional[ModelConfig] = None,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 is_lora_enabled: Optional[bool] = False,
+                 prefix: str = "",
+                 **kwargs) -> None:
+        super().__init__()
+        self.config = config
+        self.is_lora_enabled = is_lora_enabled
+        self.mamba = MambaMixer(hidden_size= config.hidden_size,
+                                ssm_state_size = config.mamba_d_state,
+                                conv_kernel_size = config.mamba_d_conv,
+                                intermediate_size = config.mamba_expand *\
+                                                    config.hidden_size,
+                                time_step_rank = config.mamba_dt_rank,
+                                use_conv_bias = config.mamba_conv_bias,
+                                use_bias = config.mamba_proj_bias,
+                                use_rms_norm=True,
+                                rms_norm_eps=config.rms_norm_eps,
+                                activation=config.hidden_act,
+                                is_lora_enabled = self.is_lora_enabled,
+                                model_config=model_config,
+                                cache_config=cache_config,
+                                prefix=f"{prefix}.mixer",
+                                )
+
+        num_experts = config.layers_num_experts[layer_idx]
+        if num_experts > 1:
+            self.feed_forward = JambaMoE(
+                config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.feed_forward",
+            )
+        else:
+            self.feed_forward = JambaMLP(
+                config.hidden_size,
+                config.intermediate_size,
+                config.hidden_act,
+                quant_config=quant_config,
+                prefix=f"{prefix}.feed_forward",
+            )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.pre_ff_layernorm = RMSNorm(config.hidden_size,
+                                        eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        mamba_cache_params: MambaCacheParams,
+        **kwargs,
+    ):
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+
+        hidden_states = self.mamba(hidden_states, mamba_cache_params)
+        # Fully Connected
+        hidden_states, residual = self.pre_ff_layernorm(
+            hidden_states, residual)
+        hidden_states = self.feed_forward(hidden_states)
+        return hidden_states, residual
+
+
+class JambaAttentionDecoderLayer(nn.Module):
+
+    def __init__(self,
+                 config: JambaConfig,
+                 layer_idx: int,
+                 model_config: Optional[ModelConfig] = None,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = "",
+                 **kwargs) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = config.num_key_value_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = config.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            config.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
+                                        config.hidden_size,
+                                        bias=False,
+                                        quant_config=quant_config)
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            prefix=f"{prefix}.attn",
+        )
+
+        num_experts = config.layers_num_experts[layer_idx]
+        if num_experts > 1:
+            self.feed_forward = JambaMoE(
+                config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.feed_forward",
+            )
+        else:
+            self.feed_forward = JambaMLP(
+                config.hidden_size,
+                config.intermediate_size,
+                config.hidden_act,
+                quant_config=quant_config,
+                prefix=f"{prefix}.feed_forward",
+            )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.pre_ff_layernorm = RMSNorm(config.hidden_size,
+                                        eps=config.rms_norm_eps)
+
+    def self_attention(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        **kwargs,
+    ):
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+
+        hidden_states = self.self_attention(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        # Fully Connected
+        hidden_states, residual = self.pre_ff_layernorm(
+            hidden_states, residual)
+        hidden_states = self.feed_forward(hidden_states)
+        return hidden_states, residual
+
+
+ALL_DECODER_LAYER_TYPES = {
+    "attention": JambaAttentionDecoderLayer,
+    "mamba": JambaMambaDecoderLayer
+}
+
+
+class JambaModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        lora_vocab = ((lora_config.lora_extra_vocab_size *
+                       (lora_config.max_loras or 1)) if lora_config else 0)
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+
+        extra_kwargs = {"is_lora_enabled": bool(vllm_config.lora_config)}
+
+        def get_layer(prefix: str):
+            layer_idx = int(prefix.rsplit(".", 1)[1])
+            layer_class = ALL_DECODER_LAYER_TYPES[
+                config.layers_block_type[layer_idx]]
+            return layer_class(config,
+                               layer_idx,
+                               model_config,
+                               cache_config,
+                               quant_config=quant_config,
+                               prefix=prefix,
+                               **extra_kwargs)
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers")
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+        self.final_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        mamba_cache_params: MambaCacheParams,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        kv_cache_index = 0
+        mamba_cache_index = 0
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            layer_mamba_cache_params = None
+            if isinstance(layer, JambaAttentionDecoderLayer):
+                kv_cache_index += 1
+            if isinstance(layer,
+                          JambaMambaDecoderLayer) and mamba_cache_params:
+                current_state_layer = mamba_cache_index
+                layer_mamba_cache_params = mamba_cache_params.at_layer_idx(
+                    current_state_layer)
+                mamba_cache_index += 1
+
+            hidden_states, residual = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                residual=residual,
+                mamba_cache_params=layer_mamba_cache_params)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.final_layernorm(hidden_states, residual)
+        return hidden_states
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if 'experts' in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for (
+                        param_name,
+                        weight_name,
+                        expert_id,
+                        shard_id,
+                ) in expert_params_mapping:
+                    if weight_name not in name:
+                        continue
+
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param,
+                                  loaded_weight,
+                                  name,
+                                  shard_id=shard_id,
+                                  expert_id=expert_id)
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    if is_pp_missing_parameter(name, self):
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
+                       IsHybrid):
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_substr={
+        ".self_attn.": ".",
+        ".A_log": ".A"
+    }, )
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+        "in_proj": ["in_proj"],
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        lora_config = vllm_config.lora_config
+        scheduler_config = vllm_config.scheduler_config
+        assert not cache_config.enable_prefix_caching, \
+            "Jamba currently does not support prefix caching"
+
+        super().__init__()
+        self.config = config
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+        self.scheduler_config = scheduler_config
+        self.model = JambaModel(vllm_config=vllm_config,
+                                prefix=maybe_prefix(prefix, "model"))
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE
+            # We need bigger padding if using lora for kernel
+            # compatibility
+            if not lora_config else lora_config.lora_vocab_padding_size,
+        )
+        # Used to track and store by the Mamba cache between steps.
+        self.mamba_cache: Optional[MambaCacheManager] = None
+
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size)
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs):
+        # NOTE: mamba_cache_params is not needed for v1
+        mamba_cache_params = None
+        if not envs.VLLM_USE_V1:
+            if self.mamba_cache is None:
+                num_layers = self.model_config.get_num_layers_by_block_type(
+                    self.vllm_config.parallel_config, LayerBlockType.mamba)
+                state_shape = self.get_mamba_state_shape_from_config(
+                    self.vllm_config)
+                state_dtype = self.get_mamba_state_dtype_from_config(
+                    self.vllm_config)
+                self.mamba_cache = MambaCacheManager(self.vllm_config,
+                                                     num_layers, *state_shape,
+                                                     *state_dtype)
+
+            mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
+
+        hidden_states = self.model(input_ids, positions, mamba_cache_params,
+                                   intermediate_tensors, inputs_embeds)
+        return hidden_states
+
+    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
+        return self.mamba_cache.copy_inputs_before_cuda_graphs(
+            input_buffers, **kwargs)
+
+    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
+        return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
+
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.mamba1_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+            vllm_config.cache_config.mamba_ssm_cache_dtype,
+        )
+
+    @classmethod
+    def get_mamba_state_shape_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[tuple[int, int], tuple[int, int]]:
+        parallel_config = vllm_config.parallel_config
+        hf_config = vllm_config.model_config.hf_config
+        hidden_size = hf_config.hidden_size
+
+        return MambaStateShapeCalculator.mamba1_state_shape(
+            tp_world_size=parallel_config.tensor_parallel_size,
+            intermediate_size=hf_config.mamba_expand * hidden_size,
+            state_size=hf_config.mamba_d_state,
+            conv_kernel=hf_config.mamba_d_conv,
+            use_v1=envs.VLLM_USE_V1,
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
+
+
+class JambaForSequenceClassification(JambaForCausalLM):
+
+    is_pooling_model = True
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        config = vllm_config.model_config.hf_config
+        num_labels: int = config.num_labels
+        score_bias: bool = getattr(config, 'score_bias', False)
+
+        # TODO: The original reward weights have float32 accuracy data, we
+        # would like to load them in fp32 to get that extra precision.
+        # Currently weight_loader passes the weight which is already in bf16
+        self.score = nn.Linear(
+            config.hidden_size,
+            num_labels,
+            bias=score_bias,
+            dtype=torch.float32,
+        )
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.pooler = DispatchPooler({
+            "encode":
+            Pooler.for_encode(pooler_config),
+            "classify":
+            Pooler.for_classify(
+                pooler_config,
+                classifier=self.score,
+            ),
+        })
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/keye.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/keye.py
new file mode 100644
index 0000000000000000000000000000000000000000..db9ed5910d78bfbec8e0a5fc24898f8307aaa9f7
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/keye.py
@@ -0,0 +1,1643 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from functools import partial
+from typing import Annotated, Any, Literal, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from transformers import PretrainedConfig
+from transformers.activations import GELUActivation
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.modeling_outputs import (BaseModelOutput,
+                                           BaseModelOutputWithPooling)
+from transformers.utils import torch_int
+
+from vllm.config import VllmConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.gptq import GPTQConfig
+from vllm.model_executor.layers.quantization.gptq_marlin import (
+    GPTQMarlinConfig)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.multimodal import MULTIMODAL_REGISTRY, NestedTensors
+from vllm.multimodal.inputs import (ImageItem, ModalityData,
+                                    MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs, VideoItem)
+from vllm.multimodal.parse import (DictEmbeddingItems, ImageSize,
+                                   ModalityDataItems, MultiModalDataItems,
+                                   MultiModalDataParser)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.platforms import _Backend
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.config import uses_mrope
+from vllm.utils import is_list_of
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
+from .siglip import SiglipMLP
+from .utils import (AutoWeightsLoader, WeightsMapper,
+                    init_vllm_registered_model, is_pp_missing_parameter,
+                    maybe_prefix, merge_multimodal_embeddings)
+from .vision import get_vit_attn_backend
+
+logger = init_logger(__name__)
+
+_MAX_FRAMES_PER_VIDEO = 16
+_MAX_IMAGE_SIZE = 9999999
+
+
+def smart_resize(
+    height: int,
+    width: int,
+    factor: int = 28,
+    min_pixels: int = 28 * 28 * 130,
+    max_pixels: int = 28 * 28 * 1280,
+):
+    if height < factor:
+        logger.warning(
+            "smart_resize: height=%s < factor=%s, reset height=factor",
+            height,
+            factor,
+        )
+        width = round((width * factor) / height)
+        height = factor
+
+    if width < factor:
+        logger.warning(
+            "smart_resize: width=%s < factor=%s, reset width=factor",
+            width,
+            factor,
+        )
+        height = round((height * factor) / width)
+        width = factor
+
+    if max(height, width) / min(height, width) > 200:
+        raise ValueError("absolute aspect ratio must be smaller than 200, got "
+                         "{max(height, width) / min(height, width)}")
+    h_bar = round(height / factor) * factor
+    w_bar = round(width / factor) * factor
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = math.floor(height / beta / factor) * factor
+        w_bar = math.floor(width / beta / factor) * factor
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = math.ceil(height * beta / factor) * factor
+        w_bar = math.ceil(width * beta / factor) * factor
+    return h_bar, w_bar
+
+
+class KeyeImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - b: Batch size
+        - np: Number of patches
+        - c: Number of channels
+        - ps: Patch size
+        - ni: Number of images
+        - g: Grid dimensions (3 for t, h, w)
+    """
+    type: Literal["pixel_values"]
+    pixel_values: Annotated[
+        torch.Tensor,
+        TensorShape("b", "np", 3, "ps", "ps", dynamic_dims={"np"})]
+    image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]
+
+
+class KeyeImageEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - nf: Number of image features
+        - hs: Hidden size (must match the hidden size of language model 
+          backbone)
+        - ni: Number of images
+        - g: Grid dimensions (3 for t, h, w)
+    """
+    type: Literal["image_embeds"]
+    image_embeds: Annotated[torch.Tensor, TensorShape("nf", "hs")]
+    image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]
+
+
+KeyeImageInputs = Union[KeyeImagePixelInputs, KeyeImageEmbeddingInputs]
+
+
+class KeyeVideoPixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - b: Batch size
+        - np: Number of patches
+        - c: Number of channels
+        - ps: Patch size
+        - ni: Number of images
+        - g: Grid dimensions (3 for t, h, w)
+    """
+    type: Literal["pixel_values_videos"]
+    pixel_values_videos: Annotated[
+        torch.Tensor,
+        TensorShape("b", "np", 3, "ps", "ps", dynamic_dims={"np"})]
+    video_grid_thw: Annotated[torch.Tensor, TensorShape("nv", 3)]
+
+
+class KeyeVideoEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - nf: Number of video features
+        - hs: Hidden size (must match the hidden size of language model 
+          backbone)
+        - nv: Number of videos
+        - g: Grid dimensions (3 for t, h, w)
+    """
+    type: Literal["video_embeds"]
+    video_embeds: Annotated[torch.Tensor, TensorShape("nf", "hs")]
+    video_grid_thw: Annotated[torch.Tensor, TensorShape("nv", 3)]
+
+
+KeyeVideoInputs = Union[KeyeVideoPixelInputs, KeyeVideoEmbeddingInputs]
+
+
+class KeyeVisionEmbeddings(nn.Module):
+
+    def __init__(self, config: PretrainedConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+
+        self.num_patches = (self.image_size // self.patch_size)**2
+        self.num_positions = self.num_patches
+        self.cache_position_embedding = dict()
+        self.cache_position_count = dict()
+        self.position_embedding = nn.Embedding(self.num_positions,
+                                               self.embed_dim)
+        self.packing_position_embedding = nn.Embedding(32768, self.embed_dim)
+
+        self.register_buffer(
+            "position_ids",
+            torch.arange(self.num_positions).expand((1, -1)),
+            persistent=False,
+        )
+
+    def interpolate_pos_encoding(
+        self,
+        embeddings: torch.Tensor,
+        height: int,
+        width: int,
+        is_after_patchify: bool = False,
+    ) -> torch.Tensor:
+
+        num_positions = self.position_embedding.weight.shape[0]
+
+        patch_pos_embed = self.position_embedding.weight.unsqueeze(0)
+
+        dim = embeddings.shape[-1]
+
+        if is_after_patchify:
+            new_height = height
+            new_width = width
+        else:
+            new_height = height // self.patch_size
+            new_width = width // self.patch_size
+
+        sqrt_num_positions = torch_int(num_positions**0.5)
+        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions,
+                                                  sqrt_num_positions, dim)
+        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed,
+            size=(new_height, new_width),
+            mode="bilinear",
+            align_corners=False,
+        )
+
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return patch_pos_embed
+
+    def fetch_position_embedding_lfu_cache(self,
+                                           embeddings,
+                                           h,
+                                           w,
+                                           max_cache: int = 20):
+        grid = (h, w)
+        if grid in self.cache_position_embedding:
+            self.cache_position_count[grid] += 1
+            return self.cache_position_embedding[grid]
+
+        if len(self.cache_position_embedding) >= max_cache:
+            min_hit_grid = min(
+                self.cache_position_count,
+                key=self.cache_position_count.get,
+            )
+            self.cache_position_count.pop(min_hit_grid)
+            self.cache_position_embedding.pop(min_hit_grid)
+
+        position_embedding = self.interpolate_pos_encoding(
+            embeddings, h, w, True)
+        self.cache_position_count[grid] = 1
+        self.cache_position_embedding[grid] = position_embedding
+        return position_embedding
+
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor,
+        position_ids: Optional[torch.Tensor] = None,
+        image_grid_thw: Optional[list[Union[
+            tuple[int, int, int],
+            list[tuple[int, int, int]],
+        ]]] = None,
+        interpolate_pos_encoding=False,
+    ) -> torch.Tensor:
+        if pixel_values.dim() == 4:
+            pixel_values = pixel_values.unsqueeze(0)
+        if pixel_values.dim() == 5:
+            if position_ids is None:
+                raise ValueError(
+                    "position_ids cannot be None when pixel_values.dim() is 5."
+                )
+            (
+                batch_size,
+                squence_len,
+                channel,
+                height,
+                width,
+            ) = pixel_values.shape
+            target_dtype = self.patch_embedding.weight.dtype
+            pixel_values = rearrange(pixel_values, "b l c h w -> (b l) c h w")
+            patch_embeds = self.patch_embedding(
+                pixel_values.to(dtype=target_dtype))
+            embeddings = patch_embeds.flatten(-2).squeeze(-1)
+
+            if interpolate_pos_encoding and image_grid_thw is not None:
+                start = 0
+                tmp_embeddings = list()
+                for image_grid in image_grid_thw:
+                    t, h, w = image_grid
+                    end = start + t * h * w
+                    image_embeddings = embeddings[start:end, :]
+                    position_embedding = (self.interpolate_pos_encoding(
+                        image_embeddings, h, w, True).squeeze(0).repeat(t, 1))
+                    image_embeddings = image_embeddings + position_embedding
+                    tmp_embeddings.append(image_embeddings)
+                    start = end
+                embeddings = torch.concat(tmp_embeddings, dim=0).unsqueeze(0)
+            else:
+                embeddings = embeddings + self.packing_position_embedding(
+                    position_ids)
+            return embeddings
+        else:
+            raise ValueError("Unsupported pixel_values dimension:"
+                             f" {pixel_values.dim()}. Expected 4 or 5.")
+
+
+def apply_rotary_pos_emb_flashatt(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    cos = cos.chunk(2, dim=-1)[0].contiguous()
+    sin = sin.chunk(2, dim=-1)[0].contiguous()
+
+    from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb
+
+    q_embed = apply_rotary_emb(q.float(), cos.float(), sin.float()).type_as(q)
+    k_embed = apply_rotary_emb(k.float(), cos.float(), sin.float()).type_as(k)
+    return q_embed, k_embed
+
+
+class KeyeSiglipAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You
+    Need' paper."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+
+        hidden_size = config.hidden_size
+        self.hidden_size = config.hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = config.num_attention_heads
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = config.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scale = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.out_proj = RowParallelLinear(
+            input_size=hidden_size,
+            output_size=hidden_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+        # Detect attention implementation.
+        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
+        if self.attn_backend not in {_Backend.FLASH_ATTN, _Backend.XFORMERS}:
+            raise RuntimeError(
+                f"Keye-VL does not support {self.attn_backend} backend now.")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+        cu_seqlens: Optional[list[torch.Tensor]] = None,
+        rope_emb: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split(
+            [self.q_size, self.kv_size, self.kv_size],
+            dim=-1,
+        )
+
+        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
+        seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+        batch_size = q.shape[0]
+
+        if rope_emb is None:
+            q = q.view(*q.shape[:-1], self.num_heads, self.head_dim)
+            k = k.view(
+                *k.shape[:-1],
+                self.num_kv_heads,
+                self.head_dim,
+            )
+            v = v.view(
+                *v.shape[:-1],
+                self.num_kv_heads,
+                self.head_dim,
+            )
+        else:
+            if cu_seqlens is None:
+                raise ValueError(
+                    "cu_seqlens cannot be None when rope_emb is not None.")
+            cos, sin = rope_emb
+            q = q.view(*q.shape[:-1], self.num_heads, self.head_dim)
+            k = k.view(
+                *k.shape[:-1],
+                self.num_kv_heads,
+                self.head_dim,
+            )
+            q, k = apply_rotary_pos_emb_flashatt(q, k, cos, sin)
+            v = v.view(
+                *v.shape[:-1],
+                self.num_kv_heads,
+                self.head_dim,
+            )
+
+        if self.attn_backend == _Backend.FLASH_ATTN:
+            from flash_attn import flash_attn_varlen_func
+
+            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
+
+            output = flash_attn_varlen_func(
+                q,
+                k,
+                v,
+                cu_seqlens_q=cu_seqlens,
+                cu_seqlens_k=cu_seqlens,
+                max_seqlen_q=max_seqlen,
+                max_seqlen_k=max_seqlen,
+                causal=False,
+                softmax_scale=self.scale,
+            )
+            context_layer = rearrange(output,
+                                      "(b s) ... -> b s ...",
+                                      b=batch_size)
+        elif self.attn_backend == _Backend.XFORMERS:
+            from xformers import ops as xops
+            from xformers.ops.fmha.attn_bias import BlockDiagonalMask
+
+            attn_bias = BlockDiagonalMask.from_seqlens(q_seqlen=seqlens,
+                                                       kv_seqlen=None,
+                                                       device=q.device)
+
+            context_layer = xops.memory_efficient_attention_forward(
+                q, k, v, attn_bias=attn_bias, p=0, scale=None)
+
+        context_layer = rearrange(context_layer,
+                                  "b s h d -> b s (h d)").contiguous()
+
+        output, _ = self.out_proj(context_layer)
+        return output
+
+
+class SigLIPRotaryEmbedding(nn.Module):
+
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.rope_init()
+
+    def rope_init(self):
+        inv_freq = 1.0 / (self.theta**(
+            torch.arange(0, self.dim, 2, dtype=torch.float) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        seq = torch.arange(
+            seqlen,
+            device=self.inv_freq.device,
+            dtype=self.inv_freq.dtype,
+        )
+        freqs = torch.outer(seq, self.inv_freq)
+        return freqs
+
+
+class KeyeSiglipEncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Union[PretrainedConfig],
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim,
+                                        eps=config.layer_norm_eps)
+        self.self_attn = KeyeSiglipAttention(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim,
+                                        eps=config.layer_norm_eps)
+        self.mlp = SiglipMLP(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        output_attentions: Optional[bool] = False,
+        cu_seqlens: Optional[list[torch.Tensor]] = None,
+        rope_emb: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> tuple[torch.FloatTensor]:
+
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            cu_seqlens=cu_seqlens,
+            rope_emb=rope_emb,
+        )
+
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class KeyeSiglipEncoder(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        embed_dim = config.hidden_size
+        num_heads = config.num_attention_heads
+        head_dim = embed_dim // num_heads
+        self.layers = nn.ModuleList([
+            KeyeSiglipEncoderLayer(
+                config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.layers.{layer_idx}",
+            ) for layer_idx in range(config.num_hidden_layers)
+        ])
+        self.rotary_pos_emb = SigLIPRotaryEmbedding(head_dim // 2)
+
+    @staticmethod
+    def flatten_list(image_grid_thw):
+        tmp_image_grid_thw = list()
+        for image_grid in image_grid_thw:
+            if isinstance(image_grid, list):
+                tmp_image_grid_thw.extend(image_grid)
+            else:
+                tmp_image_grid_thw.append(image_grid)
+        return tmp_image_grid_thw
+
+    def forward(
+        self,
+        inputs_embeds,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        cu_seqlens: Optional[list[torch.Tensor]] = None,
+        image_grid_thw: Optional[list[Union[
+            tuple[int, int, int],
+            list[tuple[int, int, int]],
+        ]]] = None,
+        height_position_ids: Optional[torch.Tensor] = None,
+        width_position_ids: Optional[torch.Tensor] = None,
+        use_rope: Optional[bool] = False,
+        window_size: Optional[bool] = -1,
+        vision_or_text: str = "vision",
+    ) -> BaseModelOutput:
+        device = inputs_embeds.device
+        hidden_states = inputs_embeds
+        if use_rope is True:
+            flatten_image_grid_thw = self.flatten_list(image_grid_thw)
+
+            if width_position_ids is None or height_position_ids is None:
+                split_hids = list()
+                split_wids = list()
+                for t, h, w in flatten_image_grid_thw:
+                    image_pids = torch.arange(t * h * w,
+                                              device=device) % (h * w)
+                    sample_hids = image_pids // w
+                    sample_wids = image_pids % w
+                    split_hids.append(sample_hids)
+                    split_wids.append(sample_wids)
+                width_position_ids = torch.concat(split_wids, dim=0)
+                height_position_ids = torch.concat(split_hids, dim=0)
+
+            pids = torch.stack(
+                [height_position_ids, width_position_ids],
+                dim=-1,
+            )
+            max_grid_size = pids.max() + 1
+            rope_emb_max_grid = self.rotary_pos_emb(max_grid_size)
+            rope_emb = rope_emb_max_grid[pids].flatten(1)
+            rope_emb = rope_emb.repeat(1, 2)
+            rope_emb = (rope_emb.cos(), rope_emb.sin())
+        else:
+            rope_emb = None
+
+        attn_cu_seqlens = cu_seqlens
+        hidden_states = inputs_embeds
+        assert attention_mask is None
+
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(
+                hidden_states,
+                attention_mask,
+                output_attentions=output_attentions,
+                cu_seqlens=attn_cu_seqlens,
+                rope_emb=rope_emb,
+            )
+        return hidden_states
+
+
+class KeyeSiglipVisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        embed_dim = config.hidden_size
+
+        self.embeddings = KeyeVisionEmbeddings(config)
+        self.encoder = KeyeSiglipEncoder(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.encoder",
+        )
+        self.post_layernorm = nn.LayerNorm(embed_dim,
+                                           eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        pixel_values,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        interpolate_pos_encoding: Optional[bool] = False,
+        attention_mask: Optional[torch.Tensor] = None,
+        sample_indices: Optional[torch.Tensor] = None,
+        image_indices: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        height_position_ids: Optional[torch.Tensor] = None,
+        width_position_ids: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[list[torch.Tensor]] = None,
+        padding_mask: Optional[torch.Tensor] = None,
+        vision_return_embed_list: Optional[bool] = False,
+        image_grid_thw: Optional[list[Union[
+            tuple[int, int, int],
+            list[tuple[int, int, int]],
+        ]]] = None,
+        return_pooler_output: Optional[bool] = True,
+        use_rope: Optional[bool] = False,
+        window_size: Optional[bool] = -1,
+    ) -> BaseModelOutputWithPooling:
+
+        hidden_states = self.embeddings(
+            pixel_values,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            position_ids=position_ids,
+            image_grid_thw=image_grid_thw,
+        )
+
+        last_hidden_state = self.encoder(
+            inputs_embeds=hidden_states,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            attention_mask=attention_mask,
+            cu_seqlens=cu_seqlens,
+            image_grid_thw=image_grid_thw,
+            use_rope=use_rope,
+            height_position_ids=height_position_ids,
+            width_position_ids=width_position_ids,
+            window_size=window_size,
+            vision_or_text="vision",
+        )
+
+        last_hidden_state = self.post_layernorm(last_hidden_state)
+
+        sample_hidden_state = list()
+        if cu_seqlens is None:
+            raise ValueError("cu_seqlens cannot be None for "
+                             "SiglipVisionTransformer output processing.")
+        for i in range(cu_seqlens.shape[0] - 1):
+            start = cu_seqlens[i]
+            end = cu_seqlens[i + 1]
+            tensor = last_hidden_state[:, start:end, :].squeeze(0)
+            sample_hidden_state.append(tensor)
+
+        return sample_hidden_state
+
+
+class KeyeSiglipVisionModel(nn.Module):
+    config_class = PretrainedConfig
+    main_input_name = "pixel_values"
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.vision_model = KeyeSiglipVisionTransformer(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.vision_model",
+        )
+        self.quant_config = quant_config
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.vision_model.embeddings.patch_embedding.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.vision_model.embeddings.patch_embedding.weight.device
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.vision_model.embeddings.patch_embedding
+
+    def forward(
+        self,
+        pixel_values,
+        sample_indices: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        interpolate_pos_encoding: bool = False,
+        position_ids: Optional[torch.Tensor] = None,
+        vision_return_embed_list: Optional[bool] = False,
+        image_grid_thw: Optional[list[Union[
+            tuple[int, int, int],
+            list[tuple[int, int, int]],
+        ]]] = None,
+        cu_seqlens: Optional[list[torch.Tensor]] = None,
+        return_pooler_output: Optional[bool] = True,
+        use_rope: Optional[bool] = False,
+        window_size: Optional[bool] = -1,
+    ) -> BaseModelOutputWithPooling:
+
+        return self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            position_ids=position_ids,
+            vision_return_embed_list=vision_return_embed_list,
+            image_grid_thw=image_grid_thw,
+            sample_indices=sample_indices,
+            cu_seqlens=cu_seqlens,
+            return_pooler_output=return_pooler_output,
+            use_rope=use_rope,
+            window_size=window_size,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if "head.attention" in name or "head.layernorm" in name:
+                continue
+            if "head.mlp" in name or "head.probe" in name:
+                continue
+            if self.quant_config is not None and (
+                    scale_name := self.quant_config.get_cache_scale(name)):
+                param = params_dict[scale_name]
+                weight_loader = getattr(
+                    param,
+                    "weight_loader",
+                    default_weight_loader,
+                )
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            for (
+                    param_name,
+                    weight_name,
+                    shard_id,
+            ) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(
+                    param,
+                    "weight_loader",
+                    default_weight_loader,
+                )
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class Projector(nn.Module):
+
+    def __init__(
+        self,
+        text_config: PretrainedConfig,
+        vision_config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.merge_kernel_size = (2, 2)
+
+        self.hidden_size = (self.vision_config.hidden_size *
+                            self.merge_kernel_size[0] *
+                            self.merge_kernel_size[1])
+
+        self.pre_norm = torch.nn.LayerNorm(self.vision_config.hidden_size,
+                                           eps=1e-05)
+        self.act = GELUActivation()
+
+        self.linear_1 = ColumnParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.linear_1",
+        )
+        self.linear_2 = RowParallelLinear(
+            self.hidden_size,
+            self.text_config.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.linear_2",
+        )
+
+    def forward(
+        self,
+        image_features: torch.Tensor,
+        image_grid_thw: list[tuple[int, int, int]],
+    ) -> torch.Tensor:
+        m1, m2 = self.merge_kernel_size
+        if isinstance(image_features, (list, tuple)):
+            processed_features = list()
+            for image_feature, image_grid in zip(image_features,
+                                                 image_grid_thw):
+                image_feature = self.pre_norm(image_feature)
+                t, h, w = image_grid
+
+                image_feature = rearrange(
+                    image_feature,
+                    "(t h p1 w p2) d -> (t h w) (p1 p2 d)",
+                    t=t,
+                    h=h // m1,
+                    p1=m1,
+                    w=w // m2,
+                    p2=m2,
+                )
+                hidden_states, _ = self.linear_1(image_feature)
+                hidden_states = self.act(hidden_states)
+                hidden_states, _ = self.linear_2(hidden_states)
+                processed_features.append(hidden_states)
+
+            return processed_features
+
+        dims = image_features.shape[:-1]
+        dim = image_features.shape[-1]
+        image_features = image_features.view(np.prod(dims), dim)
+        hidden_states = self.pre_norm(image_features).view(
+            -1, self.hidden_size)
+        hidden_states = self.linear_1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+
+        return hidden_states.view(*dims, -1)
+
+
+def _keye_field_config(hf_inputs: Mapping[str, torch.Tensor], ):
+    image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
+    image_grid_sizes = image_grid_thw.prod(-1)
+
+    video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
+    video_grid_sizes = video_grid_thw.prod(-1)
+
+    return dict(
+        pixel_values=MultiModalFieldConfig.flat_from_sizes(
+            "image", image_grid_sizes),
+        image_embeds=MultiModalFieldConfig.flat_from_sizes(
+            "image", image_grid_sizes),
+        image_grid_thw=MultiModalFieldConfig.batched("image"),
+        pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
+            "video", video_grid_sizes),
+        video_embeds=MultiModalFieldConfig.flat_from_sizes(
+            "video", video_grid_sizes),
+        video_grid_thw=MultiModalFieldConfig.batched("video"),
+    )
+
+
+class KeyeMultiModalDataParser(MultiModalDataParser):
+
+    def _parse_image_data(
+        self,
+        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
+    ) -> ModalityDataItems[Any, Any]:
+        if isinstance(data, dict):
+            return DictEmbeddingItems(
+                data,
+                modality="image",
+                required_fields={
+                    "image_embeds",
+                    "image_grid_thw",
+                },
+                fields_factory=_keye_field_config,
+            )
+
+        return super()._parse_image_data(data)
+
+    def _parse_video_data(
+        self,
+        data: Union[dict[str, torch.Tensor], ModalityData[VideoItem]],
+    ) -> ModalityDataItems[Any, Any]:
+        if isinstance(data, dict):
+            return DictEmbeddingItems(
+                data,
+                modality="video",
+                required_fields={
+                    "video_embeds",
+                    "video_grid_thw",
+                },
+                fields_factory=_keye_field_config,
+            )
+
+        return super()._parse_video_data(data)
+
+
+class KeyeProcessingInfo(BaseProcessingInfo):
+
+    def get_image_processor(self, **kwargs: object):
+        return self.get_hf_processor(**kwargs).image_processor
+
+    def get_supported_mm_limits(self, ) -> Mapping[str, Optional[int]]:
+        return {"image": None, "video": None}
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int]:
+        return {
+            "image": self.get_max_image_tokens(),
+            "video": self.get_max_video_tokens(seq_len),
+        }
+
+    def _get_vision_info(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int = 1,
+        do_resize: bool = True,
+        image_processor,
+    ) -> tuple[ImageSize, int]:
+        if image_processor is None:
+            image_processor = self.get_image_processor()
+
+        hf_config = self.get_hf_config()
+        vision_config = hf_config.vision_config
+        patch_size = vision_config.patch_size
+        merge_size = vision_config.spatial_merge_size
+        temporal_patch_size = 1
+
+        if do_resize:
+            resized_height, resized_width = smart_resize(
+                height=image_height,
+                width=image_width,
+                factor=patch_size * merge_size,
+                min_pixels=image_processor.min_pixels,
+                max_pixels=image_processor.max_pixels,
+            )
+            preprocessed_size = ImageSize(width=resized_width,
+                                          height=resized_height)
+        else:
+            preprocessed_size = ImageSize(width=image_width,
+                                          height=image_height)
+
+        padded_num_frames = num_frames + num_frames % temporal_patch_size
+
+        grid_t = max(padded_num_frames // temporal_patch_size, 1)
+        grid_h = preprocessed_size.height // patch_size
+        grid_w = preprocessed_size.width // patch_size
+
+        num_patches = grid_t * grid_h * grid_w
+        num_vision_tokens = num_patches // (merge_size**2)
+
+        return preprocessed_size, num_vision_tokens
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        image_processor,
+    ) -> int:
+        _, num_image_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            image_processor=image_processor,
+        )
+        return num_image_tokens
+
+    def get_num_video_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int,
+        image_processor,
+    ) -> int:
+        _, num_video_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            num_frames=num_frames,
+            image_processor=image_processor,
+        )
+        return num_video_tokens
+
+    def get_image_size_with_most_features(self, ) -> ImageSize:
+        max_image_size, _ = self._get_vision_info(
+            image_width=_MAX_IMAGE_SIZE,
+            image_height=_MAX_IMAGE_SIZE,
+            image_processor=None,
+        )
+        return max_image_size
+
+    def get_max_image_tokens(self) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_image_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            image_processor=None,
+        )
+
+    def _get_max_video_frames(self, max_tokens: int) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        num_frames = 0
+
+        while True:
+            next_num_frames = num_frames + 1
+            next_max_tokens = self.get_num_video_tokens(
+                image_width=target_width,
+                image_height=target_height,
+                num_frames=next_num_frames,
+                image_processor=None,
+            )
+
+            if next_max_tokens > max_tokens:
+                break
+
+            num_frames = next_num_frames
+
+        return num_frames
+
+    def get_num_frames_with_most_features(self, seq_len: int) -> int:
+        mm_config = self.ctx.get_mm_config()
+        max_images = mm_config.get_limit_per_prompt("image")
+        max_videos = mm_config.get_limit_per_prompt("video")
+
+        max_image_tokens = self.get_max_image_tokens() * max_images
+        max_total_frames = self._get_max_video_frames(seq_len -
+                                                      max_image_tokens)
+        max_frames_per_video = min(
+            max_total_frames // max(max_videos, 1),
+            _MAX_FRAMES_PER_VIDEO,
+        )
+
+        return max(max_frames_per_video, 1)
+
+    def get_max_video_tokens(self, seq_len: int) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_video_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            num_frames=self.get_num_frames_with_most_features(seq_len),
+            image_processor=None,
+        )
+
+
+class KeyeDummyInputsBuilder(BaseDummyInputsBuilder[KeyeProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        hf_processor = self.info.get_hf_processor()
+        image_token: str = hf_processor.image_token
+        video_token: str = hf_processor.video_token
+
+        return image_token * num_images + video_token * num_videos
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        target_width, target_height = (
+            self.info.get_image_size_with_most_features())
+        target_num_frames = self.info.get_num_frames_with_most_features(
+            seq_len)
+
+        mm_data = {
+            "image":
+            self._get_dummy_images(
+                width=target_width,
+                height=target_height,
+                num_images=num_images,
+            ),
+            "video":
+            self._get_dummy_videos(
+                width=target_width,
+                height=target_height,
+                num_frames=target_num_frames,
+                num_videos=num_videos,
+            ),
+        }
+
+        return mm_data
+
+
+class KeyeMultiModalProcessor(BaseMultiModalProcessor[KeyeProcessingInfo]):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        return KeyeMultiModalDataParser()
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_processor = self.info.get_image_processor(
+            **hf_processor_mm_kwargs)
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+
+        placeholder = {
+            "image": vocab[hf_processor.image_token],
+            "video": vocab[hf_processor.video_token],
+        }
+
+        merge_length = image_processor.merge_size**2
+
+        def get_replacement_keye(item_idx: int, modality: str):
+            grid_thw = out_mm_kwargs[f"{modality}_grid_thw"][item_idx]
+            assert isinstance(grid_thw, torch.Tensor)
+
+            num_tokens = int(grid_thw.prod()) // merge_length
+            return [placeholder[modality]] * num_tokens
+
+        return [
+            PromptReplacement(
+                modality=modality,
+                target=[placeholder[modality]],
+                replacement=partial(get_replacement_keye, modality=modality),
+            ) for modality in ("image", "video")
+        ]
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return _keye_field_config(hf_inputs)
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    KeyeMultiModalProcessor,
+    info=KeyeProcessingInfo,
+    dummy_inputs=KeyeDummyInputsBuilder,
+)
+class KeyeForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA,
+                                   SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
+        "lm_head.": "language_model.lm_head.",
+        "model.": "language_model.model.",
+    })
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<|vision_start|><|image_pad|><|vision_end|>"
+        if modality.startswith("video"):
+            return "<|vision_start|><|video_pad|><|vision_end|>"
+
+        raise ValueError("Only image or video modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config: PretrainedConfig = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.visual = KeyeSiglipVisionModel(
+            config.vision_config,
+            quant_config=self._maybe_ignore_quant_config(quant_config),
+            prefix=maybe_prefix(prefix, "visual"),
+        )
+        self.mlp_AR = Projector(
+            config,
+            config.vision_config,
+            quant_config=self._maybe_ignore_quant_config(quant_config),
+            prefix=maybe_prefix(prefix, "mlp_AR"),
+        )
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=["Qwen3ForCausalLM"],
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
+            return None
+        return quant_config
+
+    def _validate_and_reshape_mm_tensor(self, mm_input: NestedTensors,
+                                        name: str) -> torch.Tensor:
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of {name}. "
+                             f"Got type: {type(mm_input)}")
+        if isinstance(mm_input, torch.Tensor):
+            if mm_input.ndim == 2:
+                return mm_input
+            if mm_input.ndim == 5:
+                return mm_input
+            if mm_input.ndim != 3:
+                raise ValueError(f"{name} should be 2D or batched 3D tensor. "
+                                 f"Got ndim: {mm_input.ndim} "
+                                 f"(shape={mm_input.shape})")
+            return torch.concat(list(mm_input))
+        elif is_list_of(mm_input, torch.Tensor):
+            if all(p.dim() == 4 for p in mm_input) or all(p.dim() == 2
+                                                          for p in mm_input):
+                return mm_input
+        return torch.concat(list(mm_input))
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[KeyeImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            pixel_values = self._validate_and_reshape_mm_tensor(
+                pixel_values, "image pixel values")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            return KeyeImagePixelInputs(
+                type="pixel_values",
+                pixel_values=pixel_values,
+                image_grid_thw=image_grid_thw,
+            )
+
+        if image_embeds is not None:
+            image_embeds = self._validate_and_reshape_mm_tensor(
+                image_embeds, "image embeds")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            return KeyeImageEmbeddingInputs(
+                type="image_embeds",
+                image_embeds=image_embeds,
+                image_grid_thw=image_grid_thw,
+            )
+
+    def _parse_and_validate_video_input(
+            self, **kwargs: object) -> Optional[KeyeVideoInputs]:
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
+        video_embeds = kwargs.pop("video_embeds", None)
+        video_grid_thw = kwargs.pop("video_grid_thw", None)
+
+        if pixel_values_videos is None and video_embeds is None:
+            return None
+
+        if pixel_values_videos is not None:
+            pixel_values_videos = self._validate_and_reshape_mm_tensor(
+                pixel_values_videos,
+                "video pixel values",
+            )
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            return KeyeVideoPixelInputs(
+                type="pixel_values_videos",
+                pixel_values_videos=pixel_values_videos,
+                video_grid_thw=video_grid_thw,
+            )
+
+        if video_embeds is not None:
+            video_embeds = self._validate_and_reshape_mm_tensor(
+                video_embeds, "video embeds")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            return KeyeVideoEmbeddingInputs(
+                type="video_embeds",
+                video_embeds=video_embeds,
+                video_grid_thw=video_grid_thw,
+            )
+
+    def _process_image_input(
+            self, image_input: KeyeImageInputs) -> tuple[torch.Tensor, ...]:
+        siglip_position_ids = list()
+        image_grid_hws = list()
+        sample_indices = list()
+        cu_seqlens = [0]
+
+        image_grid_thw = image_input["image_grid_thw"]
+        assert image_grid_thw.ndim == 2
+
+        for idx, thaw in enumerate(image_grid_thw):
+            thw_tuple = tuple(thaw.detach().cpu().numpy().tolist())
+            numel = np.prod(thw_tuple)
+            image_grid_hws.append(thw_tuple)
+            image_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:])
+            siglip_position_ids.append(image_position_ids)
+            sample_indices.append(torch.full((numel, ), idx,
+                                             dtype=torch.int64))
+            cu_seqlens.append(cu_seqlens[-1] + numel)
+
+        if image_input["type"] == "image_embeds":
+            raise ValueError(
+                "Image embeddings are not supported for this processing path.")
+        else:
+            pixel_values = image_input["pixel_values"].type(self.visual.dtype)
+            siglip_position_ids = torch.concat(siglip_position_ids,
+                                               dim=0).to(pixel_values.device)
+            cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to(
+                pixel_values.device)
+            sample_indices = torch.concat(sample_indices,
+                                          dim=0).to(pixel_values.device)
+
+            image_embeds = self.visual(
+                pixel_values=pixel_values,
+                image_grid_thw=image_grid_hws,
+                position_ids=siglip_position_ids,
+                vision_return_embed_list=False,
+                interpolate_pos_encoding=True,
+                sample_indices=sample_indices,
+                cu_seqlens=cu_seqlens,
+                use_rope=True,
+                window_size=-1,
+            )
+            image_embeds = tuple(self.mlp_AR(image_embeds, image_grid_thw))
+            return image_embeds
+
+    def _process_video_input(
+            self, video_input: KeyeVideoInputs) -> tuple[torch.Tensor, ...]:
+        siglip_position_ids = list()
+        video_grid_hws = list()
+        sample_indices = list()
+        cu_seqlens = [0]
+
+        video_grid_thw = video_input["video_grid_thw"]
+        assert video_grid_thw.ndim == 2
+
+        for idx, thaw in enumerate(video_grid_thw):
+            thw_tuple = tuple(thaw.detach().cpu().numpy().tolist())
+            numel = np.prod(thw_tuple)
+
+            video_grid_hws.append(thw_tuple)
+            video_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:])
+            siglip_position_ids.append(video_position_ids)
+            sample_indices.append(torch.full((numel, ), idx,
+                                             dtype=torch.int64))
+            cu_seqlens.append(cu_seqlens[-1] + numel)
+
+        if video_input["type"] == "video_embeds":
+            raise ValueError(
+                "Video embeddings are not supported for this processing path.")
+        else:
+            pixel_values_videos = video_input["pixel_values_videos"].type(
+                self.visual.dtype)
+            siglip_position_ids = torch.concat(siglip_position_ids, dim=0).to(
+                pixel_values_videos.device)
+            cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to(
+                pixel_values_videos.device)
+            sample_indices = torch.concat(sample_indices,
+                                          dim=0).to(pixel_values_videos.device)
+
+            video_embeds = self.visual(
+                pixel_values=pixel_values_videos,
+                image_grid_thw=video_grid_hws,
+                position_ids=siglip_position_ids,
+                vision_return_embed_list=True,
+                interpolate_pos_encoding=True,
+                sample_indices=sample_indices,
+                cu_seqlens=cu_seqlens,
+                use_rope=True,
+                window_size=-1,
+            )
+            video_embeds = tuple(self.mlp_AR(video_embeds, video_grid_thw))
+            return video_embeds
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        modalities = {}
+
+        for input_key in kwargs:
+            if (input_key in ("pixel_values", "image_embeds")
+                    and "images" not in modalities):
+                modalities["images"] = self._parse_and_validate_image_input(
+                    **kwargs)
+            if (input_key in ("pixel_values_videos", "video_embeds")
+                    and "videos" not in modalities):
+                modalities["videos"] = self._parse_and_validate_video_input(
+                    **kwargs)
+
+        return modalities
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+
+        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not modalities:
+            return None
+
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        for modality in modalities:
+            if modality == "images":
+                image_input = modalities["images"]
+                vision_embeddings = self._process_image_input(image_input)
+                multimodal_embeddings += vision_embeddings
+            if modality == "videos":
+                video_input = modalities["videos"]
+                video_embeddings = self._process_video_input(video_input)
+                multimodal_embeddings += video_embeddings
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                [
+                    self.config.image_token_id,
+                    self.config.video_token_id,
+                ],
+            )
+        return inputs_embeds
+
+    def get_input_embeddings_v0(
+        self,
+        input_ids: torch.Tensor,
+        image_input: Optional[KeyeImagePixelInputs] = None,
+        video_input: Optional[KeyeVideoPixelInputs] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.get_input_embeddings(input_ids)
+        if image_input is not None:
+            image_embeds = self._process_image_input(image_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                image_embeds,
+                placeholder_token_id=self.config.image_token_id,
+            )
+
+        if video_input is not None:
+            video_embeds = self._process_video_input(video_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                video_embeds,
+                placeholder_token_id=self.config.video_token_id,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        """Run forward pass for Qwen2-VL.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            positions: Flattened (concatenated) position ids corresponding to a
+                batch.
+                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
+                opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+            pixel_values: Pixel values to be fed to a model.
+                `None` if no images are passed.
+            image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
+                `None` if no images are passed.
+            pixel_values_videos: Pixel values of videos to be fed to a model.
+                `None` if no videos are passed.
+            video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
+                `None` if no videos are passed.
+        """
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        elif inputs_embeds is None:
+            image_input = self._parse_and_validate_image_input(**kwargs)
+            video_input = self._parse_and_validate_video_input(**kwargs)
+
+            if image_input is None and video_input is None:
+                inputs_embeds = None
+            else:
+                if uses_mrope(self.config):
+                    assert positions.ndim == 2 and positions.size(0) == 3, (
+                        "multimodal section rotary embedding requires "
+                        f"(3, seq_len) positions, but got {positions.size()}")
+                inputs_embeds = self.get_input_embeddings_v0(
+                    input_ids,
+                    image_input=image_input,
+                    video_input=video_input,
+                )
+                input_ids = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """Get the module prefix in multimodal models."""
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="visual.",
+            tower_model="mlp_AR.",
+        )
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/llama.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/llama.py
new file mode 100644
index 0000000000000000000000000000000000000000..24cd448d8361f4bdd183d45e10f7b8c2eb8a4960
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/llama.py
@@ -0,0 +1,640 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only LLaMA model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from transformers import LlamaConfig
+
+from vllm.attention import Attention, AttentionType
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
+                    is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class LlamaMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+        reduce_results: bool = True,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=hidden_size,
+            output_sizes=[intermediate_size] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            input_size=intermediate_size,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        x, _ = self.gate_up_proj(x)
+        x = self.act_fn(x)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class LlamaAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        bias_o_proj: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "",
+        attn_type: str = AttentionType.DECODER,
+    ) -> None:
+        super().__init__()
+        layer_idx = extract_layer_index(prefix)
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
+        head_dim = getattr(config, "head_dim", None)
+        if head_dim is None:
+            head_dim = self.hidden_size // self.total_num_heads
+        self.head_dim = head_dim
+        # Phi models introduced a partial_rotary_factor parameter in the config
+        self.partial_rotary_factor = getattr(config, "partial_rotary_factor",
+                                             1)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.o_proj = RowParallelLinear(
+            input_size=self.total_num_heads * self.head_dim,
+            output_size=hidden_size,
+            bias=bias_o_proj,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self._init_rotary_emb(config,
+                              rope_scaling=rope_scaling,
+                              quant_config=quant_config)
+
+        sliding_window = None
+        if layer_types := getattr(config, "layer_types", None):
+            is_sliding = layer_types[layer_idx] == "sliding_attention"
+            if is_sliding:
+                sliding_window = config.sliding_window
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            per_layer_sliding_window=sliding_window,
+            attn_type=attn_type,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+    def _init_rotary_emb(self, config: LlamaConfig,
+                         rope_scaling: Optional[dict[str, Any]],
+                         quant_config: Optional[QuantizationConfig]) -> None:
+        is_neox_style = True
+        is_gguf = quant_config and quant_config.get_name() == "gguf"
+        if is_gguf and config.model_type == "llama":
+            is_neox_style = False
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=self.rope_theta,
+            rope_scaling=rope_scaling,
+            is_neox_style=is_neox_style,
+            partial_rotary_factor=self.partial_rotary_factor,
+        )
+
+
+class LlamaDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        if rope_scaling is not None and getattr(
+                config, "original_max_position_embeddings", None):
+            rope_scaling["original_max_position_embeddings"] = (
+                config.original_max_position_embeddings)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        # Support abacusai/Smaug-72B-v0.1 with attention_bias
+        # Support internlm/internlm-7b with bias
+        attention_bias = getattr(config, "attention_bias", False) or getattr(
+            config, "bias", False)
+        bias_o_proj = attention_bias
+        # support internlm/internlm3-8b with qkv_bias
+        if hasattr(config, 'qkv_bias'):
+            attention_bias = config.qkv_bias
+
+        # By default, Llama uses causal attention as it is a decoder-only model.
+        # You can override the HF config with `is_causal=False` to enable
+        # bidirectional attention, which is used in some embedding models
+        # (e.g. parasail-ai/GritLM-7B-vllm)
+        if getattr(config, "is_causal", True):
+            attn_type = AttentionType.DECODER
+        else:
+            attn_type = AttentionType.ENCODER_ONLY
+
+        self.self_attn = LlamaAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=getattr(config, "num_key_value_heads",
+                                 config.num_attention_heads),
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=attention_bias,
+            bias_o_proj=bias_o_proj,
+            cache_config=cache_config,
+            prefix=f"{prefix}.self_attn",
+            attn_type=attn_type,
+        )
+        self.mlp = LlamaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            bias=getattr(config, "mlp_bias", False),
+            prefix=f"{prefix}.mlp",
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(positions=positions,
+                                       hidden_states=hidden_states)
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class LlamaModel(nn.Module):
+
+    def __init__(self,
+                 *,
+                 vllm_config: VllmConfig,
+                 prefix: str = "",
+                 layer_type: type[nn.Module] = LlamaDecoderLayer):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.quant_config = quant_config
+        lora_vocab = (lora_config.lora_extra_vocab_size *
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+        if get_pp_group().is_first_rank or (config.tie_word_embeddings
+                                            and get_pp_group().is_last_rank):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                quant_config=quant_config,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: layer_type(config=config,
+                                      cache_config=cache_config,
+                                      quant_config=quant_config,
+                                      prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        self.aux_hidden_state_layers: tuple[int] = tuple()
+
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors, tuple[torch.Tensor,
+                                                        list[torch.Tensor]]]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        aux_hidden_states = []
+        for idx, layer in enumerate(
+                self.layers[self.start_layer:self.end_layer]):
+            if idx in self.aux_hidden_state_layers:
+                aux_hidden_states.append(hidden_states + residual)
+            hidden_states, residual = layer(positions, hidden_states, residual)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+
+        if len(aux_hidden_states) > 0:
+            return hidden_states, aux_hidden_states
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            if "scale" in name:
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"]
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings"
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    # Mistral/Llama models can also be loaded with --load-format mistral
+    # from consolidated.safetensors checkpoints
+    mistral_mapping = {
+        "layers": "model.layers",
+        "attention": "self_attn",
+        "qscale_act": "input_scale",
+        "qscale_weight": "weight_scale",
+        "kv_fake_quantizer.qscale_act": "kv_scale",
+        "q_fake_quantizer.qscale_act": "attn.q_scale",
+        "k_fake_quantizer.qscale_act": "k_scale",
+        "v_fake_quantizer.qscale_act": "v_scale",
+        "wq": "q_proj",
+        "wk": "k_proj",
+        "wv": "v_proj",
+        "wo": "o_proj",
+        "attention_norm": "input_layernorm",
+        "feed_forward": "mlp",
+        "w1": "gate_proj",
+        "w2": "down_proj",
+        "w3": "up_proj",
+        "ffn_norm": "post_attention_layernorm",
+        "tok_embeddings": "model.embed_tokens",
+        "output": "lm_head",
+        "norm": "model.norm",
+    }
+
+    def __init__(self,
+                 *,
+                 vllm_config: VllmConfig,
+                 prefix: str = "",
+                 layer_type: type[nn.Module] = LlamaDecoderLayer):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.lora_config = lora_config
+
+        self.model = self._init_model(vllm_config=vllm_config,
+                                      prefix=maybe_prefix(prefix, "model"),
+                                      layer_type=layer_type)
+
+        if get_pp_group().is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            if lora_config:
+                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=(
+                    DEFAULT_VOCAB_PADDING_SIZE
+                    # We need bigger padding if using lora for kernel
+                    # compatibility
+                    if not lora_config else
+                    lora_config.lora_vocab_padding_size),
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+            if config.tie_word_embeddings:
+                self.lm_head = self.lm_head.tie_weights(
+                    self.model.embed_tokens)
+
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    config.vocab_size,
+                                                    logit_scale)
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def set_aux_hidden_state_layers(self, layers: tuple[int]) -> None:
+        self.model.aux_hidden_state_layers = layers
+
+    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int]:
+        num_layers = len(self.model.layers)
+        return (2, num_layers // 2, num_layers - 3)
+
+    def _init_model(self,
+                    vllm_config: VllmConfig,
+                    prefix: str = "",
+                    layer_type: type[nn.Module] = LlamaDecoderLayer):
+        return LlamaModel(vllm_config=vllm_config,
+                          prefix=prefix,
+                          layer_type=layer_type)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        model_output = self.model(input_ids, positions, intermediate_tensors,
+                                  inputs_embeds)
+        return model_output
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(
+            self.maybe_remap_mistral(name, loaded_weight)
+            for name, loaded_weight in weights)
+
+    # This function is used to remap the mistral format as
+    # used by Mistral and Llama <=2
+    def maybe_remap_mistral(
+        self,
+        name: str,
+        loaded_weight: torch.Tensor,
+    ) -> tuple[str, torch.Tensor]:
+
+        def permute(w: torch.Tensor, n_heads: int):
+            attn_in = self.config.head_dim * n_heads
+            attn_out = self.config.hidden_size
+
+            return w.view(n_heads, attn_in // n_heads // 2, 2,
+                          attn_out).transpose(1, 2).reshape(attn_in, attn_out)
+
+        mapping = self.mistral_mapping
+        modules = name.split(".")
+
+        # rotary embeds should be sliced
+        if "wk" in modules and modules[-1] == "weight":
+            loaded_weight = permute(loaded_weight,
+                                    self.config.num_key_value_heads)
+        elif "wq" in modules and modules[-1] == "weight":
+            loaded_weight = permute(loaded_weight,
+                                    self.config.num_attention_heads)
+
+        num_modules = len(modules)
+        for i in range(num_modules):
+            item = modules[i]
+            next_item = modules[i + 1] if i < num_modules - 1 else None
+
+            combined_item = (f"{item}.{next_item}"
+                             if next_item is not None else None)
+
+            if combined_item in mapping:
+                name = name.replace(combined_item, mapping[combined_item])
+            elif item in mapping and mapping[item] not in name:
+                name = name.replace(item, mapping[item])
+
+        return name, loaded_weight
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/llama4.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/llama4.py
new file mode 100644
index 0000000000000000000000000000000000000000..308cb3e85e27bdee7ab27037629e41b9630ca63d
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/llama4.py
@@ -0,0 +1,727 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Copyright 2025 the LLAMA4, Meta Inc., vLLM, and HuggingFace Inc. team.
+# All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only LLaMA model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Any, Optional
+
+import torch
+from torch import nn
+from transformers import Llama4TextConfig
+
+from vllm.attention import Attention
+from vllm.attention.layers.chunked_local_attention import ChunkedLocalAttention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+
+from .llama import LlamaForCausalLM, LlamaMLP, LlamaModel
+from .utils import (AutoWeightsLoader, extract_layer_index, fast_topk,
+                    is_pp_missing_parameter)
+
+
+class Llama4MoE(nn.Module):
+
+    @staticmethod
+    def custom_routing_function(
+        hidden_states: torch.Tensor,
+        gating_output: torch.Tensor,
+        topk: int,
+        renormalize: bool,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        router_scores, router_indices = fast_topk(gating_output, topk, dim=-1)
+        # pseudo-standard is that the router scores are floats
+        router_scores = torch.sigmoid(router_scores.float())
+        return (router_scores, router_indices.to(torch.int32))
+
+    def __init__(self,
+                 config: Llama4TextConfig,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.top_k = config.num_experts_per_tok
+
+        intermediate_size_moe = config.intermediate_size
+        self.router = ReplicatedLinear(config.hidden_size,
+                                       config.num_local_experts,
+                                       bias=False,
+                                       quant_config=None,
+                                       prefix=f"{prefix}.router")
+
+        self.experts = FusedMoE(
+            num_experts=config.num_local_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            custom_routing_function=Llama4MoE.custom_routing_function,
+            intermediate_size=intermediate_size_moe,
+            apply_router_weight_on_input=True,
+            reduce_results=False,
+            renormalize=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.experts")
+
+        self.shared_expert = LlamaMLP(
+            hidden_size=config.hidden_size,
+            intermediate_size=intermediate_size_moe,
+            hidden_act="silu",
+            quant_config=quant_config,
+            bias=False,
+            prefix=f"{prefix}.shared_expert",
+            reduce_results=self.experts.must_reduce_shared_expert_outputs(),
+        )
+
+    def forward(self, hidden_states):
+        router_logits, _ = self.router(hidden_states)
+        shared_out = self.shared_expert(hidden_states)
+        routed_out = self.experts(
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+        )
+        experts_out = routed_out + shared_out
+
+        if self.tp_size > 1:
+            experts_out = self.experts.maybe_all_reduce_tensor_model_parallel(
+                experts_out)
+
+        return experts_out
+
+
+class Llama4Attention(nn.Module):
+
+    def __init__(self,
+                 config: Llama4TextConfig,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 rope_theta: float = 10000,
+                 rope_scaling: Optional[dict[str, Any]] = None,
+                 max_position_embeddings: int = 8192,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 bias: bool = False,
+                 bias_o_proj: bool = False,
+                 cache_config: Optional[CacheConfig] = None,
+                 prefix: str = "") -> None:
+        super().__init__()
+        self.layer_idx = extract_layer_index(prefix)
+        self.hidden_size = hidden_size
+        self.no_rope_layers = config.no_rope_layers
+        self.nope = self.no_rope_layers[self.layer_idx] == 0
+        self.use_qk_norm = config.use_qk_norm and not self.nope
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = config.head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.attn_temperature_tuning = self.nope and \
+            config.attn_temperature_tuning
+
+        self.floor_scale = getattr(config, "floor_scale", 8192.0)
+        self.attn_scale = getattr(config, "attn_scale", 0.1)
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+        self.n_rep = self.num_heads // self.num_kv_heads
+        self.qk_norm = RMSNorm(
+            hidden_size=self.head_dim,
+            eps=config.rms_norm_eps,
+            has_weight=False,
+            dtype=torch.float32,
+        ) if self.use_qk_norm else None
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.o_proj = RowParallelLinear(
+            input_size=self.total_num_heads * self.head_dim,
+            output_size=hidden_size,
+            bias=bias_o_proj,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        is_neox_style = True
+        is_gguf = quant_config and quant_config.get_name() == "gguf"
+        if is_gguf and config.model_type == "llama":
+            is_neox_style = False
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=int(rope_theta),
+            rope_scaling=rope_scaling if rope_scaling != "default" else None,
+            is_neox_style=is_neox_style,
+        ) if not self.nope else None
+
+        attn_cls = Attention if self.nope else ChunkedLocalAttention
+        self.attn = attn_cls(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+            **({
+                "attention_chunk_size": config.attention_chunk_size
+            } if not self.nope else {}))
+
+    def _get_attn_scale(self, positions: torch.Tensor) -> torch.Tensor:
+        floor = torch.floor((positions + 1.0) / self.floor_scale)
+        attn_scale = torch.log(floor + 1.0) * self.attn_scale + 1.0
+
+        return attn_scale.unsqueeze(-1)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        if self.rotary_emb is not None:
+            q, k = self.rotary_emb(positions, q, k)
+
+        if self.qk_norm is not None:
+            # Normalization is applied on the head_dim dimension. The rest of
+            # the dimensions are collapsed into a single dimension to support
+            # custom rms_norm cuda kernel.
+            q = q.reshape(-1, self.head_dim)
+            q = self.qk_norm(q.float()).reshape(-1, self.q_size).to(q.dtype)
+            k = k.reshape(-1, self.head_dim)
+            k = self.qk_norm(k.float()).reshape(-1, self.kv_size).to(k.dtype)
+
+        # We are applying temperature tuning (https://arxiv.org/abs/2501.19399)
+        # to NoPE layers, where the inference-time temperature tuning function
+        # is customized to not affect short context
+        # while working at very long context
+        # https://arxiv.org/abs/2501.19399
+        #
+        # We should apply temperature tuning between (after) rotary / QK norm
+        # and (before) attention.
+        if self.attn_temperature_tuning and self.nope:
+            attn_scale = self._get_attn_scale(positions)
+            q = (q * attn_scale).to(q.dtype)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Llama4DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Llama4TextConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.layer_idx = extract_layer_index(prefix)
+        self.global_layer = config.no_rope_layers[self.layer_idx] == 0
+        self.hidden_size = config.hidden_size
+        rope_theta = config.rope_theta
+        rope_scaling = config.rope_scaling
+        max_position_embeddings = config.max_position_embeddings
+
+        self.self_attn = Llama4Attention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=False,
+            bias_o_proj=False,
+            cache_config=cache_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        is_moe_layer = config.interleave_moe_layer_step > 0 and (
+            self.layer_idx + 1) % config.interleave_moe_layer_step == 0
+        if is_moe_layer:
+            self.feed_forward = Llama4MoE(
+                config=config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.feed_forward",
+            )
+        else:
+            self.feed_forward = LlamaMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=config.intermediate_size_mlp,
+                hidden_act="silu",
+                quant_config=quant_config,
+                bias=False,
+                prefix=f"{prefix}.feed_forward",
+            )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(positions=positions,
+                                       hidden_states=hidden_states)
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.feed_forward(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Llama4Model(LlamaModel):
+
+    def __init__(self,
+                 *,
+                 vllm_config: VllmConfig,
+                 prefix: str = "",
+                 layer_type: type[Llama4DecoderLayer] = Llama4DecoderLayer):
+        self.num_experts = vllm_config.model_config.hf_config.num_local_experts
+        super().__init__(vllm_config=vllm_config,
+                         prefix=prefix,
+                         layer_type=layer_type)
+
+    def load_moe_expert_weights(
+        self,
+        name: str,
+        loaded_weight: torch.Tensor,
+        params_dict: dict[str, nn.Parameter],
+        loaded_params: set[str],
+        expert_params_mapping: list[tuple[str, str, int, str]],
+        fused: bool = True,
+    ) -> bool:
+        """
+        Load MoE expert weights.
+
+        Args:
+            name: The name of the weight to load.
+            loaded_weight: The weight to load.
+            params_dict: The dictionary of module parameters.
+            loaded_params: The set of already loaded parameters.
+            expert_params_mapping: The mapping of expert parameters. Must be
+                generated by FusedMoE.make_expert_params_mapping().
+            fused: Whether the expert weights are fused into a single weight
+                tensor or are separate weight tensors for each expert.
+                When fused is True, loaded_weight should have shape of:
+                [num_experts, hidden_in, hidden_out] for gate/up/down proj and
+                [hidden_out, hidden_in] for the others like router.
+                When fused is False, loaded_weight should have shape of:
+                [hidden_out, hidden_in].
+
+        Returns:
+            True if loaded_weight is one of MoE weights and the MoE expert
+            weights are loaded successfully, False otherwise.
+        """
+
+        # Whether the MoE expert weights are loaded successfully.
+        expert_param_loaded = False
+
+        # If fused is True, the loaded weight is in the layout of:
+        # [num_experts, hidden_in, hidden_out], so we must transpose the last
+        # two dimensions to match the expected layout of the parameters.
+        if fused and loaded_weight.ndim == 3:
+            loaded_weight = loaded_weight.transpose(-1, -2)
+
+            # If the gate_proj and up_proj weights are fused into a single
+            # weight tensor, we need to split the weight tensor into a tuple
+            # of two weight tensors along the hidden_out dimension.
+            if "experts.gate_up_proj" in name:
+                loaded_weight = loaded_weight.chunk(2, dim=-2)
+
+        # Iterate over all the expert parameters and load the weights if we find
+        # a match in weight name.
+        for (param_name, weight_name, expert_id,
+             shard_id) in expert_params_mapping:
+
+            # Get a view of the loaded_weight to avoid modifying the original
+            # one across iterations.
+            new_loaded_weight = loaded_weight
+
+            # If expert weights are fused into a single weight tensor, remove
+            # the expert index from the expected weight name.
+            if fused:
+                # The string between e_str and proj_str is the expert index.
+                e_str, _, proj_str, _ = weight_name.split('.')
+                weight_name = f"{e_str}.{proj_str}"
+                param_name = f"{param_name}weight"
+
+            # Skip if the current weight is not one of the MoE weights.
+            if weight_name not in name:
+                continue
+
+            # Replace the weight name with the parameter name.
+            full_param_name = name.replace(weight_name, param_name)
+
+            # Skip if the current weight corresponds to a parameter that
+            # does not exist on the current PP (pipeline parallel) rank.
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            # Skip if the current weight is for the bias.
+            if ((name.endswith(".bias") or name.endswith("_bias"))
+                    and name not in params_dict):
+                continue
+
+            param = params_dict[full_param_name]
+            weight_loader = param.weight_loader
+
+            if fused:
+                # If the parameter is for w13 together, the corresponding weight
+                # will be a tuple, so we must select the correct weight
+                # depending on the shard id, which is either "w1" or "w3".
+                if "w13" in full_param_name:
+                    assert shard_id in ["w1", "w3"]
+                    shard_idx = 0 if shard_id == "w1" else 1
+                    new_loaded_weight = new_loaded_weight[shard_idx]
+
+                # If EP (expert parallel) is enabled, update expert_id to the
+                # starting expert index for the current EP rank and extract the
+                # corresponding expert weights.
+                layer_idx = extract_layer_index(name)
+                expert_map = self.layers[
+                    layer_idx].feed_forward.experts.expert_map
+                if expert_map is not None:
+                    local_expert_indices = (expert_map != -1) \
+                                            .nonzero() \
+                                            .flatten() \
+                                            .to(new_loaded_weight.device)
+                    new_loaded_weight = new_loaded_weight[local_expert_indices]
+                    expert_id = local_expert_indices[0].item()
+            else:
+                # TODO: add EP support for non fused weights
+                pass
+
+            # Load the weight into the module parameter with corresponding
+            # shard id and expert id.
+            weight_loader(param,
+                          new_loaded_weight,
+                          full_param_name,
+                          shard_id=shard_id,
+                          expert_id=expert_id)
+
+            loaded_params.add(full_param_name)
+            expert_param_loaded = True
+
+        return expert_param_loaded
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        # Name mapping from the parameter name to the shard name and
+        # corresponding shard id.
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        # Indicate whether the expert weights are fused into a single weight
+        # tensor.
+        fused_experts_params = False
+        # Expert parameter mapping for the case where the expert weights are
+        # not fused into a single weight tensor.
+        expert_params_mapping = FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.num_experts)
+        # Expert parameter mapping for the case where the expert weights are
+        # fused into a single weight tensor.
+        expert_params_mapping_fused = FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_up_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="gate_up_proj",
+            num_experts=1)
+        # All the module parameters.
+        params_dict = dict(self.named_parameters())
+        # The module parameters that have been loaded.
+        loaded_params: set[str] = set()
+
+        # Iterate over all the weights and load them into module parameters.
+        for name, loaded_weight in weights:
+
+            # If the name contains "experts.gate_up_proj" or "experts.down_proj"
+            # without the expert indices, it means the expert weights are fused
+            # into a single weight tensor across all experts.
+            if "experts.gate_up_proj" in name or "experts.down_proj" in name:
+                fused_experts_params = True
+                expert_params_mapping = expert_params_mapping_fused
+
+            # If kv cache quantization scales exist and the weight name
+            # corresponds to one of the kv cache quantization scales, load
+            # them.
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            # Iterate over stacked_params_mapping to check if the current weight
+            # is one of the stacked parameters. If so, load the weight with the
+            # corresponding shard id. Note that MoE weights are handled
+            # separately in the else block.
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                # Skip if the current weight is not one of the stacked
+                # parameters or if the current weight is a MoE weight.
+                if weight_name not in name or "experts" in name:
+                    continue
+
+                # For ModelOpt checkpoints, we need to rename the self_attn
+                # weight/weight_scale names except for kv cache scales.
+                if not (name.endswith(
+                    (".k_scale", ".v_scale")) and "self_attn" in name):
+                    name = name.replace(weight_name, param_name)
+
+                # Skip if the current weight corresponds to a parameter that
+                # does not exist on the current PP (pipeline parallel) rank.
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                # Remap kv cache scale names for ModelOpt checkpoints.
+                # TODO: ModelOpt should implement get_cache_scale() such that
+                #       kv cache scale name remapping can be done there.
+                if name.endswith("scale"):
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                # Load the weight into the module parameter with corresponding
+                # shard id and exit the for loop and the else block.
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+
+                if weight_loader == default_weight_loader:
+                    weight_loader(param, loaded_weight)
+                else:
+                    weight_loader(param, loaded_weight, shard_id)
+
+                loaded_params.add(name)
+                break
+
+            # Handle normal (non-stacked) weights and MoE weights.
+            else:
+                # First, try to load MoE weights using load_moe_expert_weights.
+                # If successful, move on to next loaded weight.
+                if self.load_moe_expert_weights(name,
+                                                loaded_weight,
+                                                params_dict,
+                                                loaded_params,
+                                                expert_params_mapping,
+                                                fused=fused_experts_params):
+                    continue
+
+                # Skip if the current weight corresponds to a parameter that
+                # does not exist on the current PP (pipeline parallel) rank.
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                # Handle flat expert scale parameters that don't match
+                # per-expert patterns, i.e. one weight scale tensor for all
+                # experts.
+                scale_names = [
+                    "w13_input_scale", "w13_weight_scale", "w2_input_scale",
+                    "w2_weight_scale"
+                ]
+                if ("experts." in name and any(scale_name in name
+                                               for scale_name in scale_names)):
+
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+
+                    # If weight loader supports special moe loading, use it to
+                    # avoid expensive runtime reflection
+                    if getattr(weight_loader, 'supports_moe_loading', False):
+                        # Map the weight name to the corresponding shard id.
+                        shard_id = "w2" if "w2_" in name else "w1"
+
+                        # Transpose if weight scales are FP8 block scales with
+                        # three dimensions:
+                        # [num_experts, hidden_in, hidden_out].
+                        if name.endswith("weight_scale") \
+                            and loaded_weight.dtype == torch.float8_e4m3fn \
+                            and loaded_weight.ndim == 3:
+                            loaded_weight = loaded_weight.transpose(-1, -2)
+
+                        # Load the weight into the module parameter with
+                        # corresponding shard id and expert id.
+                        weight_loader(param,
+                                      loaded_weight,
+                                      name,
+                                      shard_id=shard_id,
+                                      expert_id=0)
+
+                    else:
+                        # Regular weight loader (handles both
+                        # param.weight_loader and default_weight_loader)
+                        weight_loader(param, loaded_weight)
+
+                    loaded_params.add(name)
+                    continue
+
+                # Handle normal (non-stacked, non-MoE) weights.
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+                loaded_params.add(name)
+
+        # Finally, return the set of loaded parameters.
+        return loaded_params
+
+
+class Llama4ForCausalLM(LlamaForCausalLM):
+
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        # update temperature tuning config from generation config
+        gen_config = vllm_config.model_config.try_get_generation_config()
+        gen_config.update(vllm_config.model_config.override_generation_config)
+        # enable temperature tuning by default when max_model_len > 32K
+        default_attn_temperature_tuning = \
+            vllm_config.model_config.max_model_len > 32768
+        vllm_config.model_config.hf_config.attn_temperature_tuning \
+            = gen_config.get(
+                "attn_temperature_tuning", default_attn_temperature_tuning)
+
+        super().__init__(vllm_config=vllm_config,
+                         prefix=prefix,
+                         layer_type=Llama4DecoderLayer)
+
+    def _init_model(self,
+                    vllm_config: VllmConfig,
+                    prefix: str = "",
+                    layer_type: type[Llama4DecoderLayer] = Llama4DecoderLayer):
+        return Llama4Model(vllm_config=vllm_config,
+                           prefix=prefix,
+                           layer_type=layer_type)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        weights = [
+            self.permute_qk_weight_for_rotary(name, loaded_weight)
+            for name, loaded_weight in weights
+        ]
+        return loader.load_weights(weights)
+
+    def permute_qk_weight_for_rotary(
+        self,
+        name: str,
+        loaded_weight: torch.Tensor,
+    ) -> tuple[str, torch.Tensor]:
+
+        # Helper function to permute the weight's channels
+        def permute(w: torch.Tensor, n_heads: int, is_weight_scale: bool):
+
+            # Calculate the expected shape of the weight.
+            # Do not rely on w's shape, as it may be in another layout.
+            attn_in = self.config.head_dim * n_heads
+            attn_out = self.config.hidden_size
+
+            # If the weight is FP4 packed as uint8, we need to divide attn_out
+            # by 2.
+            if w.dtype == torch.uint8 and w.shape[1] * 2 == attn_out:
+                attn_out = attn_out // 2
+
+            # If the weight is a weight scale, we need to divide attn_out by
+            # block size, which is currently 16.
+            elif w.dtype == torch.float8_e4m3fn and is_weight_scale \
+                and w.shape[1] * 16 == attn_out:
+                attn_out = attn_out // 16
+
+            return w.view(n_heads, attn_in // n_heads // 2, 2,
+                          attn_out).transpose(1, 2).reshape(attn_in, attn_out)
+
+        modules = name.split(".")
+
+        # Permute Q/K weights and weight block scales for rotary embedding
+        is_weight = modules[-1] == "weight"
+        is_nvfp4_weight_scale = (modules[-1] == "weight_scale" and
+                                 loaded_weight.dtype == torch.float8_e4m3fn)
+
+        if is_weight or is_nvfp4_weight_scale:
+            if ("wk" in modules or "k_proj" in modules):
+                loaded_weight = permute(loaded_weight,
+                                        self.config.num_key_value_heads,
+                                        is_nvfp4_weight_scale)
+            elif ("wq" in modules or "q_proj" in modules):
+                loaded_weight = permute(loaded_weight,
+                                        self.config.num_attention_heads,
+                                        is_nvfp4_weight_scale)
+
+        return name, loaded_weight
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/medusa.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/medusa.py
new file mode 100644
index 0000000000000000000000000000000000000000..709a5a993c6f7509b03f4f7c995b570eacacdaf1
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/medusa.py
@@ -0,0 +1,219 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+import torch.nn as nn
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+
+
+class ResidualBlock(nn.Module):
+
+    def __init__(self, config: VllmConfig, hidden_size: int,
+                 num_layers: int) -> None:
+        super().__init__()
+
+        self.layers = nn.ModuleList([
+            nn.Linear(hidden_size,
+                      hidden_size,
+                      bias=getattr(config, "medusa_fc_bias", False))
+            for _ in range(num_layers)
+        ])
+        self.act = nn.SiLU()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        for layer in self.layers:
+            x = x + self.act(layer(x))
+        return x
+
+
+class Medusa(nn.Module):
+    """This class implements the Medusa draft model from the paper: https://arxiv.org/abs/2401.10774
+    Reference implementation: https://github.com/FasterDecoding/Medusa
+    
+    Differences from reference implementation:
+    1. Currently this only supports generating proposals from top-1 tokens.
+    2. We have an optional token_map which reduces draft vocab to most 
+       frequently used tokens to give some additional speed-up by reducing 
+       sampling overhead. This is disabled unless the checkpoint file has 
+       explicit token_map tensor and config has an optional attribute 
+       truncated_vocab_size < vocab_size. To use this technique, one has to find
+       the top-k most frequent tokens in target dataset and add that as a tensor
+       in the draft checkpoint (using key token_map). Also, the draft config
+       needs to have truncated_vocab_size (=k) as an attribute."""
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        config = vllm_config.speculative_config.draft_model_config.hf_config
+        super().__init__()
+        self.config = config
+        self.blocks = nn.ModuleList([
+            ResidualBlock(config=config,
+                          hidden_size=self.config.hidden_size,
+                          num_layers=self.config.num_hidden_layers)
+            for _ in range(self.config.num_heads)
+        ])
+        self.orig_vocab_size = config.vocab_size
+        self.truncated_vocab_size = config.truncated_vocab_size
+        self.unpadded_vocab_size = self.truncated_vocab_size
+
+        if getattr(config, "original_lm_head", False):
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=self.truncated_vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+            )
+            self.lm_heads = [
+                self.lm_head for _ in range(self.config.num_heads)
+            ]
+        else:
+            self.lm_heads = nn.ModuleList([
+                ParallelLMHead(
+                    self.unpadded_vocab_size,
+                    config.hidden_size,
+                    org_num_embeddings=self.truncated_vocab_size,
+                    padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                ) for _ in range(self.config.num_heads)
+            ])
+
+        logit_scale = getattr(config, "logit_scale", 1.0)
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                self.truncated_vocab_size,
+                                                logit_scale)
+
+        # Token map is a idx to token mapping to reduce the vocab size for
+        # the draft model. Using smaller vocab size for draft, containing
+        # only most frequent tokens reduces the speculation overhead. This
+        # doesn't affect the acceptance rate much and thus gives more speed
+        # -up. By default, this is disabled and is only used if the EAGLE
+        # checkpoint file has token_map tensor.
+        self.token_map = None
+
+    def forward(self, hidden_states: torch.Tensor) -> list[torch.Tensor]:
+        return [block(hidden_states) for block in self.blocks]
+
+    def compute_logits(
+            self, hidden_states: list[torch.Tensor],
+            sampling_metadata: SamplingMetadata) -> list[torch.Tensor]:
+        logits_lst: list[torch.Tensor] = []
+
+        for hs, lm_head in zip(hidden_states, self.lm_heads):
+            _logits = self.logits_processor(lm_head, hs, sampling_metadata)
+
+            if _logits is None:
+                # _logits should only be None on rank > 0, in which case
+                # it should remain true for every lm_head
+                assert len(logits_lst) == 0
+                continue
+
+            if self.token_map is None:
+                logits_lst.append(_logits)
+            else:
+                logits_lst.append(-torch.inf * torch.ones(
+                    size=(*_logits.shape[:-1], self.orig_vocab_size),
+                    device=_logits.device,
+                    dtype=_logits.dtype))
+
+                logits_lst[-1][..., self.token_map] = _logits
+
+        return logits_lst
+
+    def sample(
+        self,
+        logits: list[torch.Tensor],
+        sampling_metadata: SamplingMetadata,
+    ) -> list[SamplerOutput]:
+        logits = torch.stack(logits, dim=0).float()
+        logprobs = torch.log_softmax(logits, dim=-1)
+        token_ids = logits.argmax(-1)  # support only top-1 for now
+        probs = torch.softmax(logits, dim=-1)
+
+        token_id_list = []
+        token_prob_list = []
+        token_logprob_list = []
+
+        for idx, seq_group in enumerate(sampling_metadata.seq_groups):
+            token_id_list.append(token_ids[:, seq_group.sample_indices])
+            token_prob_list.append(probs[:, seq_group.sample_indices])
+            token_logprob_list.append(logprobs[:, seq_group.sample_indices])
+
+        outputs: list[Optional[SamplerOutput]] = []
+        for idx in range(len(sampling_metadata.seq_groups)):
+            outputs.append(
+                SamplerOutput(
+                    outputs=None,
+                    sampled_token_probs=token_prob_list[idx].squeeze(1),
+                    logprobs=token_logprob_list[idx].squeeze(1),
+                    sampled_token_ids=token_id_list[idx].squeeze(1),
+                ))
+
+        return outputs
+
+    def generate_proposals(
+        self,
+        previous_hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[list[SamplerOutput]]:
+        # During preemption, we may receive an empty tensor (batch_size=0)
+        if previous_hidden_states.size(0) == 0:
+            # Return None to signal the Top1Proposer that no proposals
+            # were generated for this batch, allowing it to handle this
+            # special case appropriately
+            return None
+
+        return self.sample(
+            logits=self.compute_logits(
+                hidden_states=self.forward(previous_hidden_states),
+                sampling_metadata=sampling_metadata,
+            ),
+            sampling_metadata=sampling_metadata,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        weights_map = {}
+
+        for name, loaded_weight in weights:
+            name = name.replace("medusa_heads.", "")
+
+            if name == "token_map":
+                if self.truncated_vocab_size < self.orig_vocab_size:
+                    self.token_map = nn.Parameter(loaded_weight,
+                                                  requires_grad=False)
+            elif name in params_dict:
+                weights_map[name] = loaded_weight
+            elif (getattr(self.config, "original_lm_head", False)
+                  and name == "lm_heads.0.weight"):
+                weights_map["lm_head.weight"] = loaded_weight
+
+        for name, loaded_weight in weights_map.items():
+            if "lm_head" in name and self.token_map is not None and\
+                loaded_weight.shape[0] > self.token_map.shape[0]:
+
+                loaded_weight = loaded_weight[self.token_map]
+
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        if self.token_map is not None:
+            self.token_map.to(device=self.lm_heads[0].weight.device)
+
+        assert (self.truncated_vocab_size
+                == self.orig_vocab_size) or (self.token_map is not None)
+
+        return loaded_params
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mimo_mtp.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mimo_mtp.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a2079bf5121a2ed8109083f1a77cd2df0eca0f4
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mimo_mtp.py
@@ -0,0 +1,273 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/model_executor/models/deepseek_mtp.py
+# Copyright 2025 Xiaomi Corporation.
+# Copyright 2023 The vLLM team.
+# Copyright 2024 DeepSeek-AI team.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only MiMo-MTP model."""
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.qwen2 import Qwen2DecoderLayer
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .utils import maybe_prefix
+
+
+class MiMoMultiTokenPredictorLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        prefix: str,
+        model_config: ModelConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+
+        self.token_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.hidden_layernorm = RMSNorm(config.hidden_size,
+                                        eps=config.rms_norm_eps)
+        self.input_proj = nn.Linear(config.hidden_size * 2,
+                                    config.hidden_size,
+                                    bias=False)
+        self.mtp_block = Qwen2DecoderLayer(config=config,
+                                           cache_config=cache_config,
+                                           quant_config=quant_config,
+                                           prefix=prefix)
+        self.final_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        spec_step_index: int = 0,
+    ) -> torch.Tensor:
+        assert inputs_embeds is not None
+        # masking inputs at position 0, as not needed by MTP
+        inputs_embeds[positions == 0] = 0
+        inputs_embeds = self.token_layernorm(inputs_embeds)
+        previous_hidden_states = self.hidden_layernorm(previous_hidden_states)
+
+        hidden_states = self.input_proj(
+            torch.cat([previous_hidden_states, inputs_embeds], dim=-1))
+
+        hidden_states, residual = self.mtp_block(positions=positions,
+                                                 hidden_states=hidden_states,
+                                                 residual=None)
+        hidden_states = residual + hidden_states
+        return self.final_layernorm(hidden_states)
+
+
+class MiMoMultiTokenPredictor(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        self.mtp_start_layer_idx = config.num_hidden_layers
+        self.num_mtp_layers = config.num_nextn_predict_layers
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+
+        self.mtp_layers = torch.nn.ModuleDict({
+            str(idx):
+            MiMoMultiTokenPredictorLayer(
+                config,
+                f"{prefix}.layers.{idx}",
+                model_config=vllm_config.model_config,
+                cache_config=vllm_config.cache_config,
+                quant_config=vllm_config.quant_config,
+            )
+            for idx in range(self.mtp_start_layer_idx,
+                             self.mtp_start_layer_idx + self.num_mtp_layers)
+        })
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        previous_hidden_states: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        return self.mtp_layers[str(self.mtp_start_layer_idx + spec_step_idx)](
+            inputs_embeds,
+            positions,
+            previous_hidden_states,
+            spec_step_idx,
+        )
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        lm_head: ParallelLMHead,
+        sampling_metadata: SamplingMetadata,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        self.mtp_layers[str(self.mtp_start_layer_idx + spec_step_idx)]
+        logits = self.logits_processor(lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+
+class MiMoMTP(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        self.config = vllm_config.model_config.hf_config
+        self.model = MiMoMultiTokenPredictor(vllm_config=vllm_config,
+                                             prefix=maybe_prefix(
+                                                 prefix, "model"))
+        self.lm_head = ParallelLMHead(self.config.vocab_size,
+                                      self.config.hidden_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        spec_step_idx: int = 0,
+    ) -> torch.Tensor:
+        assert spec_step_idx == 0, "mimo_mtp only support predict one token now"
+        hidden_states = self.model(input_ids, positions, hidden_states,
+                                   inputs_embeds, spec_step_idx)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+        spec_step_idx: int = 0,
+    ) -> Optional[torch.Tensor]:
+        return self.model.compute_logits(hidden_states, self.lm_head,
+                                         sampling_metadata, spec_step_idx)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+
+            if "rotary_emb.inv_freq" in name:
+                continue
+            name = self.map_model_name_to_mtp_param_name(name)
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+                if "mtp_layers" not in name:
+                    break
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if (("mlp.experts." in name) and name not in params_dict):
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if "mtp_layers" not in name and ("embed_tokens" not in name
+                                                 and "lm_head" not in name):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+    def map_model_name_to_mtp_param_name(self, name: str) -> str:
+        import regex as re
+        name_without_prefix = [
+            "token_layernorm", "hidden_layernorm", "input_proj",
+            "final_layernorm"
+        ]
+        for sub_name in name_without_prefix:
+            if sub_name in name:
+                return name
+        pattern = r"model.mtp_layers.(\d+)."
+        group = re.match(pattern, name)
+        if group is not None:
+            name = name.replace(group.group(), group.group() + "mtp_block.")
+        return name
+
+    def _rewrite_spec_layer_name(self, spec_layer: int, name: str) -> str:
+        """
+        Rewrite the weight name to match the format of the original model.
+        Add .mtp_block for modules in transformer layer block for spec layer
+        """
+        spec_layer_weight_names = [
+            "embed_tokens", "enorm", "hnorm", "eh_proj", "shared_head"
+        ]
+        spec_layer_weight = False
+        for weight_name in spec_layer_weight_names:
+            if weight_name in name:
+                spec_layer_weight = True
+                break
+        if not spec_layer_weight:
+            # treat rest weights as weights for transformer layer block
+            name = name.replace(f"model.layers.{spec_layer}.",
+                                f"model.layers.{spec_layer}.mtp_block.")
+        return name
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minicpm_eagle.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minicpm_eagle.py
new file mode 100644
index 0000000000000000000000000000000000000000..06c2eb4e80afb2922b3f7ac5555f74fac234f177
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minicpm_eagle.py
@@ -0,0 +1,391 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only EagleMiniCPM model compatible with HuggingFace weights."""
+import math
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .minicpm import MiniCPMAttention as EagleMiniCPMAttention
+from .minicpm import MiniCPMMLP as EagleMiniCPMMLP
+from .minicpm import MiniCPMMoE as EagleMiniCPMMoE
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, maybe_prefix)
+
+
+class EagleMiniCPMDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.cache_config = cache_config
+        self.quant_config = quant_config
+        self.hidden_size = config.hidden_size
+        self.rope_theta = getattr(config, "rope_theta", 10000)
+        self.rope_scaling = getattr(config, "rope_scaling", None)
+        self.max_position_embeddings = getattr(config,
+                                               "max_position_embeddings", 8192)
+        self.prefix = prefix
+        self._init_attn_block()
+        self._init_ffn_block()
+
+    def _init_attn_block(self):
+        self.input_layernorm = RMSNorm(self.config.hidden_size,
+                                       eps=self.config.rms_norm_eps)
+        self.self_attn = EagleMiniCPMAttention(
+            hidden_size=self.hidden_size,
+            num_heads=self.config.num_attention_heads,
+            num_kv_heads=self.config.num_key_value_heads,
+            rope_theta=self.rope_theta,
+            rope_scaling=self.rope_scaling,
+            max_position_embeddings=self.max_position_embeddings,
+            cache_config=self.cache_config,
+            quant_config=self.quant_config,
+            prefix=f"{self.prefix}.self_attn",
+        )
+
+    def _init_ffn_block(self):
+        self.post_attention_layernorm = RMSNorm(self.config.hidden_size,
+                                                eps=self.config.rms_norm_eps)
+        self.num_experts = getattr(self.config, "num_experts", 0)
+        if self.num_experts == 0:
+            self.mlp = EagleMiniCPMMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=self.config.intermediate_size,
+                hidden_act=self.config.hidden_act,
+                hidden_act_param=getattr(self.config, "hidden_act_param", 0.),
+                quant_config=self.quant_config,
+            )
+        else:
+            self.mlp = EagleMiniCPMMoE(
+                num_experts=self.config.num_experts,
+                top_k=self.config.num_experts_per_tok,
+                hidden_size=self.config.hidden_size,
+                intermediate_size=self.config.intermediate_size)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        hidden_states = residual + hidden_states * \
+            (self.config.scale_depth / math.sqrt(self.config.mup_denominator))
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states * \
+            (self.config.scale_depth / math.sqrt(self.config.mup_denominator))
+
+        return hidden_states, None
+
+
+@support_torch_compile
+class EagleMiniCPMModel(nn.Module):
+
+    def __init__(self,
+                 *,
+                 vllm_config: VllmConfig,
+                 prefix: str = "",
+                 start_layer: int = 0):
+        super().__init__()
+
+        config = vllm_config.speculative_config.draft_model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.cache_config = cache_config
+        self.quant_config = quant_config
+        lora_vocab = (lora_config.lora_extra_vocab_size *
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+        self.fc = torch.nn.Linear(self.config.hidden_size * 2,
+                                  self.config.hidden_size,
+                                  bias=False)
+        self.input_norm1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.input_norm2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+        self.num_experts = getattr(self.config, "num_experts", 0)
+        self._init_layers(prefix, config, cache_config, quant_config,
+                          start_layer)
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], self.config.hidden_size))
+
+    def _init_layers(
+        self,
+        prefix: str,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig],
+        quant_config: Optional[QuantizationConfig],
+        start_layer: int,
+    ):
+        self.eagle_layers = nn.ModuleList([
+            EagleMiniCPMDecoderLayer(
+                config,
+                cache_config,
+                quant_config,
+                f"{prefix}.eagle_layers.{i + start_layer}",
+            ) for i in range(self.config.num_hidden_layers)
+        ])
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        embedding = self.embed_tokens(input_ids)
+        return embedding * self.config.scale_emb
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        input_embeds = self.get_input_embeddings(input_ids)
+        input_embeds = self.input_norm1(input_embeds)
+        hidden_states = self.input_norm2(hidden_states)
+
+        hidden_states = self.fc(
+            torch.cat((input_embeds, hidden_states), dim=-1))
+        residual = None
+        for layer in self.eagle_layers:
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+
+        return hidden_states, hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        expert_params_mapping = [
+            # (param_name, weight_name, expert_id)
+            ("ws" if weight_name in ["w1", "w3"] else "w2s",
+             f"experts.{expert_id}.{weight_name}.weight", expert_id)
+            for expert_id in range(self.num_experts)
+            for weight_name in ["w1", "w2", "w3"]
+        ]
+        params_dict = dict(self.named_parameters())
+
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for param_name, weight_name, expert_id in expert_params_mapping:
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param,
+                                  loaded_weight,
+                                  weight_name,
+                                  expert_id=expert_id)
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class EagleMiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.speculative_config.draft_model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.prefix = prefix
+        self.vllm_config = vllm_config
+        self.config = config
+        self.lora_config = lora_config
+        self.cache_config = cache_config
+        self.quant_config = quant_config
+
+        target_layer_num = vllm_config.model_config.get_num_layers(
+            vllm_config.parallel_config)
+
+        self.model = self._init_model(vllm_config=vllm_config,
+                                      prefix=maybe_prefix(prefix, "model"),
+                                      start_layer=target_layer_num)
+
+        unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE
+            # We need bigger padding if using lora for kernel
+            # compatibility
+            if not lora_config else lora_config.lora_vocab_padding_size,
+            quant_config=quant_config,
+        )
+        if config.tie_word_embeddings:
+            self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
+        self.scale_width = self.config.hidden_size / self.config.dim_model_base
+
+        self.logits_processor = LogitsProcessor(unpadded_vocab_size,
+                                                config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def _init_model(self,
+                    *,
+                    vllm_config: VllmConfig,
+                    prefix: str = "",
+                    start_layer: int = 0):
+        return EagleMiniCPMModel(vllm_config=vllm_config,
+                                 prefix=prefix,
+                                 start_layer=start_layer)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        hidden_states, hidden_states2 = self.model(input_ids, positions,
+                                                   hidden_states)
+        hidden_states = hidden_states / self.scale_width
+        hidden_states2 = hidden_states2 / self.scale_width
+        return hidden_states, hidden_states2
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minicpmv.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minicpmv.py
new file mode 100644
index 0000000000000000000000000000000000000000..47ce771d8c90196c19c4d7d3edd24be55b7ef124
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minicpmv.py
@@ -0,0 +1,1440 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only MiniCPM-V model compatible with HuggingFace weights."""
+import math
+from collections import defaultdict
+from collections.abc import Iterable, Mapping, Sequence
+from functools import partial
+from typing import Annotated, Any, Callable, Literal, Optional, Union
+
+import numpy as np
+import torch
+import torch.types
+from torch import nn
+from transformers import BatchFeature, PretrainedConfig
+from typing_extensions import TypeVar
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.awq import AWQConfig
+from vllm.model_executor.layers.quantization.awq_marlin import AWQMarlinConfig
+from vllm.model_executor.layers.resampler import (BaseResampler, Resampler2,
+                                                  get_2d_sincos_pos_embed)
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+from vllm.model_executor.models.llama import LlamaForCausalLM
+from vllm.model_executor.models.minicpm import MiniCPMForCausalLM
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.models.qwen2 import Qwen2ForCausalLM
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    NestedTensors)
+from vllm.multimodal.parse import (DictEmbeddingItems, ImageItem,
+                                   ImageProcessorItems, ImageSize,
+                                   ModalityData, ModalityDataItems,
+                                   MultiModalDataItems, MultiModalDataParser,
+                                   VideoItem, VideoProcessorItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.platforms import current_platform
+from vllm.sequence import IntermediateTensors
+from vllm.utils import flatten_2d_lists
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .idefics2_vision_model import Idefics2VisionTransformer
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
+from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+# For profile run
+_MAX_FRAMES_PER_VIDEO = 16
+
+
+class MiniCPMVImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - bns: Batch size * number of images * number of slices
+        - bn: Batch size * number of images
+        - c: Number of channels
+        - h: Height
+        - w: Width
+    """
+
+    type: Literal["pixel_values"] = "pixel_values"
+
+    # Note that the image size may vary, so we pass it as a list instead of a
+    # batched tensor.
+    pixel_values: Annotated[
+        list[torch.Tensor],
+        TensorShape("bns", "c", "h", "w", dynamic_dims={"h", "w"}),
+    ]
+    tgt_sizes: Annotated[
+        torch.Tensor,
+        TensorShape("bns", 2),  # This should be in `(height, width)` format.
+    ]
+    num_slices: Annotated[
+        torch.Tensor,
+        TensorShape("bn"),
+    ]
+
+
+class MiniCPMVImageEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - bn: Batch size * number of images
+        - ns: Number of slices
+        - hs: Hidden size (must match language model backbone)
+    """
+
+    type: Literal["image_embeds"]
+    image_embeds: Annotated[
+        Union[torch.Tensor, list[torch.Tensor]],
+        TensorShape("bn", "ns", "hs"),
+    ]
+
+
+MiniCPMVImageInputs = Union[MiniCPMVImagePixelInputs,
+                            MiniCPMVImageEmbeddingInputs]
+
+DEFAULT_LN = partial(nn.LayerNorm, eps=1e-6)
+
+
+class Resampler2_5(BaseResampler):
+
+    def __init__(self,
+                 num_queries: int,
+                 embed_dim: int,
+                 num_heads: int,
+                 kv_dim: Optional[int] = None,
+                 norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
+                 max_size: tuple[int, int] = (70, 70),
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = "") -> None:
+        super().__init__(num_queries,
+                         embed_dim,
+                         num_heads,
+                         kv_dim,
+                         norm_layer,
+                         quant_config=quant_config,
+                         prefix=prefix)
+
+        self.max_size = max_size
+        self._set_2d_pos_cache(self.max_size)
+
+    def _set_2d_pos_cache(self,
+                          max_size: tuple[int, int],
+                          device: torch.types.Device = "cpu") -> None:
+        pos_embed_arr = get_2d_sincos_pos_embed(self.embed_dim,
+                                                max_size,
+                                                version=(2, 5))
+        pos_embed = torch.from_numpy(pos_embed_arr).float().to(device)
+        self.register_buffer("pos_embed", pos_embed, persistent=False)
+
+    def _adjust_pos_cache(self, tgt_sizes: torch.Tensor,
+                          device: torch.types.Device) -> None:
+        max_h = tgt_sizes[:, 0].max().item()
+        max_w = tgt_sizes[:, 1].max().item()
+        assert isinstance(max_h, int) and isinstance(max_w, int)
+
+        if max_h > self.max_size[0] or max_w > self.max_size[1]:
+            self.max_size = (
+                max(max_h, self.max_size[0]),
+                max(max_w, self.max_size[1]),
+            )
+            self._set_2d_pos_cache(self.max_size, device)
+
+    def forward(self, x: torch.Tensor,
+                tgt_sizes: torch.Tensor) -> torch.Tensor:
+        assert x.shape[0] == tgt_sizes.shape[0]
+        bs = x.shape[0]
+
+        device = x.device
+        dtype = x.dtype
+
+        patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
+
+        self._adjust_pos_cache(tgt_sizes, device=device)
+
+        max_patch_len = patch_len.max().item()
+        assert isinstance(max_patch_len, int)
+
+        key_padding_mask = torch.zeros((bs, max_patch_len),
+                                       dtype=torch.bool,
+                                       device=device)
+
+        pos_embed = []
+        for i in range(bs):
+            tgt_h, tgt_w = tgt_sizes[i].tolist()
+            pos_embed.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape(
+                (tgt_h * tgt_w, -1)).to(dtype))  # patches * D
+            key_padding_mask[i, patch_len[i]:] = True
+        pos_embed = torch.nn.utils.rnn.pad_sequence(pos_embed,
+                                                    batch_first=True,
+                                                    padding_value=0.0).permute(
+                                                        1, 0,
+                                                        2)  # BLD => L * B * D
+        x, _ = self.kv_proj(x)  # B * L * D
+        x = self.ln_kv(x).permute(1, 0, 2)  # L * B * D
+
+        q = self.ln_q(self.query)  # Q * D
+
+        out = self.attn(
+            self._repeat(q, bs),  # Q * B * D
+            x + pos_embed,  # L * B * D +  L * B * D
+            x,
+            key_padding_mask=key_padding_mask,
+        )[0]
+        #  out: Q * B * D
+        x = out.permute(1, 0, 2)  # B * Q * D
+
+        x = self.ln_post(x)
+        x = x @ self.proj
+        return x
+
+
+def get_version_by_config(config: PretrainedConfig) -> tuple[int, ...]:
+    version_float = getattr(config, "version", None)
+
+    # The old configs do not include version number
+    # TODO: Remove this after the HF repos are updated
+    if version_float is None:
+        if config.hidden_size == 2304 and config.query_num == 64:
+            return (2, 0)
+        return (2, 5)
+    version_str = str(version_float)
+    return tuple(int(x) for x in version_str.split("."))
+
+
+def _minicpmv_field_config(hf_inputs: Mapping[str, torch.Tensor]):
+    pixel_values = hf_inputs.get("pixel_values", torch.empty(0))
+    num_images = len(pixel_values)
+
+    video_pixel_values = hf_inputs.get("video_pixel_values", torch.empty(0))
+    num_videos = len(video_pixel_values)
+
+    return dict(
+        pixel_values=MultiModalFieldConfig.batched("image"),
+        image_sizes=MultiModalFieldConfig.batched("image"),
+        tgt_sizes=MultiModalFieldConfig.batched("image"),
+        image_embeds=MultiModalFieldConfig.batched("image"),
+        video_pixel_values=MultiModalFieldConfig.batched("video"),
+        video_image_sizes=MultiModalFieldConfig.batched("video"),
+        video_tgt_sizes=MultiModalFieldConfig.batched("video"),
+        video_embeds=MultiModalFieldConfig.batched("video"),
+        image_token_id=MultiModalFieldConfig.shared("image", num_images),
+        video_token_id=MultiModalFieldConfig.shared("video", num_videos),
+    )
+
+
+class MiniCPMVImageEmbeddingItems(DictEmbeddingItems):
+
+    def __init__(
+        self,
+        data: Mapping[str, torch.Tensor],
+        fields_factory: Callable[
+            [Mapping[str, torch.Tensor]],
+            Mapping[str, MultiModalFieldConfig],
+        ],
+    ) -> None:
+        super().__init__(
+            data,
+            modality="image",
+            required_fields={"image_embeds", "image_sizes"},
+            fields_factory=fields_factory,
+        )
+
+    def get_image_size(self, index: int) -> ImageSize:
+        image_size = self.get(index)["image_sizes"].tolist()
+        return ImageSize(width=image_size[0], height=image_size[1])
+
+
+class MiniCPMVVideoEmbeddingItems(DictEmbeddingItems):
+
+    def __init__(
+        self,
+        data: Mapping[str, torch.Tensor],
+        fields_factory: Callable[
+            [Mapping[str, torch.Tensor]],
+            Mapping[str, MultiModalFieldConfig],
+        ],
+    ) -> None:
+        super().__init__(
+            data,
+            modality="video",
+            required_fields={"video_embeds", "video_image_sizes"},
+            fields_factory=fields_factory,
+        )
+
+    def get_frame_size(self, index: int) -> ImageSize:
+        frame_size = self.get(index)["video_image_sizes"].tolist()
+        return ImageSize(width=frame_size[0], height=frame_size[1])
+
+    def get_num_frames(self, index: int) -> int:
+        return len(self.get(index)["video_image_sizes"])
+
+
+class MiniCPMVMultiModalDataParser(MultiModalDataParser):
+
+    def _parse_image_data(
+        self,
+        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
+    ) -> Optional[ModalityDataItems[Any, Any]]:
+        if isinstance(data, dict):
+            return MiniCPMVImageEmbeddingItems(
+                data,
+                fields_factory=_minicpmv_field_config,
+            )
+
+        return super()._parse_image_data(data)
+
+    def _parse_video_data(
+        self,
+        data: Union[dict[str, torch.Tensor], ModalityData[VideoItem]],
+    ) -> Optional[ModalityDataItems[Any, Any]]:
+        if isinstance(data, dict):
+            return MiniCPMVVideoEmbeddingItems(
+                data,
+                fields_factory=_minicpmv_field_config,
+            )
+
+        return super()._parse_video_data(data)
+
+
+class MiniCPMVProcessingInfo(BaseProcessingInfo):
+    image_pattern = "(<image>./</image>)"
+    video_pattern = "(<video>./</video>)"
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config()
+
+    def get_hf_processor(self, **kwargs: object):
+        hf_processor = self.ctx.get_hf_processor(**kwargs)
+
+        # NumPy arrays are considered as Iterable but not Sequence in
+        # https://github.com/huggingface/transformers/blob/main/src/transformers/image_transforms.py#L428
+        image_processor = hf_processor.image_processor  # type: ignore
+        for attr in ("mean", "std"):
+            val = getattr(image_processor, attr)
+            if isinstance(val, np.ndarray):
+                setattr(image_processor, attr, val.tolist())
+
+        return hf_processor
+
+    def get_image_processor(self, **kwargs: object):
+        return self.get_hf_processor(**kwargs).image_processor
+
+    def get_model_version(self):
+        return get_version_by_config(self.get_hf_config())
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        mm_limits = {"image": None}
+        if self.get_model_version() == (2,
+                                        6) or self.get_model_version() == (4,
+                                                                           0):
+            mm_limits["video"] = None
+
+        return mm_limits
+
+    def get_slice_image_placeholder(
+        self,
+        image_size: ImageSize,
+        # For MiniCPM V/O 2.6
+        image_idx: int = 0,
+        max_slice_nums: Optional[int] = None,
+        use_image_id: bool = True,
+    ) -> str:
+        image_processor = self.get_image_processor()
+        version = self.get_model_version()
+
+        if version == (2, 0) or version == (2, 5):
+            return image_processor.get_slice_image_placeholder(image_size)
+
+        return image_processor.get_slice_image_placeholder(
+            image_size,
+            image_idx=image_idx,
+            max_slice_nums=max_slice_nums,
+            use_image_id=use_image_id,
+        )
+
+    def get_sliced_grid(
+        self,
+        image_size: ImageSize,
+        # For MiniCPM V/O 2.6
+        max_slice_nums: Optional[int] = None,
+    ) -> Optional[tuple[int, int]]:
+        image_processor = self.get_image_processor()
+        version = self.get_model_version()
+
+        if version == (2, 0) or version == (2, 5):
+            return image_processor.get_sliced_grid(image_size)
+
+        if max_slice_nums is None:
+            max_slice_nums = image_processor.max_slice_nums
+
+        return image_processor.get_sliced_grid(
+            image_size,
+            max_slice_nums=max_slice_nums,
+        )
+
+    def get_num_image_tokens(
+        self,
+        image_size: ImageSize,
+        max_slice_nums: Optional[int] = None,
+    ) -> int:
+        image_processor = self.get_image_processor()
+
+        grid = self.get_sliced_grid(
+            image_size,
+            max_slice_nums=max_slice_nums,
+        )
+        if grid is None:
+            ncols = nrows = 0
+        else:
+            ncols, nrows = grid
+
+        return (ncols * nrows + 1) * image_processor.image_feature_size
+
+    def get_max_image_tokens(self) -> int:
+        image_size = self.get_image_size_with_most_features()
+        return self.get_num_image_tokens(image_size)
+
+    def get_image_max_slice_num(self) -> int:
+        return getattr(self.get_hf_config(), "max_slice_num", 9)
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        image_size = getattr(self.get_hf_config(), "image_size", 448)
+        max_slice_num = self.get_image_max_slice_num()
+        return ImageSize(width=image_size, height=image_size * max_slice_num)
+
+    def get_max_video_frame_tokens(self) -> int:
+        frame_size = self.get_video_frame_size_with_most_features()
+
+        return self.get_num_image_tokens(
+            frame_size,
+            max_slice_nums=self.get_video_max_slice_num(),
+        )
+
+    def get_max_video_tokens(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        num_frames = self.get_num_frames_with_most_features(seq_len, mm_counts)
+        num_video_tokens_total = self.get_max_video_frame_tokens() * num_frames
+        return num_video_tokens_total
+
+    def get_video_max_slice_num(self) -> int:
+        return 1
+
+    def get_video_frame_size_with_most_features(self) -> ImageSize:
+        image_size = getattr(self.get_hf_config(), "image_size", 448)
+        max_slice_num = self.get_video_max_slice_num()
+        return ImageSize(width=image_size, height=image_size * max_slice_num)
+
+    def get_max_video_frames(self, max_tokens: int) -> int:
+        num_frame_tokens = self.get_max_video_frame_tokens()
+        num_frames = max_tokens // num_frame_tokens
+        return num_frames
+
+    def get_num_frames_with_most_features(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        max_images = mm_counts.get("image", 0)
+        max_videos = mm_counts.get("video", 0)
+
+        max_image_tokens = self.get_max_image_tokens() * max_images
+        max_total_frames = self.get_max_video_frames(seq_len -
+                                                     max_image_tokens)
+        max_frames_per_video = min(max_total_frames // max(max_videos, 1),
+                                   _MAX_FRAMES_PER_VIDEO)
+
+        return max(max_frames_per_video, 1)
+
+
+_I = TypeVar("_I",
+             bound=MiniCPMVProcessingInfo,
+             default=MiniCPMVProcessingInfo)
+
+
+class MiniCPMVDummyInputsBuilder(BaseDummyInputsBuilder[_I]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        image_prompt_texts = self.info.image_pattern * num_images
+        video_prompt_texts = self.info.video_pattern * num_videos
+
+        return image_prompt_texts + video_prompt_texts
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        image_width, image_height = \
+            self.info.get_image_size_with_most_features()
+        video_width, video_height = \
+            self.info.get_video_frame_size_with_most_features()
+        num_video_frames = \
+            self.info.get_num_frames_with_most_features(seq_len, mm_counts)
+
+        return {
+            "image":
+            self._get_dummy_images(width=image_width,
+                                   height=image_height,
+                                   num_images=num_images),
+            "video": [
+                self._get_dummy_images(width=video_width,
+                                       height=video_height,
+                                       num_images=num_video_frames)
+            ] * num_videos,
+        }
+
+
+class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        return MiniCPMVMultiModalDataParser()
+
+    def get_image_prompt_texts(self,
+                               image_size: ImageSize,
+                               image_idx: int = 0) -> str:
+        return self.info.get_slice_image_placeholder(
+            image_size,
+            image_idx=image_idx,
+        )
+
+    def get_video_prompt_texts(self, image_size: ImageSize,
+                               num_frames: int) -> str:
+        return self.info.get_slice_image_placeholder(
+            image_size=image_size,
+            image_idx=0,
+            max_slice_nums=self.info.get_video_max_slice_num(),
+            use_image_id=False,
+        ) * num_frames
+
+    def process_images(
+        self,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> Mapping[str, NestedTensors]:
+        if (images := mm_data.get("images")) is None:
+            return {}
+
+        parsed_images = (self._get_data_parser().parse_mm_data({
+            "image": images
+        }).get_items("image",
+                     (MiniCPMVImageEmbeddingItems, ImageProcessorItems)))
+
+        if isinstance(parsed_images, MiniCPMVImageEmbeddingItems):
+            image_inputs = {}
+        else:
+            image_inputs = self._base_call_hf_processor(
+                prompts=[self.info.image_pattern] * len(parsed_images),
+                mm_data={"images": [[image] for image in parsed_images]},
+                mm_kwargs=mm_kwargs,
+                tok_kwargs=tok_kwargs,
+                out_keys={"pixel_values", "image_sizes", "tgt_sizes"},
+            )
+
+        tokenizer = self.info.get_tokenizer()
+        unk_token_id = tokenizer.get_vocab()["<unk>"]
+        image_inputs["image_token_id"] = torch.tensor(unk_token_id)
+
+        return image_inputs
+
+    def process_videos(
+        self,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> Mapping[str, NestedTensors]:
+        if (videos := mm_data.get("videos")) is None:
+            return {}
+
+        parsed_videos = (self._get_data_parser().parse_mm_data({
+            "video": videos
+        }).get_items("video",
+                     (MiniCPMVVideoEmbeddingItems, VideoProcessorItems)))
+
+        if isinstance(parsed_videos, MiniCPMVVideoEmbeddingItems):
+            video_inputs = {}
+        else:
+            video_inputs = self._base_call_hf_processor(
+                prompts=[
+                    self.info.image_pattern * len(video)
+                    for video in parsed_videos
+                ],
+                mm_data={"images": list(parsed_videos)},
+                mm_kwargs={
+                    **mm_kwargs,
+                    "max_slice_nums":
+                    self.info.get_video_max_slice_num(),
+                },
+                tok_kwargs=tok_kwargs,
+                out_keys={"pixel_values", "image_sizes", "tgt_sizes"},
+            )
+
+        video_inputs = {f"video_{k}": v for k, v in video_inputs.items()}
+
+        tokenizer = self.info.get_tokenizer()
+        unk_token_id = tokenizer.get_vocab()["<unk>"]
+        video_inputs["video_token_id"] = torch.tensor(unk_token_id)
+
+        return video_inputs
+
+    def process_mm_inputs(
+        self,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> Mapping[str, NestedTensors]:
+        return {
+            **self.process_images(mm_data, mm_kwargs, tok_kwargs),
+            **self.process_videos(mm_data, mm_kwargs, tok_kwargs),
+        }
+
+    def _base_call_hf_processor(
+        self,
+        prompts: list[str],
+        mm_data: Mapping[str, Sequence[object]],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+        *,
+        out_keys: set[str],
+    ) -> dict[str, NestedTensors]:
+        # This processor supports zipping prompt and mm_data together
+        if self.info.get_model_version() == (
+                2, 6) or self.info.get_model_version() == (4, 0):
+            inputs = super()._call_hf_processor(
+                prompt=prompts,  # type: ignore
+                mm_data=mm_data,
+                mm_kwargs=mm_kwargs,
+                tok_kwargs=tok_kwargs,
+            )
+        else:
+            inputs = defaultdict[str, list[torch.Tensor]](list)
+
+            for i, prompt in enumerate(prompts):
+                inputs_one = super()._call_hf_processor(
+                    prompt=prompt,
+                    mm_data={
+                        k: v[i]
+                        for k, v in mm_data.items()
+                    },
+                    mm_kwargs=mm_kwargs,
+                    tok_kwargs=tok_kwargs,
+                )
+
+                for k, v in inputs_one.items():
+                    assert len(v) == 1, (k, len(v))
+                    inputs[k].append(v[0])
+
+        return {k: inputs[k] for k in out_keys}
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        tokenizer = self.info.get_tokenizer()
+
+        input_ids = torch.tensor([tokenizer.encode(prompt, **tok_kwargs)])
+        mm_inputs = self.process_mm_inputs(mm_data, mm_kwargs, tok_kwargs)
+
+        return BatchFeature({
+            "input_ids": input_ids,
+            **mm_inputs,
+        })
+
+    def _hf_processor_applies_updates(
+        self,
+        prompt_text: str,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        tokenization_kwargs: Mapping[str, object],
+    ) -> bool:
+        return False
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        placeholders = [("image", self.info.image_pattern),
+                        ("video", self.info.video_pattern)]
+
+        # hard code for inconsistency of encode-decode image_pattern
+        additional_placeholders = []
+        tokenizer = self.info.get_tokenizer()
+        for modality, pattern in placeholders:
+            sub_pattern = tokenizer.decode(
+                tokenizer.encode(pattern, add_special_tokens=False))
+            if sub_pattern != pattern:
+                additional_placeholders.append((modality, sub_pattern))
+        placeholders += additional_placeholders
+
+        def get_image_replacement(item_idx: int):
+            images = mm_items.get_items(
+                "image", (MiniCPMVImageEmbeddingItems, ImageProcessorItems))
+
+            image_size = images.get_image_size(item_idx)
+
+            return PromptUpdateDetails.select_text(
+                self.get_image_prompt_texts(image_size, item_idx),
+                "<unk>",
+            )
+
+        def get_video_replacement(item_idx: int):
+            videos = mm_items.get_items(
+                "video", (MiniCPMVVideoEmbeddingItems, VideoProcessorItems))
+
+            frame_size = videos.get_frame_size(item_idx)
+            num_frames = videos.get_num_frames(item_idx)
+
+            return PromptUpdateDetails.select_text(
+                self.get_video_prompt_texts(frame_size, num_frames),
+                "<unk>",
+            )
+
+        get_replacement = {
+            "image": get_image_replacement,
+            "video": get_video_replacement,
+        }
+
+        return [
+            PromptReplacement(modality=modality,
+                              target=pattern,
+                              replacement=get_replacement[modality])
+            for modality, pattern in placeholders
+        ]
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return _minicpmv_field_config(hf_inputs)
+
+
+class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
+    """
+    The abstract class of MiniCPMV can only be inherited, but cannot be
+    instantiated.
+    """
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "(<image>./</image>)"
+        if modality.startswith("video"):
+            return "(<video>./</video>)"
+
+        raise ValueError("Only image or video modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        quant_config = vllm_config.quant_config
+        super().__init__()
+        # All MiniCPM-V models disable `tie_word_embeddings` but
+        # `PretrainedConfig.tie_word_embeddings` defaults to True; we cannot
+        # check `tie_word_embeddings` until vLLM integrate MiniCPM-V model
+        # and config class
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.version = get_version_by_config(self.config)
+        self.llm = self.init_llm(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "llm"))
+        self.vpm = self.init_vision_module(config,
+                                           quant_config,
+                                           prefix=maybe_prefix(prefix, "vpm"))
+        self.vision_dim = (self.vpm.embed_dim if self.version == (2, 0) else
+                           self.vpm.embeddings.embed_dim)
+        self.embed_dim = self.config.hidden_size
+
+        self.resampler = self.init_resampler(self.embed_dim,
+                                             self.vision_dim,
+                                             quant_config=quant_config,
+                                             prefix=maybe_prefix(
+                                                 prefix, "resampler"))
+
+        self.mm_token_ids = set[int]()
+        self.make_empty_intermediate_tensors = (
+            self.llm.make_empty_intermediate_tensors)
+
+    def _parse_and_validate_vision_input(
+        self,
+        modality: str,
+        **kwargs: object,
+    ) -> Optional[MiniCPMVImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        image_token_id = kwargs.pop("image_token_id")
+        if image_token_id is not None:
+            assert isinstance(image_token_id, torch.Tensor)
+            self.mm_token_ids.add(image_token_id.flatten().unique().item())
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, (torch.Tensor, list)):
+                raise ValueError(
+                    f"Incorrect type of image_embeds for {modality=}. "
+                    f"Got type: {type(image_embeds)}")
+
+            image_embeds_flat = flatten_bn(image_embeds)
+
+            return MiniCPMVImageEmbeddingInputs(
+                type="image_embeds",
+                image_embeds=image_embeds_flat,
+            )
+
+        if not isinstance(pixel_values, (torch.Tensor, list)):
+            raise ValueError(
+                f"Incorrect type of pixel_values for {modality=}. "
+                f"Got type: {type(pixel_values)}")
+
+        tgt_sizes = kwargs.pop("tgt_sizes")
+        if not isinstance(tgt_sizes, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of tgt_sizes for {modality=}. "
+                             f"Got type: {type(tgt_sizes)}")
+
+        num_slices = [[len(p) for p in ps] for ps in pixel_values]
+        num_slices_flat = flatten_bn(torch.tensor(num_slices))
+
+        pixel_values_flat = flatten_bn(flatten_2d_lists(pixel_values))
+        tgt_sizes_flat = flatten_bn(flatten_2d_lists(tgt_sizes), concat=True)
+
+        return MiniCPMVImagePixelInputs(
+            type="pixel_values",
+            pixel_values=pixel_values_flat,
+            tgt_sizes=tgt_sizes_flat,
+            num_slices=num_slices_flat,
+        )
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        modalities = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if input_key in ("pixel_values",
+                             "image_embeds") and "images" not in modalities:
+                modalities["images"] = self._parse_and_validate_vision_input(
+                    "images", **kwargs)
+            if input_key in ("video_pixel_values",
+                             "video_embeds") and "videos" not in modalities:
+
+                def _image_key(video_key: str):
+                    if video_key == "video_token_id":
+                        return "image_token_id"
+
+                    return video_key.removeprefix("video_")
+
+                modalities["videos"] = self._parse_and_validate_vision_input(
+                    "videos", **{
+                        _image_key(k): v
+                        for k, v in kwargs.items()
+                    })
+
+        return modalities
+
+    def _process_vision_input(
+        self,
+        image_input: MiniCPMVImageInputs,
+    ) -> Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor, ...]]:
+        if image_input["type"] == "image_embeds":
+            return image_input["image_embeds"]
+
+        image_features_flat = self.get_vision_hidden_states(image_input)
+
+        num_slices = image_input["num_slices"]
+        return [
+            e.flatten(0, 1)
+            for e in image_features_flat.split(num_slices.tolist())
+        ]
+
+    def _process_multimodal_inputs(self, modalities: dict):
+        # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor correspoending to a multimodal data item (image or video).
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in modalities:
+            if modality == "images":
+                image_input = modalities["images"]
+                image_features = self._process_vision_input(image_input)
+                multimodal_embeddings += tuple(image_features)
+            if modality == "videos":
+                video_input = modalities["videos"]
+                video_features = self._process_vision_input(video_input)
+                multimodal_embeddings += tuple(video_features)
+
+        return multimodal_embeddings
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.llm
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not modalities:
+            return []
+
+        return self._process_multimodal_inputs(modalities)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.llm.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            assert len(self.mm_token_ids) > 0
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                list(self.mm_token_ids),
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: Any,
+    ) -> torch.Tensor:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner from
+        # `get_multimodal_embeddings` and `get_input_embeddings`, this
+        # condition is only for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.llm.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.llm.compute_logits(hidden_states, sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(language_model="llm",
+                                                connector="resampler",
+                                                tower_model="vpm")
+
+    def init_llm(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> nn.Module:
+        raise NotImplementedError
+
+    def init_vision_module(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig],
+        prefix: str = "",
+    ) -> nn.Module:
+        raise NotImplementedError
+
+    def init_resampler(self,
+                       embed_dim: int,
+                       vision_dim: int,
+                       quant_config: Optional[QuantizationConfig] = None,
+                       prefix: str = "") -> nn.Module:
+        raise NotImplementedError
+
+    def get_vision_hidden_states(
+            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
+        raise NotImplementedError
+
+
+class MiniCPMV2_0(MiniCPMVBaseModel):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        assert self.version == (2, 0)
+
+    def init_llm(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> nn.Module:
+        return MiniCPMForCausalLM(vllm_config=vllm_config, prefix=prefix)
+
+    def init_vision_module(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig],
+        prefix: str = "",
+    ) -> nn.Module:
+        # TODO: refactor vision model through timm wrapper from transformers
+        try:
+            import timm
+        except ImportError:
+            raise ImportError("Please install timm==0.9.10") from ImportError
+
+        with set_default_torch_dtype(torch.float16):
+            model = timm.create_model(
+                "vit_so400m_patch14_siglip_384.webli",
+                pretrained=False,
+                num_classes=0,
+                dynamic_img_size=True,
+                dynamic_img_pad=True,
+            )
+
+        model = model.to(dtype=torch.get_default_dtype())
+
+        if (isinstance(model, timm.models.VisionTransformer)
+                and model.attn_pool is not None):
+            model.attn_pool = torch.nn.Identity()
+
+        if self.config.drop_vision_last_layer:
+            model.blocks = model.blocks[:-1]
+
+        return model
+
+    def init_resampler(self,
+                       embed_dim: int,
+                       vision_dim: int,
+                       quant_config: Optional[QuantizationConfig] = None,
+                       prefix: str = "") -> nn.Module:
+        with set_default_torch_dtype(torch.float16):
+            resampler = Resampler2(embed_dim=embed_dim,
+                                   num_heads=embed_dim // 128,
+                                   grid_size=int(
+                                       math.sqrt(self.config.query_num)),
+                                   kv_dim=vision_dim,
+                                   adaptive=False,
+                                   do_post_projection=True,
+                                   quant_config=quant_config,
+                                   prefix=prefix)
+
+        return resampler.to(device=current_platform.device_type,
+                            dtype=torch.get_default_dtype())
+
+    def get_vision_hidden_states(
+            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
+        pixel_values = data["pixel_values"]
+
+        P_h, P_w = self.vpm.patch_embed.patch_size
+        dtype: torch.dtype = self.vpm.pos_embed.data.dtype
+        num_prefix_tokens = getattr(self.vpm, "num_prefix_tokens", 0)
+
+        res = list[torch.Tensor]()
+        for pixel_value in pixel_values:
+            H, W = pixel_value[0].shape[-2:]
+            tgt_size = (math.ceil(H / P_h), math.ceil(W / P_w))
+            vision_embedding = self.vpm.forward_features(
+                pixel_value.unsqueeze(0).type(dtype))
+
+            if num_prefix_tokens > 0:
+                vision_embedding = vision_embedding[:, num_prefix_tokens:]
+            res.append(self.resampler(vision_embedding, tgt_size))
+
+        return torch.vstack(res)
+
+
+class MiniCPMV2_5(MiniCPMVBaseModel, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        assert self.version == (2, 5)
+
+    def init_llm(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> nn.Module:
+        return LlamaForCausalLM(vllm_config=vllm_config, prefix=prefix)
+
+    def init_vision_module(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig],
+        prefix: str = "",
+    ) -> nn.Module:
+        model = Idefics2VisionTransformer(config.vision_config,
+                                          quant_config=quant_config,
+                                          prefix=prefix)
+        if self.config.drop_vision_last_layer:
+            model.encoder.layers = model.encoder.layers[:-1]
+        return model
+
+    def init_resampler(self,
+                       embed_dim: int,
+                       vision_dim: int,
+                       quant_config: Optional[QuantizationConfig] = None,
+                       prefix: str = "") -> nn.Module:
+        with set_default_torch_dtype(torch.float16):
+            resampler = Resampler2_5(num_queries=self.config.query_num,
+                                     embed_dim=embed_dim,
+                                     num_heads=embed_dim // 128,
+                                     kv_dim=vision_dim,
+                                     quant_config=quant_config,
+                                     prefix=prefix)
+
+        return resampler.to(device=current_platform.device_type,
+                            dtype=torch.get_default_dtype())
+
+    def get_vision_hidden_states(
+            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
+        pixel_values = data["pixel_values"]
+        tgt_sizes = data["tgt_sizes"]
+
+        B = len(pixel_values)
+        P = pixel_values[0].shape[-2]
+        L = max(item.shape[-1] for item in pixel_values)
+        device = pixel_values[0].device
+        dtype = pixel_values[0].dtype
+
+        all_pixel_values = torch.zeros((B, 3, P, L),
+                                       dtype=dtype,
+                                       device=device)
+        for i, pixel_values_item in enumerate(pixel_values):
+            L_item = pixel_values_item.shape[-1]
+            all_pixel_values[i, ..., :L_item] = pixel_values_item
+
+        num_patches = tgt_sizes.prod(-1)
+        max_patches = num_patches.max().item()
+        assert isinstance(max_patches, int)
+
+        patch_attn_mask = torch.zeros((B, max_patches),
+                                      dtype=torch.bool,
+                                      device=device)
+        for i, num_patches_item in enumerate(num_patches):
+            patch_attn_mask[i, :num_patches_item] = True
+
+        vision_embedding = self.vpm(
+            all_pixel_values,
+            patch_attention_mask=patch_attn_mask.unsqueeze(1),
+            tgt_sizes=None,
+        )
+
+        return self.resampler(vision_embedding, tgt_sizes)
+
+
+class MiniCPMV2_6(MiniCPMVBaseModel, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        assert self.version == (2, 6)
+
+    def init_llm(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> nn.Module:
+        return Qwen2ForCausalLM(vllm_config=vllm_config, prefix=prefix)
+
+    def init_vision_module(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> nn.Module:
+        model = Idefics2VisionTransformer(config.vision_config,
+                                          quant_config=quant_config,
+                                          prefix=prefix)
+        if self.config.drop_vision_last_layer:
+            model.encoder.layers = model.encoder.layers[:-1]
+        return model
+
+    def init_resampler(self,
+                       embed_dim: int,
+                       vision_dim: int,
+                       quant_config: Optional[QuantizationConfig] = None,
+                       prefix: str = "") -> nn.Module:
+        with set_default_torch_dtype(torch.float16):
+            # The resampler in 2.6 remains consistent with the one in 2.5.
+            resampler = Resampler2_5(num_queries=self.config.query_num,
+                                     embed_dim=embed_dim,
+                                     num_heads=embed_dim // 128,
+                                     kv_dim=vision_dim,
+                                     quant_config=quant_config,
+                                     prefix=prefix)
+
+        return resampler.to(device=current_platform.device_type,
+                            dtype=torch.get_default_dtype())
+
+    def get_vision_hidden_states(
+            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
+        pixel_values = data["pixel_values"]
+        tgt_sizes = data["tgt_sizes"]
+
+        B = len(pixel_values)
+        P = pixel_values[0].shape[-2]
+        L = max(item.shape[-1] for item in pixel_values)
+        device = pixel_values[0].device
+        dtype = pixel_values[0].dtype
+
+        all_pixel_values = torch.zeros((B, 3, P, L),
+                                       dtype=dtype,
+                                       device=device)
+        for i, pixel_values_item in enumerate(pixel_values):
+            L_item = pixel_values_item.shape[-1]
+            all_pixel_values[i, ..., :L_item] = pixel_values_item
+
+        num_patches = tgt_sizes.prod(-1)
+        max_patches = num_patches.max().item()
+        assert isinstance(max_patches, int)
+
+        patch_attn_mask = torch.zeros((B, max_patches),
+                                      dtype=torch.bool,
+                                      device=device)
+        for i, num_patches_item in enumerate(num_patches):
+            patch_attn_mask[i, :num_patches_item] = True
+
+        vision_embedding = self.vpm(
+            all_pixel_values,
+            patch_attention_mask=patch_attn_mask.unsqueeze(1),
+            tgt_sizes=tgt_sizes,
+        )
+
+        return self.resampler(vision_embedding, tgt_sizes)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self,
+                                   skip_prefixes=["apm.", "audio", "tts"])
+        return loader.load_weights(weights)
+
+
+class MiniCPMV4_0(MiniCPMVBaseModel, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        assert self.version == (4, 0)
+
+    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+        if isinstance(quant_config, (AWQConfig, AWQMarlinConfig)):
+            return None
+        return quant_config
+
+    def init_llm(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> nn.Module:
+        return LlamaForCausalLM(vllm_config=vllm_config, prefix=prefix)
+
+    def init_vision_module(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> nn.Module:
+        quant_config = self._maybe_ignore_quant_config(quant_config)
+        model = Idefics2VisionTransformer(config.vision_config,
+                                          quant_config=quant_config,
+                                          prefix=prefix)
+        if self.config.drop_vision_last_layer:
+            model.encoder.layers = model.encoder.layers[:-1]
+        return model
+
+    def init_resampler(
+        self,
+        embed_dim: int,
+        vision_dim: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> nn.Module:
+        quant_config = self._maybe_ignore_quant_config(quant_config)
+        with set_default_torch_dtype(torch.float16):
+            # The resampler in 4.0 remains consistent with the one in 2.5/2.6.
+            resampler = Resampler2_5(num_queries=self.config.query_num,
+                                     embed_dim=embed_dim,
+                                     num_heads=embed_dim // 128,
+                                     kv_dim=vision_dim,
+                                     quant_config=quant_config,
+                                     prefix=prefix)
+
+        return resampler.to(device=current_platform.device_type,
+                            dtype=torch.get_default_dtype())
+
+    def get_vision_hidden_states(
+            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
+        pixel_values = data["pixel_values"]
+        tgt_sizes = data["tgt_sizes"]
+
+        B = len(pixel_values)
+        P = pixel_values[0].shape[-2]
+        L = max(item.shape[-1] for item in pixel_values)
+        device = pixel_values[0].device
+        dtype = pixel_values[0].dtype
+
+        all_pixel_values = torch.zeros((B, 3, P, L),
+                                       dtype=dtype,
+                                       device=device)
+        for i, pixel_values_item in enumerate(pixel_values):
+            L_item = pixel_values_item.shape[-1]
+            all_pixel_values[i, ..., :L_item] = pixel_values_item
+
+        num_patches = tgt_sizes.prod(-1)
+        max_patches = num_patches.max().item()
+        assert isinstance(max_patches, int)
+
+        patch_attn_mask = torch.zeros((B, max_patches),
+                                      dtype=torch.bool,
+                                      device=device)
+        for i, num_patches_item in enumerate(num_patches):
+            patch_attn_mask[i, :num_patches_item] = True
+
+        vision_embedding = self.vpm(
+            all_pixel_values,
+            patch_attention_mask=patch_attn_mask.unsqueeze(1),
+            tgt_sizes=tgt_sizes,
+        )
+
+        return self.resampler(vision_embedding, tgt_sizes)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self,
+                                   skip_prefixes=["apm.", "audio", "tts"])
+        return loader.load_weights(weights)
+
+
+_SUPPORT_VERSION = {
+    (2, 0): MiniCPMV2_0,
+    (2, 5): MiniCPMV2_5,
+    (2, 6): MiniCPMV2_6,
+    (4, 0): MiniCPMV4_0,
+}
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    MiniCPMVMultiModalProcessor,
+    info=MiniCPMVProcessingInfo,
+    dummy_inputs=MiniCPMVDummyInputsBuilder)
+class MiniCPMV(MiniCPMVBaseModel, SupportsMultiModal, SupportsLoRA):
+    """
+    Different versions of MiniCPMV use different visual encoders and LLMs,
+    which is not conducive to the current integration logic of LoRA and
+    bitsandbytes in vLLM. Therefore, it is necessary to separate them.
+    """
+
+    def __new__(cls, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_config
+        if not hasattr(config, "version"):
+            if config.hidden_size == 2304 and config.query_num == 64:
+                version = (2, 0)
+            else:
+                version = (2, 5)
+        else:
+            version = str(config.version).split(".")
+            version = tuple([int(x) for x in version])
+        # Dispatch class based on version
+        instance_cls = _SUPPORT_VERSION.get(version)
+        if instance_cls is None:
+            supported_versions = ", ".join(
+                [f"{v[0]}.{v[1]}" for v in sorted(_SUPPORT_VERSION.keys())])
+            raise ValueError(f"Currently, MiniCPMV only supports versions "
+                             f"{supported_versions}. Got version: {version}")
+
+        # quant_config references base class members,
+        # so update values before init is called
+        cls.packed_modules_mapping.update(instance_cls.packed_modules_mapping)
+        cls.embedding_modules.update(instance_cls.embedding_modules)
+        cls.embedding_padding_modules += instance_cls.embedding_padding_modules
+        return instance_cls(vllm_config=vllm_config, prefix=prefix)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minimax_text_01.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minimax_text_01.py
new file mode 100644
index 0000000000000000000000000000000000000000..82e96844cd5f666d846d770d96f7435f21572771
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minimax_text_01.py
@@ -0,0 +1,1463 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only MiniMaxText01 model."""
+import copy
+import math
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import regex as re
+import torch
+import torch.distributed
+import torch.nn.functional as F
+from einops import rearrange
+from torch import nn
+from transformers import MiniMaxConfig
+
+from vllm import envs
+from vllm.attention import Attention, AttentionMetadata
+from vllm.config import (CacheConfig, ModelConfig, VllmConfig,
+                         get_current_vllm_config)
+from vllm.distributed.communication_op import tensor_model_parallel_all_reduce
+from vllm.distributed.parallel_state import (
+    get_pp_group, get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size)
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.lightning_attn import (
+    lightning_attention, linear_decode_forward_triton)
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mamba.abstract import MambaBase
+from vllm.model_executor.layers.mamba.mamba_utils import (
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.utils import maybe_prefix
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+from vllm.v1.attention.backends.linear_attn import LinearAttentionMetadata
+
+from .interfaces import HasInnerState, IsHybrid
+from .minimax_cache import MinimaxCacheManager, MinimaxCacheParams
+from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
+
+
+def replace_weight_name(name: str,
+                        key: str = None,
+                        to: str = None,
+                        count: int = None,
+                        prefix: str = None) -> str:
+    name = name.replace(key, to) if count is None else \
+        name.replace(key, to, count)
+    return name
+
+
+def weight_loader_with_alias(alias: str):
+
+    def wrapper(func: callable):
+
+        def inner_func(param: torch.Tensor,
+                       loaded_weight: torch.Tensor,
+                       *args,
+                       prefix: str = None,
+                       **kwargs):
+            value = func(param, loaded_weight, *args, **kwargs)
+            return value
+
+        return inner_func
+
+    return wrapper
+
+
+class MiniMaxText01RMSNormTP(CustomOp):
+    name = "MiniMaxText01RMSNormTP"
+
+    def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.tp_world = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+        self.weight = nn.Parameter(torch.ones(int(hidden_size /
+                                                  self.tp_world)))
+
+        self.weight.weight_loader = self.weight_loader
+        self.variance_epsilon = eps
+        return
+
+    @staticmethod
+    def weight_loader(
+        param: nn.Parameter,
+        loaded_weight: torch.Tensor,
+    ) -> None:
+        tp_world = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+
+        shard_size = loaded_weight.shape[0] // tp_world
+        shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
+        param.data.copy_(loaded_weight[shard])
+        return
+
+    def _forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        orig_dtype = x.dtype
+        x = x.to(torch.float32)
+        variance = x.pow(2).mean(dim=-1, keepdim=True, dtype=torch.float32)
+        if self.tp_world > 1:
+            variance = tensor_model_parallel_all_reduce(
+                variance) / self.tp_world
+        x = x * torch.rsqrt(variance + self.variance_epsilon)
+
+        weight = self.weight
+        if x.size(-1) != self.weight.size(0):
+            if self.weight.size(0) < x.size(-1):
+                repeat_count = (x.size(-1) + self.weight.size(0)) // x.size(-1)
+                full_weight = self.weight.repeat(repeat_count)
+                weight = full_weight[:x.size(-1)]
+            else:
+                weight = self.weight[:x.size(-1)]
+
+        x = x.to(orig_dtype) * weight
+        return x
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+        assert residual is None, "RMSNorm does not support residual connection."
+        return self._forward(x)
+
+
+class MiniMaxText01RotaryEmbedding(CustomOp):
+    name = "MiniMaxText01RotaryEmbedding"
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position: int,
+        base: float,
+        is_neox_style: bool,
+        cache_dtype: torch.dtype,
+    ) -> None:
+        super().__init__()
+        self.head_size = head_size
+        self.rotary_dim = rotary_dim
+        self.max_position_embeddings = max_position
+        self.base = base
+        self.is_neox_style = is_neox_style
+        self.cache_dtype = cache_dtype
+        cache = self._compute_cos_sin_cache().to(cache_dtype)
+        self.register_buffer("cos_sin_cache", cache, persistent=False)
+
+    def _compute_inv_freq(self, base: float) -> torch.Tensor:
+        """Compute the inverse frequency."""
+        inv_freq = 1.0 / (base**(torch.arange(
+            0, self.rotary_dim, 2, dtype=torch.float) / self.rotary_dim))
+        return inv_freq
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        """Compute the cos and sin cache."""
+        inv_freq = self._compute_inv_freq(self.base)
+        t = torch.arange(self.max_position_embeddings, dtype=torch.float)
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos()
+        sin = freqs.sin()
+        cache = torch.cat((cos, sin), dim=-1)
+        return cache
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        from vllm import _custom_ops as ops
+        self.cos_sin_cache = self.cos_sin_cache.to(positions.device)
+        query_cast = query.to(self.cache_dtype)
+        key_cast = key.to(self.cache_dtype)
+        ops.rotary_embedding(positions, query_cast, key_cast, self.head_size,
+                             self.cos_sin_cache, self.is_neox_style)
+        query = query_cast.to(query.dtype)
+        key = key_cast.to(key.dtype)
+        return query, key
+
+
+class MiniMaxText01MLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        layer_idx: int = None,
+        prefix: str = "mlp",
+    ) -> None:
+        super().__init__()
+        self.layer_idx = layer_idx
+
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+        self.act_fn = SiluAndMul()
+        return
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class MiniMaxText01MoE(nn.Module):
+
+    def __init__(
+        self,
+        num_experts: int,
+        top_k: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: Optional[torch.dtype] = None,
+        layer_idx: int = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "moe",
+    ) -> None:
+        super().__init__()
+
+        self.layer_idx = layer_idx
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_total_experts = num_experts
+        self.top_k = top_k
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size // self.tp_size
+        self.quant_config = quant_config
+
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        self.params_dtype = params_dtype
+
+        self.gate = ReplicatedLinear(
+            self.hidden_size,
+            self.num_total_experts,
+            bias=False,
+            params_dtype=torch.float32,
+            quant_config=None,
+            prefix=f"{prefix}.gate",
+        )
+        self.gate.weight.weight_loader = MiniMaxText01MoE.gate_weight_loader
+
+        self.experts = FusedMoE(
+            num_experts=self.num_total_experts,
+            top_k=self.top_k,
+            hidden_size=self.hidden_size,
+            intermediate_size=self.intermediate_size * self.tp_size,
+            params_dtype=self.params_dtype,
+            reduce_results=True,
+            renormalize=True,
+            quant_config=self.quant_config,
+            tp_size=self.tp_size,
+            prefix=f"{prefix}.experts",
+        )
+        return
+
+    @staticmethod
+    def gate_weight_loader(param: nn.Parameter,
+                           loaded_weight: torch.Tensor) -> None:
+        assert param.size() == loaded_weight.size()
+        param.data.copy_(loaded_weight.to(torch.float32))
+        return
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_size = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        router_logits_fp32, _ = self.gate(hidden_states.to(torch.float32))
+        final_hidden_states = self.experts(
+            hidden_states, router_logits_fp32.to(hidden_states.dtype))
+        final_hidden = final_hidden_states.view(num_tokens, hidden_size)
+        return final_hidden
+
+
+class MiniMaxText01LinearKernel:
+
+    @staticmethod
+    def jit_linear_forward_prefix(q: torch.Tensor,
+                                  k: torch.Tensor,
+                                  v: torch.Tensor,
+                                  kv_caches: torch.Tensor,
+                                  slope_rate: torch.Tensor,
+                                  block_size: int,
+                                  layer_idx: int = None,
+                                  **kwargs) -> torch.Tensor:
+
+        slope_rate = slope_rate.to(torch.float32)
+        should_pad_dim = q.dim() == 3
+        if should_pad_dim:
+            q = q.unsqueeze(0)
+            k = k.unsqueeze(0)
+            v = v.unsqueeze(0)
+        b, h, n, d = q.shape
+        e = d
+        kv_history = kv_caches.reshape(1, h, d, e).contiguous()
+        output, kv_history = lightning_attention(q,
+                                                 k,
+                                                 v,
+                                                 slope_rate,
+                                                 block_size=block_size,
+                                                 kv_history=kv_history)
+        kv_caches.copy_(kv_history[:, :, -1, :, :].reshape(h, d, e))
+        assert output.shape[0] == 1, "batch size must be 1"
+        return rearrange(output.squeeze(0), "h n d -> n (h d)")
+
+
+class MiniMaxText01LinearAttention(nn.Module, MambaBase):
+
+    @property
+    def mamba_type(self) -> str:
+        return "linear_attention"
+
+    def get_state_dtype(self) -> tuple[torch.dtype]:
+        return MambaStateDtypeCalculator.linear_attention_state_dtype(
+            self.model_config.dtype,
+            self.cache_config.mamba_cache_dtype,
+        )
+
+    def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]:
+        return MambaStateShapeCalculator.linear_attention_state_shape(
+            num_heads=self.num_heads,
+            tp_size=self.tp_size,
+            head_dim=self.head_dim)
+
+    def __init__(
+        self,
+        hidden_size: int,
+        hidden_inner_size: int,
+        num_heads: int,
+        head_dim: int,
+        max_position: int,
+        block_size: int,
+        num_hidden_layer: int,
+        model_config: Optional[ModelConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        layer_idx: int = 0,
+        linear_layer_idx: int = 0,
+        prefix: str = "linear_attn",
+    ) -> None:
+        super().__init__()
+
+        self.layer_idx = layer_idx
+        self.BLOCK = block_size
+        self.hidden_size = hidden_size
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.total_num_heads = num_heads
+        self.hidden_inner_size = hidden_inner_size
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+
+        assert self.total_num_heads % self.tp_size == 0
+        self.tp_heads = self.total_num_heads // self.tp_size
+        self.qkv_size = self.num_heads * self.head_dim
+        self.tp_hidden = self.head_dim * self.tp_heads
+        self.model_config = model_config
+        self.cache_config = cache_config
+        self.prefix = prefix
+
+        self.qkv_proj = ColumnParallelLinear(
+            hidden_size,
+            self.hidden_inner_size * 3,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.output_gate = ColumnParallelLinear(
+            hidden_size,
+            self.hidden_inner_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.output_gate",
+        )
+        self.out_proj = RowParallelLinear(
+            self.hidden_inner_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+        self.norm = MiniMaxText01RMSNormTP(
+            self.hidden_inner_size,
+            eps=1e-5,
+        )
+
+        slope_rate = MiniMaxText01LinearAttention._build_slope_tensor(
+            self.num_heads)
+        if num_hidden_layer <= 1:
+            self.slope_rate = slope_rate * (1 + 1e-5)
+        else:
+            self.slope_rate = slope_rate * (1 - layer_idx /
+                                            (num_hidden_layer - 1) + 1e-5)
+        self.tp_slope = self.slope_rate[self.tp_rank *
+                                        self.tp_heads:(self.tp_rank + 1) *
+                                        self.tp_heads].contiguous()
+
+        if envs.VLLM_USE_V1:
+            compilation_config = get_current_vllm_config().compilation_config
+            if prefix in compilation_config.static_forward_context:
+                raise ValueError(f"Duplicate layer name: {prefix}")
+            compilation_config.static_forward_context[prefix] = self
+
+    @staticmethod
+    def weight_direct_load(param: torch.Tensor,
+                           loaded_weight: torch.Tensor) -> None:
+        assert param.size() == loaded_weight.size()
+        param.data.copy_(loaded_weight)
+        return
+
+    @staticmethod
+    def _build_slope_tensor(n_attention_heads: int):
+
+        def get_slopes(n):
+
+            def get_slopes_power_of_2(n):
+                start = 2**(-(2**-(math.log2(n) - 3)))
+                ratio = start
+                return [start * ratio**i for i in range(n)]
+
+            if math.log2(n).is_integer():
+                return get_slopes_power_of_2(n)
+            else:
+                closest_power_of_2 = 2**math.floor(math.log2(n))
+                return (get_slopes_power_of_2(closest_power_of_2) + get_slopes(
+                    2 * closest_power_of_2)[0::2][:n - closest_power_of_2])
+
+        slopes = torch.tensor(get_slopes(n_attention_heads),
+                              dtype=torch.float32).reshape(
+                                  n_attention_heads, 1, 1)
+        return slopes
+
+    def _prefill_and_mix_infer(self, q, k, v, kv_cache, state_indices_tensor,
+                               attn_metadata):
+        hidden = []
+        for _prefill_idx in range(getattr(attn_metadata, "num_prefills", 0)):
+            if _prefill_idx >= len(attn_metadata.query_start_loc):
+                break
+            if _prefill_idx >= len(state_indices_tensor):
+                break
+            # prefills are packed at end of batch in V1
+            offset = attn_metadata.num_decode_tokens if envs.VLLM_USE_V1 else 0
+            _start = attn_metadata.query_start_loc[offset + _prefill_idx]
+            _end = attn_metadata.query_start_loc[offset + _prefill_idx + 1]
+            slot_id = state_indices_tensor[offset + _prefill_idx]
+            qs = q[_start:_end].transpose(0, 1).contiguous()
+            ks = k[_start:_end].transpose(0, 1).contiguous()
+            vs = v[_start:_end].transpose(0, 1).contiguous()
+            slice_layer_cache = kv_cache[slot_id, ...]
+
+            out_slice = MiniMaxText01LinearKernel.jit_linear_forward_prefix(
+                qs,
+                ks,
+                vs,
+                slice_layer_cache,
+                self.tp_slope,
+                self.BLOCK,
+                layer_idx=self.layer_idx)
+            hidden.append(out_slice.contiguous())
+        if attn_metadata.num_decode_tokens > 0:
+            hidden_decode = self._decode_infer(q, k, v, kv_cache,
+                                               state_indices_tensor,
+                                               attn_metadata)
+            if envs.VLLM_USE_V1:
+                hidden.insert(0, hidden_decode)
+            else:
+                hidden.append(hidden_decode)
+
+        if not hidden:
+            return torch.empty((0, q.size(-1)), device=q.device, dtype=q.dtype)
+
+        hidden = torch.concat(hidden, dim=0).contiguous()
+        return hidden
+
+    def _decode_infer(self, q, k, v, kv_cache, state_indices_tensor,
+                      attn_metadata):
+        if not envs.VLLM_USE_V1:
+            q = q[attn_metadata.num_prefill_tokens:].unsqueeze(2).contiguous()
+            k = k[attn_metadata.num_prefill_tokens:].unsqueeze(2).contiguous()
+            v = v[attn_metadata.num_prefill_tokens:].unsqueeze(2).contiguous()
+            num_prefills = getattr(attn_metadata, "num_prefills", 0)
+            slot_id = state_indices_tensor[num_prefills:]
+        else:
+            q = q[:attn_metadata.num_decode_tokens].unsqueeze(2).contiguous()
+            k = k[:attn_metadata.num_decode_tokens].unsqueeze(2).contiguous()
+            v = v[:attn_metadata.num_decode_tokens].unsqueeze(2).contiguous()
+            slot_id = state_indices_tensor[:attn_metadata.num_decodes]
+        hidden = linear_decode_forward_triton(q, k, v, kv_cache, self.tp_slope,
+                                              slot_id, 32)
+        return hidden
+
+    def forward(self, hidden_states: torch.Tensor, positions: torch.Tensor,
+                kv_caches: MinimaxCacheParams, **kwargs) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        qkv32 = qkv.to(torch.float32)
+        qkvact = torch.nn.functional.silu(qkv32)
+        qkvact = qkvact.view((qkv.shape[0], self.tp_heads, -1))
+        q, k, v = torch.split(qkvact, [self.head_dim] * 3, dim=-1)
+        forward_context = get_forward_context()
+        attn_metadata = forward_context.attn_metadata
+        if envs.VLLM_USE_V1:
+            if attn_metadata is not None:
+                assert isinstance(attn_metadata, dict)
+                attn_metadata = attn_metadata[self.prefix]
+                assert isinstance(attn_metadata, LinearAttentionMetadata)
+                kv_cache = self.kv_cache[forward_context.virtual_engine][0]
+                state_indices_tensor = attn_metadata.state_indices_tensor
+
+                num_prefills = getattr(attn_metadata, "num_prefills", 0)
+                if num_prefills > 0:
+                    num_decode_tokens = getattr(attn_metadata,
+                                                "num_decode_tokens", 0)
+                    for prefill_idx in range(num_prefills):
+                        q_start = attn_metadata.query_start_loc[
+                            num_decode_tokens + prefill_idx]
+                        q_end = attn_metadata.query_start_loc[num_decode_tokens
+                                                              + prefill_idx +
+                                                              1]
+                        query_len = q_end - q_start
+                        context_len = attn_metadata.seq_lens[
+                            num_decode_tokens + prefill_idx] - query_len
+                        if context_len == 0:
+                            block_to_clear = state_indices_tensor[
+                                num_decode_tokens + prefill_idx]
+                            kv_cache[block_to_clear, ...] = 0
+        else:
+            kv_cache = kv_caches.minimax_cache
+            state_indices_tensor = kv_caches.state_indices_tensor
+
+        decode_only = getattr(attn_metadata, "num_prefills", 0) == 0
+        if attn_metadata is None:
+            hidden = torch.empty((q.shape[0], q.shape[1] * q.shape[2]),
+                                 device=q.device,
+                                 dtype=q.dtype)
+        else:
+            if not decode_only:
+                hidden = self._prefill_and_mix_infer(q, k, v, kv_cache,
+                                                     state_indices_tensor,
+                                                     attn_metadata)
+            else:
+                hidden = self._decode_infer(q, k, v, kv_cache,
+                                            state_indices_tensor,
+                                            attn_metadata)
+
+        hidden = self.norm._forward(hidden)
+        gate, _ = self.output_gate(hidden_states)
+        hidden = F.sigmoid(gate) * hidden
+        hidden = hidden.to(hidden_states.dtype)
+        hidden, _ = self.out_proj(hidden)
+        return hidden
+
+
+class MiniMaxText01Attention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        head_dim: int,
+        num_kv_heads: int,
+        rotary_dim: int,
+        max_position: int = 4096 * 32,
+        rope_theta: float = 10000,
+        sliding_window: Optional[int] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        layer_idx: int = None,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "mha",
+    ) -> None:
+        super().__init__()
+        self.layer_idx = layer_idx
+
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = head_dim
+
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.sliding_window = sliding_window
+        self.prefix = prefix
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+        return
+
+    def forward(self, hidden_states: torch.Tensor, positions: torch.Tensor,
+                **kwargs) -> torch.Tensor:
+        forward_context = get_forward_context()
+        attn_metadata = forward_context.attn_metadata
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if envs.VLLM_USE_V1:
+            if attn_metadata is not None:
+                q, k = attn_metadata[f"{self.prefix}.attn"].rotary_emb(
+                    positions, q, k)
+        else:
+            q, k = attn_metadata.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MiniMaxText01DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: MiniMaxConfig,
+        model_config: Optional[ModelConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        expert_num: int = 1,
+        layer_id: int = None,
+        linear_layer_id: Optional[int] = None,
+        prefix: str = "decoder",
+    ) -> None:
+        self._ilayer = layer_id
+        self._irank = get_tensor_model_parallel_rank()
+        self.prefix = prefix
+        super().__init__()
+
+        self.hidden_size = config.hidden_size
+        self.expert_num = expert_num
+
+        rope_theta = getattr(config, "rope_theta", 10000)
+
+        head_dim = getattr(config, "head_dim", None)
+        if head_dim is None:
+            head_dim = config.hidden_size // config.num_attention_heads
+        if hasattr(config, "max_model_len") and isinstance(
+                config.max_model_len, int):
+            max_position_embeddings = min(config.max_position_embeddings,
+                                          config.max_model_len)
+        if config.attention_type == 0:
+            use_headxdim = True
+            hidden_inner = (head_dim * config.num_attention_heads
+                            if use_headxdim else config.hidden_size)
+            self.self_attn = MiniMaxText01LinearAttention(
+                hidden_size=self.hidden_size,
+                hidden_inner_size=hidden_inner,
+                num_heads=config.num_attention_heads,
+                head_dim=head_dim,
+                max_position=max_position_embeddings,
+                block_size=config.block if hasattr(config, "block") else 256,
+                num_hidden_layer=config.num_hidden_layers,
+                model_config=model_config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                layer_idx=self._ilayer,
+                linear_layer_idx=linear_layer_id,
+                prefix=prefix)
+        elif config.attention_type == 1:
+            self.self_attn = MiniMaxText01Attention(
+                hidden_size=self.hidden_size,
+                num_heads=config.num_attention_heads,
+                head_dim=head_dim,
+                rotary_dim=config.rotary_dim
+                if hasattr(config, "rotary_dim") else head_dim,
+                num_kv_heads=config.num_key_value_heads,
+                max_position=max_position_embeddings,
+                rope_theta=rope_theta,
+                sliding_window=config.sliding_window,
+                quant_config=quant_config,
+                layer_idx=self._ilayer,
+                cache_config=cache_config,
+                prefix=prefix)
+        else:
+            raise ValueError(
+                f"Unsupported attention type: {self.config.attention_type}")
+
+        if expert_num == 1:
+            self.mlp = MiniMaxText01MLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=config.intermediate_size,
+                quant_config=quant_config,
+                layer_idx=self._ilayer,
+                prefix=prefix)
+        else:
+            self.block_sparse_moe = MiniMaxText01MoE(
+                num_experts=expert_num,
+                top_k=config.num_experts_per_tok,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                layer_idx=self._ilayer,
+                quant_config=quant_config,
+                prefix=prefix)
+
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+        if config.attention_type == 0:
+            self.layernorm_attention_alpha = getattr(
+                config, 'layernorm_linear_attention_alpha',
+                getattr(config, 'linear_attn_alpha_factor', 1))
+            self.layernorm_attention_beta = getattr(
+                config, 'layernorm_linear_attention_beta',
+                getattr(config, 'linear_attn_beta_factor', 1))
+        else:
+            self.layernorm_attention_alpha = getattr(
+                config, 'layernorm_full_attention_alpha',
+                getattr(config, 'full_attn_alpha_factor', 1))
+            self.layernorm_attention_beta = getattr(
+                config, 'layernorm_full_attention_beta',
+                getattr(config, 'full_attn_beta_factor', 1))
+        self.layernorm_mlp_alpha = getattr(
+            config, 'layernorm_mlp_alpha',
+            getattr(config, 'mlp_alpha_factor', 1))
+        self.layernorm_mlp_beta = getattr(
+            config, 'layernorm_mlp_beta', getattr(config, 'mlp_beta_factor',
+                                                  1))
+        self.postnorm = getattr(config, 'postnorm', False)
+        self.shared_moe = False
+
+        shared_intermediate = getattr(config, 'shared_intermediate_size', 0)
+        if isinstance(shared_intermediate, list):
+            shared_intermediate = shared_intermediate[
+                layer_id] if layer_id < len(shared_intermediate) else 0
+        if shared_intermediate > 0:
+            self.shared_moe = True
+            self.shared_mlp = MiniMaxText01MLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=shared_intermediate,
+                quant_config=quant_config,
+                layer_idx=self._ilayer,
+                prefix=prefix)
+            self.coefficient = ReplicatedLinear(
+                self.hidden_size,
+                1,
+                bias=False,
+                quant_config=quant_config,
+                params_dtype=torch.float32,
+            )
+            self.coefficient.weight.weight_loader = (
+                self.shared_moe_coefficient_loader)
+            self.shared_moe_mode = getattr(config, 'shared_moe_mode',
+                                           'softmax')
+        return
+
+    def forward(self,
+                hidden_states: torch.Tensor,
+                positions: torch.Tensor,
+                kv_caches: Union[list[dict], Optional[torch.Tensor]],
+                attn_metadata: AttentionMetadata,
+                residual: Optional[torch.Tensor],
+                is_warmup: bool = False,
+                **kwargs) -> tuple[torch.Tensor, torch.Tensor]:
+
+        forward_context = get_forward_context()
+        attn_metadata = forward_context.attn_metadata
+        layernorm_input = hidden_states
+        layernorm_output = self.input_layernorm(layernorm_input)
+        residual = layernorm_output if self.postnorm else layernorm_input
+        self_attention_output = self.self_attn(
+            hidden_states=layernorm_output,
+            positions=positions,
+            kv_caches=kv_caches,
+            attn_metadata=attn_metadata,
+        )
+
+        residual = residual * self.layernorm_attention_alpha
+        self_attention_output = (self_attention_output *
+                                 self.layernorm_attention_beta)
+
+        layernorm_input = residual + self_attention_output
+        layernorm_output = self.post_attention_layernorm(layernorm_input)
+        residual = layernorm_output if self.postnorm else layernorm_input
+
+        if self.expert_num == 1:
+            hidden_states = self.mlp(layernorm_output)
+        else:
+            moe_hidden_states = self.block_sparse_moe(
+                copy.deepcopy(layernorm_output))
+            if self.shared_moe:
+                before_moe_dtype = layernorm_output.dtype
+                moe_hidden_fp32 = moe_hidden_states.to(torch.float32)
+                output_mlp = self.shared_mlp(layernorm_output).to(
+                    torch.float32)
+
+                coef, _ = self.coefficient(layernorm_output.to(torch.float32))
+
+                if self.shared_moe_mode == 'softmax':
+                    coef = torch.nn.functional.softmax(coef, dim=-1)
+                    hidden_states = moe_hidden_fp32 * (
+                        1 - coef) + output_mlp * coef
+                elif self.shared_moe_mode == 'sigmoid':
+                    coef = torch.nn.functional.sigmoid(coef)
+                    hidden_states = moe_hidden_fp32 * (
+                        1 - coef) + output_mlp * coef
+
+                hidden_states = hidden_states.to(before_moe_dtype)
+            else:
+                hidden_states = moe_hidden_states
+
+        residual = residual * self.layernorm_mlp_alpha
+        hidden_states = hidden_states * self.layernorm_mlp_beta
+
+        hidden_states = residual + hidden_states
+
+        return hidden_states, None
+
+    @staticmethod
+    def shared_moe_coefficient_loader(param: torch.Tensor,
+                                      loaded_weight: torch.Tensor) -> None:
+        assert param.size() == loaded_weight.size()
+
+        param.data.copy_(loaded_weight.to(torch.float32))
+        return
+
+
+class MiniMaxText01Model(nn.Module):
+
+    def __init__(
+        self,
+        config: MiniMaxConfig,
+        model_config: Optional[ModelConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+        scheduler_config=None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.decoder_attention_types = getattr(
+            config, "attn_type_list", False) or getattr(
+                config, "decoder_attention_types", False)
+        # The HF format uses "layer_types" instead of "attn_type_list"
+        # where "linear_attention" is 0 and "full_attention" is 1
+        if not self.decoder_attention_types and hasattr(config, "layer_types"):
+            self.decoder_attention_types = []
+            for layer_type in config.layer_types:
+                if layer_type == "linear_attention":
+                    self.decoder_attention_types.append(0)
+                elif layer_type == "full_attention":
+                    self.decoder_attention_types.append(1)
+                else:
+                    raise ValueError(f"Unsupported layer type: {layer_type}")
+        # Default to full attention
+        if not self.decoder_attention_types:
+            self.decoder_attention_types = [1] * config.num_hidden_layers
+        self.num_layers = config.num_hidden_layers
+
+        self._layer_barrier = False
+        if get_pp_group().is_first_rank:
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=self.vocab_size,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        def layer_fn(prefix):
+            layer_idx = int(prefix.split('.')[-1])
+            layer_config = config
+            layer_config.attention_type = self.decoder_attention_types[
+                layer_idx]
+            layer_config.layer_idx = layer_idx
+
+            decoder_kwargs = {
+                "quant_config": quant_config,
+                "layer_id": layer_idx,
+                "model_config": model_config,
+                "cache_config": cache_config
+            }
+
+            if layer_config.attention_type == 0:
+                decoder_kwargs["linear_layer_id"] = sum(
+                    1 for i in range(layer_idx)
+                    if self.decoder_attention_types[i] == 0)
+            else:
+                decoder_kwargs["linear_layer_id"] = None
+
+            if hasattr(config, "num_local_experts") and isinstance(
+                    config.num_local_experts, list):
+                decoder_kwargs["expert_num"] = config.num_local_experts[
+                    layer_idx]
+            elif hasattr(config, "num_local_experts") and isinstance(
+                    config.num_local_experts, int):
+                decoder_kwargs["expert_num"] = config.num_local_experts
+            else:
+                decoder_kwargs["expert_num"] = 1
+
+            return MiniMaxText01DecoderLayer(layer_config,
+                                             **decoder_kwargs,
+                                             prefix=prefix)
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers, layer_fn, prefix=f"{prefix}.layers")
+
+        linear_layer_nums = sum(1 for i in range(config.num_hidden_layers)
+                                if self.decoder_attention_types[i] == 0)
+        max_slots_number = scheduler_config.max_num_seqs
+        self.cache_shape = (linear_layer_nums, max_slots_number,
+                            config.num_attention_heads //
+                            get_tensor_model_parallel_world_size(),
+                            config.head_dim, config.head_dim)
+        _dummy = torch.zeros(1)
+        self._dtype = _dummy.dtype
+        del _dummy
+
+        if not envs.VLLM_USE_V1:
+            self.minimax_cache = MinimaxCacheManager(
+                dtype=torch.float32, cache_shape=self.cache_shape)
+
+        rope_theta = getattr(config, "rope_theta", 10000)
+        head_dim = getattr(config, "head_dim", None)
+        if head_dim is None:
+            head_dim = config.hidden_size // config.num_attention_heads
+        if hasattr(config, "max_model_len") and isinstance(
+                config.max_model_len, int):
+            max_position_embeddings = min(config.max_position_embeddings,
+                                          config.max_model_len)
+        self.rotary_emb = MiniMaxText01RotaryEmbedding(
+            head_dim,
+            rotary_dim=config.rotary_dim
+            if hasattr(config, "rotary_dim") else head_dim,
+            max_position=max_position_embeddings,
+            base=int(rope_theta),
+            is_neox_style=True,
+            cache_dtype=torch.float32,
+        )
+
+        norm_kwargs = {}
+        if hasattr(config, "rms_norm_eps"):
+            norm_kwargs["eps"] = config.rms_norm_eps
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, **norm_kwargs)
+        else:
+            self.norm = PPMissingLayer()
+        self.embed_scale = 1.0
+        return
+
+    def _clear_prefill_cache(self, attn_metadata,
+                             minimax_cache_tensors: torch.Tensor, **kwargs):
+        seq_to_slot_maps = {}
+        seq_id_map = sum(list(kwargs["request_ids_to_seq_ids"].values()), [])
+        for _, seq_to_slot_map in (
+                self.minimax_cache.cache_indices_mapping.items()):
+            seq_to_slot_maps.update(seq_to_slot_map)
+
+        slots_to_clear = []
+        for _prefill_id in range(getattr(attn_metadata, "num_prefills", 0)):
+            if _prefill_id >= len(seq_id_map):
+                break
+            seq_id = seq_id_map[_prefill_id]
+            if attn_metadata.context_lens_tensor[
+                    _prefill_id] == 0 and seq_id in seq_to_slot_maps:
+                slots_to_clear.append(seq_to_slot_maps[seq_id])
+
+        if slots_to_clear:
+            slots_tensor = torch.tensor(slots_to_clear,
+                                        device=minimax_cache_tensors.device,
+                                        dtype=torch.long)
+            minimax_cache_tensors[:, slots_tensor, ...] = 0
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(self,
+                input_ids: Optional[torch.Tensor],
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs) -> Union[torch.Tensor, IntermediateTensors]:
+        forward_context = get_forward_context()
+        attn_metadata = forward_context.attn_metadata
+        if not envs.VLLM_USE_V1 and attn_metadata is None:
+            return None
+        if "request_ids_to_seq_ids" not in kwargs:
+            kwargs["request_ids_to_seq_ids"] = {}
+        if "finished_requests_ids" not in kwargs:
+            kwargs["finished_requests_ids"] = []
+
+        if not envs.VLLM_USE_V1:
+            (
+                minimax_cache_tensors,
+                state_indices_tensor,
+            ) = self.minimax_cache.current_run_tensors(**kwargs)
+            if getattr(attn_metadata, "num_prefills", 0) > 0:
+                self._clear_prefill_cache(attn_metadata, minimax_cache_tensors,
+                                          **kwargs)
+
+            minimax_cache_params = MinimaxCacheParams(minimax_cache_tensors,
+                                                      state_indices_tensor)
+        else:
+            minimax_cache_params = None
+
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is None:
+                hidden_states = self.embed_scale * self.embed_tokens(input_ids)
+            else:
+                hidden_states = inputs_embeds
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        minimax_cache_index = 0
+
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            if attn_metadata is not None:
+                # TODO (tdoublep): this whole thing with the rotary_emb is
+                # weird. we shouldn't be passing it via attn_metadata imo.
+                if envs.VLLM_USE_V1:
+                    if isinstance(layer.self_attn, MiniMaxText01Attention):
+                        attn_metadata[layer.prefix +
+                                      ".attn"].rotary_emb = self.rotary_emb
+                else:
+                    attn_metadata.rotary_emb = self.rotary_emb
+
+            _caches = None
+            if not envs.VLLM_USE_V1 and isinstance(
+                    layer.self_attn, MiniMaxText01LinearAttention):
+                current_state_layer = minimax_cache_index
+                _caches = minimax_cache_params.at_layer_idx(
+                    current_state_layer)
+                minimax_cache_index += 1
+            hidden_states, residual = layer(
+                hidden_states=hidden_states,
+                positions=positions,
+                kv_caches=_caches,
+                attn_metadata=attn_metadata,
+                residual=residual,
+            )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        if residual is not None:
+            hidden_states, _ = self.norm(hidden_states, residual)
+        else:
+            hidden_states = self.norm(hidden_states)
+
+        return hidden_states
+
+
+class MiniMaxText01ForCausalLM(nn.Module, HasInnerState, IsHybrid):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.lora_config = lora_config
+
+        if not hasattr(config, "sliding_window"):
+            config.sliding_window = None
+
+        self.CONCAT_FFN = True
+
+        self.unpadded_vocab_size = self.config.vocab_size
+        if hasattr(vllm_config.model_config, "max_model_len"):
+            self.config.max_model_len = vllm_config.model_config.max_model_len
+        self.model = MiniMaxText01Model(
+            self.config,
+            model_config=vllm_config.model_config,
+            cache_config=vllm_config.cache_config,
+            quant_config=quant_config,
+            scheduler_config=vllm_config.scheduler_config,
+            prefix=maybe_prefix(prefix, "model"))
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                self.config.hidden_size,
+                org_num_embeddings=self.config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+            )
+
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    self.config.vocab_size)
+
+        else:
+            self.lm_head = PPMissingLayer()
+        self.lm_head.float()
+        flash_layer_count = sum(
+            1 for attn_type in self.model.decoder_attention_types
+            if attn_type == 1)
+        self.kv_cache = [torch.tensor([]) for _ in range(flash_layer_count)]
+        return
+
+    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
+        return self.model.minimax_cache.copy_inputs_before_cuda_graphs(
+            input_buffers, **kwargs)
+
+    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
+        return self.model.minimax_cache.get_seqlen_agnostic_capture_inputs(
+            batch_size)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds, **kwargs)
+
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states.float(),
+                                       sampling_metadata)
+
+        return logits
+
+    def make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+            "residual":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        def which_layer(name: str) -> int:
+            if "layers" in name:
+                after_layer = name.split("layers")[-1]
+                return int(after_layer.split(".")[1])
+            return None
+
+        def is_linear_attn_layer(layer_idx: int) -> bool:
+            if layer_idx is None or layer_idx >= len(
+                    self.model.decoder_attention_types):
+                return False
+            return self.model.decoder_attention_types[layer_idx] == 0
+
+        def is_moe_weight(name: str) -> bool:
+            return "block_sparse_moe" in name and not name.endswith(".bias")
+
+        def get_expert_id(param_name):
+            pattern = r'model\.layers\.\d+\.block_sparse_moe\.experts\.(\d+)\.'
+            match = re.search(pattern, param_name)
+            if match:
+                return match.group(1)
+            return None
+
+        def load_sparse_moe_weight(name: str, loaded_weight: torch.Tensor,
+                                   self) -> None:
+            if isinstance(self.config.num_local_experts, list):
+                expert_params_mapping = [
+                    ("w13_weight"
+                     if weight_name in ["w1", "w3"] else "w2_weight",
+                     f"experts.{expert_id}.{weight_name}.weight", expert_id)
+                    for expert_id in range(max(self.config.num_local_experts))
+                    for weight_name in ["w1", "w2", "w3"]
+                ]
+            else:
+                expert_params_mapping = [
+                    ("w13_scale" if weight_name in ["w1", "w3"] else
+                     "w2_scale", f"{expert_id}.{weight_name}.weight_scale",
+                     expert_id, weight_name)
+                    for expert_id in range(self.config.num_local_experts)
+                    for weight_name in ["w1", "w2", "w3"]
+                ] + [("w13_weight" if weight_name in ["w1", "w3"] else
+                      "w2_weight", f"{expert_id}.{weight_name}.weight",
+                      expert_id, weight_name)
+                     for expert_id in range(self.config.num_local_experts)
+                     for weight_name in ["w1", "w2", "w3"]]
+            for (param_name, weight_name, expert_id,
+                 shard_id) in expert_params_mapping:
+                name_expert_id = get_expert_id(name)
+                if name_expert_id is not None and int(name_expert_id) != int(
+                        expert_id):
+                    continue
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if is_pp_missing_parameter(name, self):
+                    return
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader = weight_loader_with_alias(name)(weight_loader)
+                weight_loader(param,
+                              loaded_weight,
+                              weight_name,
+                              expert_id=expert_id,
+                              shard_id=shard_id)
+                loaded_params.add(name)
+                break
+            else:
+                if is_pp_missing_parameter(name, self):
+                    return
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader = weight_loader_with_alias(name)(weight_loader)
+                weight_loader(param, loaded_weight)
+                loaded_params.add(name)
+            return
+
+        def is_shared_mlp_weight(name: str) -> bool:
+            return "shared_mlp" in name and not name.endswith(".bias")
+
+        def load_shared_mlp_weight(name: str, loaded_weight: torch.Tensor,
+                                   self) -> None:
+            if not self.CONCAT_FFN:
+                if "gate_proj" in name:
+                    name = name.replace("gate_proj", "w1", 1)
+                elif "up_proj" in name:
+                    name = name.replace("up_proj", "w3", 1)
+                elif "down_proj" in name:
+                    name = name.replace("down_proj", "w2", 1)
+            else:
+                if "gate_proj" in name:
+                    name = name.replace("gate_proj", "gate_up_proj", 1)
+                    loaded_shard_id = 0
+                elif "up_proj" in name:
+                    name = name.replace("up_proj", "gate_up_proj", 1)
+                    loaded_shard_id = 1
+            if is_pp_missing_parameter(name, self):
+                return
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader = weight_loader_with_alias(name)(weight_loader)
+            if not self.CONCAT_FFN:
+                weight_loader(param, loaded_weight)
+            else:
+                if "gate_up_proj" in name:
+                    weight_loader(param, loaded_weight, loaded_shard_id)
+                elif "down_proj" in name:
+                    weight_loader(param, loaded_weight)
+                else:
+                    raise AssertionError(
+                        "MLP weight not in [gate_up_proj, down_proj]")
+            loaded_params.add(name)
+            return
+
+        def is_mha_weight(name: str) -> bool:
+            return "self_attn" in name and not name.endswith(".bias")
+
+        def load_linear_attn_weight(name: str, loaded_weight: torch.Tensor,
+                                    self) -> None:
+            if is_pp_missing_parameter(name, self):
+                return
+            param = params_dict[name]
+
+            weight_loader = getattr(
+                param, "weight_loader",
+                MiniMaxText01LinearAttention.weight_direct_load)
+            weight_loader = weight_loader_with_alias(name)(weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+            return
+
+        def load_flash_attn_weight(name: str, loaded_weight: torch.Tensor,
+                                   self) -> None:
+
+            flash_mha_params_mapping = [
+                ("qkv_proj", "q_proj", "q"),
+                ("qkv_proj", "k_proj", "k"),
+                ("qkv_proj", "v_proj", "v"),
+                ("gate_up_proj", "gate_proj", 0),
+                ("gate_up_proj", "up_proj", 1),
+            ]
+            for (param_name, weight_name,
+                 shard_id) in flash_mha_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if is_pp_missing_parameter(name, self):
+                    return
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader = weight_loader_with_alias(name)(weight_loader)
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name)
+                break
+            else:
+                if is_pp_missing_parameter(name, self):
+                    return
+                param = params_dict[name]
+
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader = weight_loader_with_alias(name)(weight_loader)
+                weight_loader(param, loaded_weight)
+                loaded_params.add(name)
+            return
+
+        def is_layer_norm_weight(name: str) -> bool:
+            return "norm" in name and not name.endswith(
+                ".bias") and name in params_dict
+
+        def load_layer_norm_weight(name: str, loaded_weight: torch.Tensor,
+                                   self) -> None:
+            if is_pp_missing_parameter(name, self):
+                return
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader = weight_loader_with_alias(name)(weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+            return
+
+        def load_basic_weight(name: str, loaded_weight: torch.Tensor,
+                              self) -> None:
+            if is_pp_missing_parameter(name, self):
+                return
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader = weight_loader_with_alias(name)(weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+            return
+
+        for name, loaded_weight in weights:
+            weight_at_layer = which_layer(name)
+            if weight_at_layer and weight_at_layer >= len(
+                    self.model.decoder_attention_types):
+                continue
+
+            if is_layer_norm_weight(name):
+                load_layer_norm_weight(name, loaded_weight, self)
+                continue
+            if is_mha_weight(name):
+                if is_linear_attn_layer(weight_at_layer):
+                    load_linear_attn_weight(name, loaded_weight, self)
+                else:
+                    load_flash_attn_weight(name, loaded_weight, self)
+                continue
+            if is_moe_weight(name):
+                load_sparse_moe_weight(name, loaded_weight, self)
+                continue
+            if is_shared_mlp_weight(name):
+                load_shared_mlp_weight(name, loaded_weight, self)
+                continue
+
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            load_basic_weight(name, loaded_weight, self)
+        return loaded_params
+
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.linear_attention_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+        )
+
+    @classmethod
+    def get_mamba_state_shape_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+        use_v1: bool = True,
+    ) -> tuple[tuple[int, ...], ...]:
+        """Calculate shape for MiniMaxText01LinearAttention cache.
+
+        Args:
+            vllm_config: vLLM config
+            use_v1: Get shapes for V1 (or V0)
+
+        Returns:
+            Tuple containing:
+            - state_shape: Shape of the cache
+        """
+        parallel_config = vllm_config.parallel_config
+        hf_config = vllm_config.model_config.hf_config
+
+        return MambaStateShapeCalculator.linear_attention_state_shape(
+            num_heads=hf_config.num_attention_heads,
+            tp_size=parallel_config.tensor_parallel_size,
+            head_dim=hf_config.head_dim,
+        )
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minimax_vl_01.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minimax_vl_01.py
new file mode 100644
index 0000000000000000000000000000000000000000..8107c6e8a04a192c3813898843ea9bbb0bccc2ba
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/minimax_vl_01.py
@@ -0,0 +1,373 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterable, Mapping
+from typing import Literal, Optional, TypedDict, Union, cast
+
+import torch
+import torch.nn as nn
+from transformers import BatchFeature, PretrainedConfig
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalFieldConfig
+from vllm.sequence import IntermediateTensors
+from vllm.utils.jsontree import json_map_leaves
+
+from .clip import CLIPVisionModel
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .llava import (BaseLlavaMultiModalProcessor, LlavaDummyInputsBuilder,
+                    init_vision_tower_for_llava)
+from .llava_next import LlavaNextProcessingInfo
+from .pixtral import PixtralHFVisionModel
+from .siglip import SiglipVisionModel
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
+                    maybe_prefix, merge_multimodal_embeddings)
+
+
+class MiniMaxVL01ImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values: torch.Tensor
+    """
+    Shape: `(batch_size * num_images, num_channels, height, width)`
+
+    Note that `height` or `width` may be different per batch and image,
+    in which case the data is passed as a list instead of a batched tensor.
+    """
+
+
+class MiniMaxVL01ImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: torch.Tensor
+    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
+
+    `hidden_size` must match the hidden size of language model backbone.
+    """
+
+
+MiniMaxVL01ImageInputs = Union[MiniMaxVL01ImagePixelInputs,
+                               MiniMaxVL01ImageEmbeddingInputs]
+
+
+class MiniMaxVL01MultiModalProjector(nn.Module):
+
+    def __init__(self,
+                 vision_hidden_size: int,
+                 text_hidden_size: int,
+                 projector_hidden_act: str,
+                 multimodal_projector_bias: bool,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+
+        self.linear_1 = ColumnParallelLinear(vision_hidden_size,
+                                             text_hidden_size,
+                                             bias=multimodal_projector_bias,
+                                             quant_config=quant_config,
+                                             prefix=f"{prefix}.linear_1")
+        self.act = get_act_fn(projector_hidden_act)
+        self.linear_2 = RowParallelLinear(text_hidden_size,
+                                          text_hidden_size,
+                                          bias=multimodal_projector_bias,
+                                          quant_config=quant_config,
+                                          prefix=f"{prefix}.linear_2")
+
+    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.linear_1(image_features)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class MiniMaxVL01DummyInputsBuilder(LlavaDummyInputsBuilder):
+    pass
+
+
+class MiniMaxVL01ProcessingInfo(LlavaNextProcessingInfo):
+
+    def get_hf_config(self):  # Need to override the config type
+        return self.ctx.get_hf_config(PretrainedConfig)
+
+    def get_hf_processor(self, **kwargs: object):
+        hf_processor = self.ctx.get_hf_processor(**kwargs)
+        image_processor = hf_processor.image_processor
+        image_processor.anyres_preprocess = (
+            image_processor.anyres_for_vllm_preprocess)
+
+        return hf_processor
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+
+class MiniMaxVL01MultiModalProcessor(
+        BaseLlavaMultiModalProcessor[MiniMaxVL01ProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+
+        pixel_values = processed_outputs.get("pixel_values")
+        if pixel_values is not None:
+            # Avoid padding since we need the output for each image to be
+            # independent of other images for the cache to work correctly
+            image_sizes = processed_outputs["image_sizes"]
+            assert len(pixel_values) == len(image_sizes)
+
+            processed_outputs["pixel_values"] = [
+                p[:, :h, :w] for p, (h, w) in zip(pixel_values, image_sizes)
+            ]
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return {
+            "pixel_values": MultiModalFieldConfig.batched("image"),
+            "image_embeds": MultiModalFieldConfig.batched("image"),
+        }
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    MiniMaxVL01MultiModalProcessor,
+    info=MiniMaxVL01ProcessingInfo,
+    dummy_inputs=MiniMaxVL01DummyInputsBuilder)
+class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                          SupportsPP):
+
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"]
+    }
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<image>"
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        # TODO: Optionally initializes this for supporting embeddings.
+        self.vision_tower = init_vision_tower_for_llava(
+            config,
+            quant_config,
+            require_post_norm=False,
+            prefix=maybe_prefix(prefix, "vision_tower"))
+        self.multi_modal_projector = MiniMaxVL01MultiModalProjector(
+            vision_hidden_size=config.vision_config.hidden_size,
+            text_hidden_size=config.text_config.hidden_size,
+            projector_hidden_act=config.projector_hidden_act,
+            multimodal_projector_bias=True,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "multi_modal_projector"))
+        self.image_newline = nn.Parameter(
+            torch.empty(config.text_config.hidden_size))
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+        self.vision_feature_layer = config.vision_feature_layer
+        self.vocab_size = config.text_config.vocab_size
+        self.pad_token_id = -1
+        if self.config.pad_token_id is not None:
+            self.pad_token_id = self.config.pad_token_id
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                self.config.image_token_index,
+            )
+        return inputs_embeds
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def _select_image_features(self, image_features: torch.Tensor, *,
+                               strategy: str) -> torch.Tensor:
+        if strategy == "default":
+            return image_features[:, 1:]
+        elif strategy == "full":
+            return image_features
+
+        raise ValueError(f"Unexpected select feature strategy: {strategy}")
+
+    def _image_pixels_to_features(
+        self,
+        vision_tower: Union[CLIPVisionModel, SiglipVisionModel,
+                            PixtralHFVisionModel],
+        pixel_values: Union[torch.Tensor, list[torch.Tensor]],
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
+        # NOTE: we skip the step to select the vision feature layer since
+        # this is already done inside the vision tower
+        image_features = vision_tower(pixel_values)
+
+        def select_features(leaf: torch.Tensor):
+            return self._select_image_features(
+                leaf,
+                strategy=self.config.vision_feature_select_strategy,
+            )
+
+        return cast(
+            Union[torch.Tensor, tuple[torch.Tensor, ...]],
+            json_map_leaves(select_features, image_features),
+        )
+
+    def _process_image_pixels(
+        self,
+        inputs: MiniMaxVL01ImagePixelInputs,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
+        assert self.vision_tower is not None
+
+        pixel_values = inputs["pixel_values"]
+
+        return self._image_pixels_to_features(self.vision_tower, pixel_values)
+
+    def _process_image_input(
+        self,
+        image_input: MiniMaxVL01ImageInputs,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
+        assert self.vision_tower is not None
+        image_features = self._process_image_pixels(image_input)
+
+        if isinstance(image_features, torch.Tensor):
+            return self.multi_modal_projector(image_features)
+
+        feature_sizes = [
+            image_feature.shape[0] for image_feature in image_features
+        ]
+
+        image_embeds = self.multi_modal_projector(torch.cat(image_features))
+        image_embeds = torch.split(image_embeds, feature_sizes)
+        return image_embeds
+
+    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
+        h = w = self.config.vision_config.image_size
+        expected_dims = (3, h, w)
+        actual_dims = tuple(data.shape[1:])
+
+        if actual_dims != expected_dims:
+            expected_expr = ("batch_size", *map(str, expected_dims))
+            raise ValueError(
+                f"The expected shape of pixel values is {expected_expr}. "
+                f"You supplied {tuple(data.shape)}.")
+
+        return data
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[MiniMaxVL01ImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            if not isinstance(pixel_values, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values)}")
+
+            return MiniMaxVL01ImagePixelInputs(
+                type="pixel_values",
+                pixel_values=self._validate_pixel_values(
+                    flatten_bn(pixel_values, concat=True)),
+            )
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+
+            return MiniMaxVL01ImageEmbeddingInputs(
+                type="image_embeds",
+                data=flatten_bn(image_embeds, concat=True),
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+
+        return self._process_image_input(image_input)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model.model(input_ids,
+                                                  positions,
+                                                  intermediate_tensors,
+                                                  inputs_embeds=inputs_embeds)
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mixtral.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mixtral.py
new file mode 100644
index 0000000000000000000000000000000000000000..30de83da49e0e4118a9ddcb5ffb946b282f50576
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mixtral.py
@@ -0,0 +1,493 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Mixtral model."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import MixtralConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class MixtralMoE(nn.Module):
+    """A tensor-parallel MoE implementation for Mixtral that shards each expert
+    across all ranks.
+
+    Each expert's weights are sharded across all ranks and a fused MoE
+    kernel is used for the forward pass, and finally we reduce the outputs
+    across ranks.
+    """
+
+    def __init__(self,
+                 num_experts: int,
+                 top_k: int,
+                 hidden_size: int,
+                 intermediate_size: int,
+                 params_dtype: Optional[torch.dtype] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 tp_size: Optional[int] = None,
+                 dp_size: Optional[int] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.hidden_size = hidden_size
+
+        # Gate always runs at half / full precision for now.
+
+        self.gate = ReplicatedLinear(hidden_size,
+                                     num_experts,
+                                     bias=False,
+                                     params_dtype=params_dtype,
+                                     quant_config=None,
+                                     prefix=f"{prefix}.gate")
+
+        self.experts = FusedMoE(num_experts=num_experts,
+                                top_k=top_k,
+                                hidden_size=hidden_size,
+                                intermediate_size=intermediate_size,
+                                params_dtype=params_dtype,
+                                reduce_results=True,
+                                renormalize=True,
+                                quant_config=quant_config,
+                                tp_size=tp_size,
+                                dp_size=dp_size,
+                                prefix=f"{prefix}.experts")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # NOTE: hidden_states can have either 1D or 2D shape.
+        orig_shape = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+        final_hidden_states = self.experts(hidden_states, router_logits)
+        return final_hidden_states.view(orig_shape)
+
+
+class MixtralAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position: int = 4096 * 32,
+        rope_theta: float = 10000,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        # MixtralConfig has an optional head_dim argument
+        self.head_dim = getattr(config, "head_dim", None)
+        if self.head_dim is None:
+            self.head_dim = self.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MixtralDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        self.self_attn = MixtralAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn")
+        self.block_sparse_moe = MixtralMoE(
+            num_experts=config.num_local_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.block_sparse_moe")
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.block_sparse_moe(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class MixtralModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.quant_config = quant_config
+        lora_vocab = (lora_config.lora_extra_vocab_size *
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: MixtralDecoderLayer(
+                config, cache_config, quant_config=quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers")
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(positions, hidden_states, residual)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="w1",
+            ckpt_down_proj_name="w2",
+            ckpt_up_proj_name="w3",
+            num_experts=self.config.num_local_experts)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+        for name, loaded_weight in weights:
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if ((name.endswith(".bias") or name.endswith("_bias"))
+                        and name not in params_dict):
+                    continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+                if name.endswith("scale"):
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param,
+                                  loaded_weight,
+                                  name,
+                                  shard_id=shard_id,
+                                  expert_id=expert_id)
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class MixtralForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    fall_back_to_pt_during_load = False
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.lora_config = lora_config
+        self.quant_config = quant_config
+
+        self.model = MixtralModel(vllm_config=vllm_config,
+                                  prefix=maybe_prefix(prefix, "model"))
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE
+            # We need bigger padding if using lora for kernel
+            # compatibility
+            if not lora_config else lora_config.lora_vocab_padding_size,
+            quant_config=quant_config,
+        )
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mixtral_quant.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mixtral_quant.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8ad358c622d256af38faf39181e65207a178cc4
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mixtral_quant.py
@@ -0,0 +1,453 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Mixtral model."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import MixtralConfig
+
+from vllm.attention import Attention
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class MixtralMLP(nn.Module):
+
+    def __init__(
+        self,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size: int,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.num_experts = num_experts
+        self.ffn_dim = intermediate_size
+        self.hidden_dim = hidden_size
+
+        self.w1 = ReplicatedLinear(self.hidden_dim,
+                                   self.ffn_dim,
+                                   bias=False,
+                                   quant_config=quant_config)
+        self.w2 = ReplicatedLinear(self.ffn_dim,
+                                   self.hidden_dim,
+                                   bias=False,
+                                   quant_config=quant_config)
+        self.w3 = ReplicatedLinear(self.hidden_dim,
+                                   self.ffn_dim,
+                                   bias=False,
+                                   quant_config=quant_config)
+
+        # TODO: Use vllm's SiluAndMul
+        self.act_fn = nn.SiLU()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        w1_out, _ = self.w1(hidden_states)
+        w1_out = self.act_fn(w1_out)
+        w3_out, _ = self.w3(hidden_states)
+        current_hidden_states = w1_out * w3_out
+        current_hidden_states, _ = self.w2(current_hidden_states)
+        return current_hidden_states
+
+
+class MixtralMoE(nn.Module):
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.rank = get_tensor_model_parallel_rank()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_total_experts = config.num_local_experts
+        self.top_k = config.num_experts_per_tok
+        if self.tp_size > self.num_total_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {self.num_total_experts}.")
+        # Split experts equally between ranks
+        self.expert_indices = np.array_split(range(self.num_total_experts),
+                                             self.tp_size)[self.rank].tolist()
+        if not self.expert_indices:
+            raise ValueError(
+                f"Rank {self.rank} has no experts assigned to it.")
+
+        self.experts = nn.ModuleList([
+            MixtralMLP(self.num_total_experts,
+                       config.hidden_size,
+                       config.intermediate_size,
+                       quant_config=quant_config)
+            if idx in self.expert_indices else None
+            for idx in range(self.num_total_experts)
+        ])
+        self.gate = ReplicatedLinear(config.hidden_size,
+                                     self.num_total_experts,
+                                     bias=False,
+                                     quant_config=None)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(routing_weights,
+                                                       self.top_k,
+                                                       dim=-1)
+        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+
+        final_hidden_states = None
+        for expert_idx in self.expert_indices:
+            expert_layer = self.experts[expert_idx]
+            expert_mask = (selected_experts == expert_idx)
+            expert_weights = (routing_weights * expert_mask).sum(dim=-1,
+                                                                 keepdim=True)
+
+            current_hidden_states = expert_layer(hidden_states).mul_(
+                expert_weights)
+            if final_hidden_states is None:
+                final_hidden_states = current_hidden_states
+            else:
+                final_hidden_states.add_(current_hidden_states)
+
+        return tensor_model_parallel_all_reduce(final_hidden_states).view(
+            num_tokens, hidden_dim)
+
+
+class MixtralAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position: int = 4096 * 32,
+        rope_theta: float = 10000,
+        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        # MixtralConfig has an optional head_dim argument
+        self.head_dim = getattr(config, "head_dim", None)
+        if self.head_dim is None:
+            self.head_dim = self.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MixtralDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: MixtralConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        self.self_attn = MixtralAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.block_sparse_moe = MixtralMoE(config=config,
+                                           quant_config=quant_config)
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.block_sparse_moe(hidden_states)
+        return hidden_states, residual
+
+
+class MixtralModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: MixtralDecoderLayer(
+                config, cache_config, quant_config=quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers")
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(positions, hidden_states, residual)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if name.endswith("scale"):
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip experts that are not assigned to this worker.
+                if ("block_sparse_moe.experts." in name
+                        and name not in params_dict):
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class MixtralForCausalLM(nn.Module, SupportsPP):
+    fall_back_to_pt_during_load = False
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = MixtralModel(vllm_config=vllm_config,
+                                  prefix=maybe_prefix(prefix, "model"))
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config)
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mllama4.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mllama4.py
new file mode 100644
index 0000000000000000000000000000000000000000..b405dfca6d39b7fa24c931332ed7fc10e651cabc
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mllama4.py
@@ -0,0 +1,1082 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Copyright 2025 the LLAMA4, Meta Inc., vLLM, and HuggingFace Inc. team.
+# All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+from collections.abc import Iterable, Mapping
+from itertools import tee
+from typing import Literal, Optional, TypedDict, Union
+
+import torch
+from torch import nn
+from transformers import BatchFeature, Llama4Config, Llama4VisionConfig
+from transformers.image_utils import SizeDict
+from transformers.models.llama4 import Llama4Processor
+from transformers.models.llama4.image_processing_llama4_fast import (
+    find_supported_resolutions, get_best_fit)
+
+from vllm.attention.layer import MultiHeadAttention
+from vllm.config import VllmConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.inputs import InputProcessingContext
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.model_loader.utils import initialize_model
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs, NestedTensors)
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+                                   MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.multimodal.utils import run_dp_sharded_vision_model
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .llama4 import Llama4ForCausalLM
+from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+
+class Llama4ImagePatchInputs(TypedDict):
+    type: Literal["pixel_values"]
+    flat_data: torch.Tensor
+    """
+    Shape:
+    `(batch_size * num_chunks, num_channels, image size, image size)`
+    """
+    patches_per_image: torch.Tensor
+    """
+    The number of total patches for each image in the batch.
+
+    This is used to split the embeddings which has the first two dimensions
+    flattened just like `flat_data`.
+    """
+
+    aspect_ratios: Union[torch.Tensor, list[torch.Tensor]]
+    """
+    A list of aspect ratios corresponding to the number of tiles
+    in each dimension that each image in the batch corresponds to.
+
+    Shape:
+    `(batch_size, ratio)` where ratio is a pair `(ratio_h, ratio_w)`
+    """
+
+
+class Llama4VisionMLP(nn.Module):
+
+    def __init__(
+        self,
+        input_size: int,
+        intermediate_size: int,
+        output_size: int,
+        bias: bool,
+        output_activation: bool,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        cls_fc1 = (ReplicatedLinear
+                   if use_data_parallel else ColumnParallelLinear)
+        self.fc1 = cls_fc1(
+            input_size=input_size,
+            output_size=intermediate_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+        )
+        cls_fc2 = ReplicatedLinear if use_data_parallel else RowParallelLinear
+        self.fc2 = cls_fc2(
+            input_size=intermediate_size,
+            output_size=output_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+        )
+        self.activation_fn = nn.GELU()
+        self.output_activation = output_activation
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        if self.output_activation:
+            return self.activation_fn(hidden_states)
+        return hidden_states
+
+
+class Llama4MultiModalProjector(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.linear_1 = ColumnParallelLinear(
+            input_size=config.vision_config.vision_output_dim,
+            output_size=config.text_config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            gather_output=True,
+            prefix=f"{prefix}.linear_1",
+        )
+
+    def forward(self, image_features):
+        hidden_states, _ = self.linear_1(image_features)
+        return hidden_states
+
+
+def pixel_shuffle(input_tensor, shuffle_ratio):
+    # input_tensor: [batch_size, num_patches, channels]
+    batch_size, num_patches, channels = input_tensor.shape
+    patch_size = int(math.sqrt(num_patches))
+
+    input_tensor = input_tensor.view(batch_size, patch_size, patch_size, -1)
+    batch_size, height, width, channels = input_tensor.size()
+
+    reshaped_tensor = input_tensor.view(batch_size, height,
+                                        int(width * shuffle_ratio),
+                                        int(channels / shuffle_ratio))
+    reshaped_tensor = reshaped_tensor.permute(0, 2, 1, 3).contiguous()
+
+    reshaped_tensor = reshaped_tensor.view(
+        batch_size,
+        int(height * shuffle_ratio),
+        int(width * shuffle_ratio),
+        int(channels / (shuffle_ratio**2)),
+    )
+    reshaped_tensor = reshaped_tensor.permute(0, 2, 1, 3).contiguous()
+
+    output_tensor = reshaped_tensor.view(batch_size, -1,
+                                         reshaped_tensor.shape[-1])
+    return output_tensor
+
+
+class Llama4VisionPixelShuffleMLP(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.pixel_shuffle_ratio = config.pixel_shuffle_ratio
+        self.inner_dim = int(config.projector_input_dim //
+                             (self.pixel_shuffle_ratio**2))
+        self.output_dim = config.projector_output_dim
+        self.mlp = Llama4VisionMLP(
+            input_size=config.intermediate_size,
+            intermediate_size=config.projector_input_dim,
+            output_size=config.projector_output_dim,
+            bias=config.multi_modal_projector_bias,
+            output_activation=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+            use_data_parallel=use_data_parallel,
+        )
+
+    def forward(self, encoded_patches: torch.Tensor) -> torch.Tensor:
+        encoded_patches = pixel_shuffle(encoded_patches,
+                                        self.pixel_shuffle_ratio)
+        return self.mlp(encoded_patches)
+
+
+class Llama4VisionAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: Llama4VisionConfig,
+        quant_config: Optional[QuantizationConfig],
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.config = config
+        self.tp_size = (1 if use_data_parallel else
+                        get_tensor_model_parallel_world_size())
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.hidden_size // self.num_heads
+        assert self.num_heads % self.tp_size == 0
+        self.num_local_heads = self.num_heads // self.tp_size
+        self.q_size = self.num_local_heads * self.head_dim
+        self.kv_size = self.num_local_heads * self.head_dim
+        self.attention_dropout = config.attention_dropout
+        self.scaling = self.head_dim**-0.5
+
+        self.attn = MultiHeadAttention(self.num_local_heads, self.head_dim,
+                                       self.scaling)
+
+        if use_data_parallel:
+            self.qkv_proj = ReplicatedLinear(
+                self.embed_dim,
+                self.q_size + 2 * self.kv_size,
+                bias=True,
+                quant_config=quant_config,
+                prefix=f"{prefix}.qkv_proj",
+            )
+            self.o_proj = ReplicatedLinear(
+                self.num_heads * self.head_dim,
+                self.embed_dim,
+                bias=True,
+                quant_config=quant_config,
+                prefix=f"{prefix}.o_proj",
+            )
+        else:
+            self.qkv_proj = QKVParallelLinear(
+                self.embed_dim,
+                self.head_dim,
+                self.num_heads,
+                bias=True,
+                quant_config=quant_config,
+                prefix=f"{prefix}.qkv_proj",
+            )
+            self.o_proj = RowParallelLinear(
+                self.num_heads * self.head_dim,
+                self.embed_dim,
+                bias=True,
+                input_is_parallel=True,
+                quant_config=quant_config,
+                prefix=f"{prefix}.o_proj",
+            )
+
+        self.rotary_emb = get_rope(
+            head_size=self.head_dim,
+            rotary_dim=config.hidden_size // config.num_attention_heads // 2,
+            # number of image patches
+            max_position=(config.image_size // config.patch_size)**2,
+            base=config.rope_theta,
+            rope_scaling={"rope_type": "mllama4"},
+            is_neox_style=False,
+            dtype=torch.complex64,  # important
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        input_shape = hidden_states.shape[:-1]
+
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        q = q.view(q.shape[0], q.shape[1], self.num_local_heads, self.head_dim)
+        k = k.view(k.shape[0], k.shape[1], self.num_local_heads, self.head_dim)
+        q, k = self.rotary_emb(q, k)
+
+        q = q.view(q.shape[0], q.shape[1], -1)
+        k = k.view(k.shape[0], k.shape[1], -1)
+
+        attn_output = self.attn(q, k, v)
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output, _ = self.o_proj(attn_output)
+
+        return attn_output
+
+
+class Llama4VisionEncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Llama4VisionConfig,
+        quant_config: Optional[QuantizationConfig],
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.intermediate_size = config.intermediate_size
+
+        self.self_attn = Llama4VisionAttention(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+            use_data_parallel=use_data_parallel,
+        )
+        self.mlp = Llama4VisionMLP(
+            input_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            output_size=config.hidden_size,
+            bias=True,
+            output_activation=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+            use_data_parallel=use_data_parallel,
+        )
+
+        self.input_layernorm = nn.LayerNorm(config.hidden_size)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size)
+
+    def forward(
+        self,
+        hidden_state: torch.Tensor,
+    ):
+        # Self Attention
+        residual = hidden_state
+        hidden_state = self.input_layernorm(hidden_state)
+        hidden_state = self.self_attn(hidden_state)
+        hidden_state = residual + hidden_state
+
+        # Feed forward
+        residual = hidden_state
+        hidden_state = self.post_attention_layernorm(hidden_state)
+        hidden_state = self.mlp(hidden_state)
+        hidden_state = residual + hidden_state
+
+        outputs = (hidden_state, )
+        return outputs
+
+
+class Llama4VisionEncoder(nn.Module):
+
+    def __init__(
+        self,
+        config: Llama4VisionConfig,
+        quant_config: Optional[QuantizationConfig],
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList([
+            Llama4VisionEncoderLayer(
+                config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.layers.{layer_idx}",
+                use_data_parallel=use_data_parallel,
+            ) for layer_idx in range(config.num_hidden_layers)
+        ])
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape
+                    `(batch_size, sequence_length, hidden_size)`):
+                Optionally, instead of passing `input_ids` you can choose to
+                directly pass an embedded representation. This is useful if you
+                want more control over how to convert `input_ids` indices into
+                associated vectors than the model's internal embedding
+                lookup matrix.
+        """
+
+        for encoder_layer in self.layers:
+            layer_outputs = encoder_layer(hidden_states)
+            hidden_states = layer_outputs[0]
+
+        return hidden_states
+
+
+class Llama4UnfoldConvolution(nn.Module):
+
+    def __init__(
+        self,
+        config: Llama4VisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        kernel_size = config.patch_size
+        if isinstance(kernel_size, int):
+            kernel_size = (kernel_size, kernel_size)
+        self.unfold = torch.nn.Unfold(kernel_size=kernel_size,
+                                      stride=config.patch_size)
+        params = {
+            "input_size":
+            config.num_channels * kernel_size[0] * kernel_size[1],
+            "output_size": config.hidden_size,
+            "bias": False,
+            "quant_config": quant_config,
+            "prefix": f"{prefix}.linear",
+        }
+        if use_data_parallel:
+            cls = ReplicatedLinear
+        else:
+            cls = ColumnParallelLinear
+            params["gather_output"] = True
+        self.linear = cls(**params)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.unfold(hidden_states)
+        hidden_states = hidden_states.permute(0, 2, 1)
+        hidden_states, _ = self.linear(hidden_states)
+        return hidden_states
+
+
+class Llama4VisionModel(nn.Module):
+
+    def __init__(
+        self,
+        config: Llama4VisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.config = config
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+        self.hidden_size = config.hidden_size
+        self.num_channels = config.num_channels
+
+        self.num_patches = (self.image_size // self.patch_size)**2 + 1
+        self.scale = config.hidden_size**-0.5
+
+        self.patch_embedding = Llama4UnfoldConvolution(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.patch_embedding",
+            use_data_parallel=use_data_parallel,
+        )
+
+        self.class_embedding = nn.Parameter(self.scale *
+                                            torch.randn(self.hidden_size))
+        self.positional_embedding_vlm = nn.Parameter(
+            self.scale * torch.randn(self.num_patches, self.hidden_size))
+
+        # layer norms
+        self.layernorm_pre = nn.LayerNorm(self.hidden_size, eps=1e-5)
+        self.layernorm_post = nn.LayerNorm(self.hidden_size, eps=1e-5)
+
+        # encoders
+        self.model = Llama4VisionEncoder(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.model",
+            use_data_parallel=use_data_parallel,
+        )
+        self.vision_adapter = Llama4VisionPixelShuffleMLP(
+            config,
+            quant_config,
+            prefix=f"{prefix}.vision_adapter",
+            use_data_parallel=use_data_parallel,
+        )
+
+    def forward(
+        self,
+        images_flattened: torch.Tensor,
+    ) -> torch.Tensor:
+        # Patch embedding
+        hidden_state = self.patch_embedding(images_flattened)
+        num_tiles, num_patches, hidden_dim = hidden_state.shape
+
+        # Add cls token
+        class_embedding = self.class_embedding.expand(hidden_state.shape[0], 1,
+                                                      hidden_state.shape[-1])
+        hidden_state = torch.cat([hidden_state, class_embedding], dim=1)
+        num_patches += 1
+
+        # Position embeddings
+        hidden_state = hidden_state.reshape(
+            num_tiles,
+            1,
+            num_patches,
+            hidden_dim,
+        )
+        positional_embedding = self.positional_embedding_vlm.to(
+            dtype=hidden_state.dtype, device=hidden_state.device)
+        hidden_state = hidden_state + positional_embedding
+        hidden_state = self.layernorm_pre(hidden_state)
+        hidden_state = hidden_state.view(num_tiles, -1, hidden_dim)
+
+        # Apply encoder
+        hidden_state = self.model(hidden_state)
+        hidden_state = self.layernorm_post(hidden_state)
+
+        # Remove CLS token output
+        hidden_state = hidden_state[:, :-1, :]
+
+        # now, we use Llama4VisionPixelShuffle + mlp to project embeddings
+        hidden_state = self.vision_adapter(hidden_state)
+
+        return hidden_state
+
+
+class Mllama4ProcessingInfo(BaseProcessingInfo):
+
+    def __init__(self, ctx: InputProcessingContext) -> None:
+        super().__init__(ctx)
+
+    def get_hf_config(self) -> Llama4Config:
+        return self.ctx.get_hf_config(Llama4Config)
+
+    def get_hf_processor(self, **kwargs: object) -> Llama4Processor:
+        return self.ctx.get_hf_processor(Llama4Processor,
+                                         use_fast=kwargs.pop("use_fast", True),
+                                         **kwargs)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        # Although vLLM can support more images from an infra capability
+        # perspective, we do not recommend using >10 images in practice.
+        return {"image": None}
+
+    @staticmethod
+    def get_patch_per_chunk(vision_config: Llama4VisionConfig) -> int:
+        image_size = vision_config.image_size
+        patch_size = vision_config.patch_size
+
+        assert (
+            image_size %
+            patch_size == 0), f"chunk size {image_size} should be multiple of "
+        f"patch_size {patch_size}"
+
+        ds_ratio = int(round(1.0 / (vision_config.pixel_shuffle_ratio**2)))
+        return (image_size // patch_size)**2 // ds_ratio
+
+    def get_max_num_tiles(self) -> int:
+        image_processor = self.get_hf_processor().image_processor
+        return image_processor.max_patches
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        vision_config = self.get_hf_config().vision_config
+        image_size = vision_config.image_size
+        # Result in the max possible feature size (h:w = 16:1)
+        return ImageSize(height=self.get_max_num_tiles() * image_size,
+                         width=image_size)
+
+
+class Mllama4MultiModalProcessor(BaseMultiModalProcessor[Mllama4ProcessingInfo]
+                                 ):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        tokenizer = self.info.get_tokenizer()
+
+        if mm_data is None:
+            return tokenizer(prompt, add_special_tokens=False)  # exclude bos
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+
+        processor = self.info.get_hf_processor(**mm_kwargs)
+        image_processor = processor.image_processor
+        vision_config = self.info.get_hf_config().vision_config
+
+        if processed_outputs.get("pixel_values") is not None:
+            assert (
+                "images" in mm_data
+            ), "images expected to be in mm_data when pixel_values is present"
+
+            images = mm_data["images"]
+            parsed_images = (self._get_data_parser().parse_mm_data({
+                "image":
+                images
+            }).get_items("image", ImageProcessorItems))
+
+            tile_size = vision_config.image_size
+            possible_resolutions = find_supported_resolutions(
+                max_num_chunks=self.info.get_max_num_tiles(),
+                patch_size=SizeDict(height=tile_size, width=tile_size),
+            )
+            best_fit_sizes = [
+                get_best_fit(
+                    (image.size[1], image.size[0]),
+                    torch.tensor(possible_resolutions),
+                    resize_to_max_canvas=image_processor.resize_to_max_canvas,
+                ) for image in parsed_images
+            ]
+            # TODO tile height/width do not necessarily need to match
+            aspect_ratios = [(image_size[0] // tile_size,
+                              image_size[1] // tile_size)
+                             for image_size in best_fit_sizes]
+            patches_per_image = [
+                1 if r_h * r_w == 1 else 1 + r_h * r_w
+                for (r_h, r_w) in aspect_ratios
+            ]
+
+            processed_outputs["aspect_ratios"] = aspect_ratios
+            processed_outputs["patches_per_image"] = torch.tensor(
+                patches_per_image)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        patches_per_image = hf_inputs.get("patches_per_image", torch.empty(0))
+        return dict(
+            pixel_values=MultiModalFieldConfig.flat_from_sizes(
+                "image", patches_per_image),
+            patches_per_image=MultiModalFieldConfig.batched("image"),
+            aspect_ratios=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> list[PromptUpdate]:
+        assert (
+            mm_items.get_count("image", strict=False) == 0
+            or "aspect_ratios" in out_mm_kwargs
+        ), "Transformers expect to include aspect_ratios in out_mm_kwargs"
+
+        config = self.info.get_hf_config()
+        vision_config = config.vision_config
+
+        num_patches_per_chunk = self.info.get_patch_per_chunk(vision_config)
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_token = hf_processor.image_token
+        img_patch_token = hf_processor.img_patch_token
+
+        def get_replacement(item_idx: int):
+            aspect_ratio = out_mm_kwargs["aspect_ratios"][item_idx]
+
+            repl = hf_processor._prompt_split_image(
+                aspect_ratio=aspect_ratio,
+                num_patches_per_chunk=num_patches_per_chunk,
+            )
+
+            return PromptUpdateDetails.select_text(repl, img_patch_token)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=image_token,
+                replacement=get_replacement,
+            )
+        ]
+
+
+class Mllama4DummyInputsBuilder(BaseDummyInputsBuilder[Mllama4ProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        processor = self.info.get_hf_processor()
+        image_token = processor.fake_image_token
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+
+        (target_width,
+         target_height) = self.info.get_image_size_with_most_features()
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Mllama4MultiModalProcessor,
+    info=Mllama4ProcessingInfo,
+    dummy_inputs=Mllama4DummyInputsBuilder,
+)
+class Llama4ForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                     SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<|image|>"
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.use_data_parallel = (vllm_config.parallel_config.
+                                  enable_multimodal_encoder_data_parallel)
+        self.config = config
+        self.quant_config = quant_config
+        self.multimodal_config = multimodal_config
+        if multimodal_config.get_limit_per_prompt("image"):
+            self.vision_model = Llama4VisionModel(
+                config.vision_config,
+                None,
+                prefix=maybe_prefix(prefix, "vision_model"),
+                use_data_parallel=self.use_data_parallel,
+            )
+            self.multi_modal_projector = Llama4MultiModalProjector(
+                self.config,
+                None,
+                prefix=maybe_prefix(prefix, "multi_modal_projector"))
+        else:
+            self.vision_model = None
+            self.multi_modal_projector = None
+        self.language_model = initialize_model(
+            vllm_config=vllm_config.with_hf_config(config.text_config,
+                                                   ["LlamaForCausalLM"]),
+            prefix=maybe_prefix(prefix, "language_model"),
+            model_class=Llama4ForCausalLM,
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Llama4ImagePatchInputs]:
+        # num_images, 1, num_chunks, channel, image_size, image_size
+        pixel_values = kwargs.pop("pixel_values", None)
+        if pixel_values is None:
+            return None
+
+        # num_images x num_chunks, channel, image_size, image_size
+        # TODO: confirm handling for variable lengths
+        flat_pixel_values = flatten_bn(pixel_values, concat=True)
+        patches_per_image = flatten_bn(kwargs.pop("patches_per_image"))
+
+        aspect_ratios = kwargs.pop("aspect_ratios", None)
+        if not isinstance(aspect_ratios, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of aspect_ratios. "
+                             f"Got type: {type(aspect_ratios)}")
+
+        return Llama4ImagePatchInputs(
+            type="pixel_values",
+            flat_data=flat_pixel_values,
+            patches_per_image=patches_per_image,
+            aspect_ratios=aspect_ratios,
+        )
+
+    def _process_image_input(
+            self, image_input: Llama4ImagePatchInputs) -> MultiModalEmbeddings:
+
+        assert self.vision_model and self.multi_modal_projector
+        flat_data = image_input["flat_data"]
+        patches_per_image = image_input["patches_per_image"].tolist()
+
+        # shard image input
+        if self.use_data_parallel:
+            vision_embeddings_flat = run_dp_sharded_vision_model(
+                flat_data, self.vision_model)
+        else:
+            vision_embeddings_flat = self.vision_model(flat_data)
+
+        vision_embeddings_flat = self.multi_modal_projector(
+            vision_embeddings_flat)
+
+        return [
+            img.flatten(0, 1)
+            for img in vision_embeddings_flat.split(patches_per_image, dim=0)
+        ]
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self, **kwargs) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+
+        return self._process_image_input(image_input)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+
+        if multimodal_embeddings is not None and len(
+                multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                self.config.image_token_index,
+            )
+
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner,
+        # this condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        return self.language_model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def separate_weights(
+        self,
+        weights: Iterable[tuple[str, torch.Tensor]],
+        prefix: str,
+    ) -> tuple[Iterable[tuple[str, torch.Tensor]], Iterable[tuple[
+            str, torch.Tensor]]]:
+        weights1, weights2 = tee(weights, 2)
+
+        def get_prefix_weights() -> Iterable[tuple[str, torch.Tensor]]:
+            for name, data in weights1:
+                if name.startswith(prefix):
+                    yield (name, data)
+
+        def get_other_weights() -> Iterable[tuple[str, torch.Tensor]]:
+            for name, data in weights2:
+                if not name.startswith(prefix):
+                    yield (name, data)
+
+        return get_prefix_weights(), get_other_weights()
+
+    def _consolidate_qkv_weights(
+        self, weights: Iterable[tuple[str, torch.Tensor]]
+    ) -> Iterable[tuple[str, torch.Tensor]]:
+        qkv_idx_mappings = {
+            ".self_attn.q_proj": 0,
+            ".self_attn.k_proj": 1,
+            ".self_attn.v_proj": 2,
+        }
+        qkv_weights = {}
+        for name, loaded_weight in weights:
+            for weight_name, idx in qkv_idx_mappings.items():
+                if weight_name not in name:
+                    continue
+                new_name = name.replace(weight_name, ".self_attn.qkv_proj")
+                if new_name not in qkv_weights:
+                    qkv_weights[new_name] = [None] * 3
+                qkv_weights[new_name][idx] = loaded_weight
+                break
+            else:
+                yield name, loaded_weight
+        for key, weight in qkv_weights.items():
+            qkv_weight = torch.cat(weight, dim=0)
+            yield key, qkv_weight
+
+    def _rename_weight_for_modelopt_checkpoint(self, name: str) -> str:
+        """Rename weights from ModelOpt llama4 fp8 checkpoints to vLLM
+        format."""
+        if name.startswith("model.") or name.startswith(
+                "language_model.model."):
+            renamed = name.replace("model.", "language_model.model.",
+                                   1) if name.startswith("model.") else name
+            # Handle expert scale parameters with flat naming
+            if "feed_forward.experts." in name and ("_input_scale" in name or
+                                                    "_weight_scale" in name):
+                # Map checkpoint naming to vLLM's expected naming
+                if "down_proj_input_scale" in renamed:
+                    return renamed.replace("down_proj_input_scale",
+                                           "w2_input_scale")
+                elif "down_proj_weight_scale" in renamed:
+                    return renamed.replace("down_proj_weight_scale",
+                                           "w2_weight_scale")
+                elif "gate_up_proj_input_scale" in renamed:
+                    return renamed.replace("gate_up_proj_input_scale",
+                                           "w13_input_scale")
+                elif "gate_up_proj_weight_scale" in renamed:
+                    return renamed.replace("gate_up_proj_weight_scale",
+                                           "w13_weight_scale")
+                return renamed
+
+            # Handle attention scale parameters
+            elif "self_attn." in name and (".k_scale" in name
+                                           or ".v_scale" in name):
+                if ".k_proj.k_scale" in renamed:
+                    return renamed.replace(".k_proj.k_scale", ".attn.k_scale")
+                elif ".v_proj.v_scale" in renamed:
+                    return renamed.replace(".v_proj.v_scale", ".attn.v_scale")
+                return renamed
+
+            # Standard model.* to language_model.model.* renaming
+            return renamed
+
+        elif name.startswith("lm_head.weight"):
+            return name.replace("lm_head.weight",
+                                "language_model.lm_head.weight")
+
+        return name
+
+    def _separate_and_rename_weights(
+        self, weights: Iterable[tuple[str, torch.Tensor]]
+    ) -> tuple[list[tuple[str, torch.Tensor]], list[tuple[str, torch.Tensor]]]:
+        """Rename weights and separate them into language_model and other
+        weights."""
+        language_model_weights = []
+        other_weights = []
+
+        for name, weight in weights:
+            renamed = self._rename_weight_for_modelopt_checkpoint(name)
+
+            if renamed.startswith("language_model."):
+                language_model_weights.append((renamed, weight))
+            else:
+                other_weights.append((renamed, weight))
+
+        return language_model_weights, other_weights
+
+    def _handle_expert_scale_broadcasting(
+            self, weights: list[tuple[str, torch.Tensor]], params_dict: dict
+    ) -> tuple[list[tuple[str, torch.Tensor]], set[str]]:
+        """Handle expert scale parameters that need broadcasting.
+
+        ModelOpt checkpoints use a single value tensor scalar for BMM style
+        experts, vLLM expects the scale to be broadcasted across all experts.
+        """
+        regular_weights = []
+        expert_scale_weights = []
+        updated_params = set()
+
+        for name, weight in weights:
+            # Check if this is an expert scale parameter that needs broadcasting
+            if ("feed_forward.experts." in name and "scale" in name
+                    and ".shared_expert" not in name):
+                if name in params_dict:
+                    param = params_dict[name]
+                    if (hasattr(param, 'data') and param.data.numel() > 1
+                            and weight.numel() == 1):
+                        # Broadcast single value to all experts
+                        param.data.fill_(weight.item())
+                        updated_params.add(name)
+                        continue
+
+                expert_scale_weights.append((name, weight))
+            else:
+                regular_weights.append((name, weight))
+
+        return regular_weights, expert_scale_weights, updated_params
+
+    def _load_other_weights(self, other_weights: Iterable[tuple[str,
+                                                                torch.Tensor]],
+                            params_dict: dict,
+                            stacked_params_mapping: list) -> set[str]:
+        """Load non-language-model weights with stacking support."""
+        updated_params = set()
+
+        if self.use_data_parallel:
+            other_weights = self._consolidate_qkv_weights(other_weights)
+
+        for name, loaded_weight in other_weights:
+            # Try stacked parameter mapping first
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name or self.use_data_parallel:
+                    continue
+                name = name.replace(weight_name, param_name)
+                param = params_dict[name]
+                updated_params.add(name)
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Use regular weight loading
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+                updated_params.add(name)
+
+        return updated_params
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".self_attn.qkv_proj", ".self_attn.q_proj", "q"),
+            (".self_attn.qkv_proj", ".self_attn.k_proj", "k"),
+            (".self_attn.qkv_proj", ".self_attn.v_proj", "v"),
+            # Shared expert gate_up_proj stacking
+            (".shared_expert.gate_up_proj", ".shared_expert.gate_proj", 0),
+            (".shared_expert.gate_up_proj", ".shared_expert.up_proj", 1),
+            # Feed forward gate_up_proj stacking (for non-MoE layers if any)
+            (".feed_forward.gate_up_proj", ".feed_forward.gate_proj", 0),
+            (".feed_forward.gate_up_proj", ".feed_forward.up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        updated_params: set[str] = set()
+
+        # Separate and rename weights
+        language_model_weights, other_weights = (
+            self._separate_and_rename_weights(weights))
+
+        # Skip loading vision model and projector if they're not initialized.
+        if self.vision_model is None and self.multi_modal_projector is None:
+            other_weights = []
+
+        # Handle expert scale parameters
+        regular_weights, expert_scale_weights, updated_params_from_experts = (
+            self._handle_expert_scale_broadcasting(language_model_weights,
+                                                   params_dict))
+        updated_params.update(updated_params_from_experts)
+
+        loader = AutoWeightsLoader(self)
+        loaded_language_model_params = loader.load_weights(regular_weights)
+        assert loaded_language_model_params is not None
+        updated_params.update(loaded_language_model_params)
+
+        if expert_scale_weights:
+            loaded_expert_scale_params = loader.load_weights(
+                expert_scale_weights)
+            if loaded_expert_scale_params:
+                updated_params.update(loaded_expert_scale_params)
+
+        updated_params.update(
+            self._load_other_weights(other_weights, params_dict,
+                                     stacked_params_mapping))
+
+        return updated_params
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/molmo.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/molmo.py
new file mode 100644
index 0000000000000000000000000000000000000000..78dc0dca957f037cfa569dff2af2d8a0bb88a728
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/molmo.py
@@ -0,0 +1,1576 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from dataclasses import dataclass
+from functools import cached_property, partial
+from typing import Optional, TypedDict, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from transformers import (BatchFeature, PretrainedConfig, ProcessorMixin,
+                          TensorType)
+from transformers.image_utils import ImageInput
+from transformers.tokenization_utils_base import TextInput
+
+from vllm.attention import Attention
+from vllm.attention.layer import MultiHeadAttention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size,
+                              split_tensor_along_last_dim,
+                              tensor_model_parallel_all_gather)
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.activation import (MulAndSilu, QuickGELU,
+                                                   SiluAndMul)
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+                                   MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptIndexTargets,
+                                        PromptInsertion, PromptUpdate,
+                                        PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP, SupportsQuant)
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+                    is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix, merge_multimodal_embeddings)
+
+# TODO: hard-coded for now. Consider making it configurable.
+VIT_LAYERS = [-2, -9]
+NUM_PREFIX_TOKENS = 1
+ADDITIONAL_VOCAB_SIZE = 128
+IMAGE_PATCH_TOKEN = "<im_patch>"
+IM_COL_TOKEN = "<im_col>"
+IM_START_TOKEN = "<im_start>"
+IM_END_TOKEN = "<im_end>"
+POOLING_SIZE = 2
+
+
+class MolmoImageInputs(TypedDict):
+    images: Union[torch.Tensor, list[torch.Tensor]]
+    """Shape: `(batch_size * num_images, num_crops, num_patch, patch_dim)`"""
+
+    image_masks: Optional[Union[torch.Tensor, list[torch.Tensor]]]
+    """Shape: `(batch_size * num_images, num_crops, num_patch)`"""
+
+    feat_is_patch: Union[torch.Tensor, list[torch.Tensor]]
+    """
+    A boolean mask indicating which image features correspond
+    to patch tokens.
+
+    Shape: `(batch_size * num_images, num_crops, num_patch)`
+    """
+
+    num_crops: torch.Tensor
+    """Shape: `(batch_size * num_images)`"""
+
+
+@dataclass
+class VisionBackboneConfig:
+    image_default_input_size: tuple[int, int] = (336, 336)
+    image_patch_size: int = 14
+    image_pos_patch_size: int = 14
+    image_emb_dim: int = 1024
+    image_num_heads: int = 16
+    image_num_key_value_heads: int = 16
+    image_num_layers: int = 23
+    image_mlp_dim: int = 4096
+    image_mlp_activations: str = "quick_gelu"
+    image_num_pos: int = 577
+    image_norm_eps: float = 1e-5
+
+    def __post_init__(self):
+        self.image_default_input_size = tuple(
+            self.image_default_input_size)  # type: ignore[assignment]
+
+    @property
+    def image_num_patch(self):
+        h, w = self.image_default_input_size
+        return h // self.image_patch_size, w // self.image_patch_size
+
+
+class ViTMLP(nn.Module):
+    """MLP used in Vision Transformer."""
+
+    def __init__(
+        self,
+        config: VisionBackboneConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.w1 = ColumnParallelLinear(
+            config.image_emb_dim,
+            config.image_mlp_dim,
+            bias=True,
+            quant_config=quant_config,
+        )
+        # Activation function.
+        assert config.image_mlp_activations == "quick_gelu"
+        self.act = QuickGELU()
+        self.w2 = RowParallelLinear(
+            config.image_mlp_dim,
+            config.image_emb_dim,
+            bias=True,
+            quant_config=quant_config,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.w1(x)
+        x = self.act(x)
+        x, _ = self.w2(x)
+        return x
+
+
+class MultiHeadDotProductAttention(nn.Module):
+    """Multi-head attention used in Vision Transformer."""
+
+    def __init__(
+        self,
+        config: VisionBackboneConfig,
+        use_bias: bool = True,
+        nlayers: int = 1,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+
+        self.hidden_size = config.image_emb_dim
+        self.total_num_heads = config.image_num_heads
+        tp_size = get_tensor_model_parallel_world_size()
+
+        assert self.hidden_size % self.total_num_heads == 0
+        assert self.total_num_heads % tp_size == 0
+
+        self.num_heads = self.total_num_heads // tp_size
+        self.head_dim = self.hidden_size // self.total_num_heads
+
+        self.total_num_kv_heads = config.image_num_key_value_heads
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            assert tp_size % self.total_num_kv_heads == 0
+
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        self.wq = ColumnParallelLinear(
+            nlayers * self.hidden_size,
+            self.total_num_heads * self.head_dim,
+            bias=use_bias,
+            quant_config=quant_config,
+        )
+        self.wk = ColumnParallelLinear(
+            nlayers * self.hidden_size,
+            self.total_num_kv_heads * self.head_dim,
+            bias=use_bias,
+            quant_config=quant_config,
+        )
+        self.wv = ColumnParallelLinear(
+            nlayers * self.hidden_size,
+            self.total_num_kv_heads * self.head_dim,
+            bias=use_bias,
+            quant_config=quant_config,
+        )
+        self.wo = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=use_bias,
+            quant_config=quant_config,
+        )
+
+        self.scale = self.head_dim**-0.5
+        self.attn = MultiHeadAttention(self.num_heads,
+                                       self.head_dim,
+                                       self.scale,
+                                       num_kv_heads=self.num_kv_heads)
+
+    def forward(self,
+                inputs_q: torch.Tensor,
+                inputs_kv: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        if inputs_kv is not None:
+            inputs_k = inputs_kv
+            inputs_v = inputs_kv
+        else:
+            inputs_k = inputs_q
+            inputs_v = inputs_q
+
+        xq, _ = self.wq(inputs_q)
+        xk, _ = self.wk(inputs_k)
+        xv, _ = self.wv(inputs_v)
+
+        output = self.attn(xq, xk, xv)
+        output, _ = self.wo(output)
+
+        return output
+
+
+class ResidualAttentionBlock(nn.Module):
+    """Residual attention block used in Vision Transformer."""
+
+    def __init__(
+        self,
+        config: VisionBackboneConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.attention = MultiHeadDotProductAttention(
+            config, quant_config=quant_config)
+        self.feed_forward = ViTMLP(config, quant_config)
+        self.attention_norm = nn.LayerNorm(
+            config.image_emb_dim,
+            eps=config.image_norm_eps,
+        )
+        self.ffn_norm = nn.LayerNorm(
+            config.image_emb_dim,
+            eps=config.image_norm_eps,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.attention(self.attention_norm(x))
+        x = x + self.feed_forward(self.ffn_norm(x))
+        return x
+
+
+class BlockCollection(nn.Module):
+    """Collection of residual attention blocks used in Vision Transformer."""
+
+    def __init__(
+        self,
+        config: VisionBackboneConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.resblocks = nn.ModuleList([
+            ResidualAttentionBlock(config, quant_config)
+            for _ in range(config.image_num_layers)
+        ])
+
+    def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
+        hidden_states = []
+        for r in self.resblocks:
+            x = r(x)
+            hidden_states.append(x)
+        return hidden_states
+
+
+def _expand_token(token: torch.Tensor, batch_size: int) -> torch.Tensor:
+    return token.view(1, 1, -1).expand(batch_size, -1, -1)
+
+
+class VisionTransformer(nn.Module):
+    """Vision Transformer used in Vision Backbone."""
+
+    def __init__(
+        self,
+        config: VisionBackboneConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        scale = config.image_emb_dim**-0.5
+        self.patch_num = config.image_num_patch
+        self.class_embedding = nn.Parameter(
+            torch.randn(config.image_emb_dim) * scale)
+        self.num_prefix_tokens: int = NUM_PREFIX_TOKENS
+        self.positional_embedding = nn.Parameter(
+            torch.randn(config.image_num_pos, config.image_emb_dim) * scale)
+        image_patch_size = config.image_patch_size
+        self.patch_embedding = nn.Linear(
+            image_patch_size * image_patch_size * 3,
+            config.image_emb_dim,
+            bias=False,
+        )
+        self.pre_ln = nn.LayerNorm(config.image_emb_dim,
+                                   eps=config.image_norm_eps)
+        self.transformer = BlockCollection(config, quant_config)
+
+    def add_pos_emb(self, x: torch.Tensor, patch_num: int) -> torch.Tensor:
+        cls_emb = self.positional_embedding[0:1]
+        pos_emb = self.positional_embedding[1:]
+
+        pos_emb = pos_emb.reshape(
+            (int(math.sqrt(pos_emb.shape[0])),
+             int(math.sqrt(pos_emb.shape[0])), pos_emb.shape[1]))
+
+        (patch_num_0, patch_num_1) = patch_num
+
+        if pos_emb.shape[0] != patch_num_0 or pos_emb.shape[1] != patch_num_1:
+            # from https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
+            pos_emb = pos_emb.unsqueeze(0).permute(0, 3, 1, 2)
+            pos_emb = F.interpolate(
+                pos_emb,
+                size=(patch_num_0, patch_num_1),
+                mode="bicubic",
+                align_corners=False,
+                antialias=True,
+            )
+            pos_emb = pos_emb.permute(0, 2, 3, 1).squeeze(0)
+
+        pos_emb = pos_emb.reshape(-1, pos_emb.shape[-1])
+        x = x + torch.cat([cls_emb[None, :, :], pos_emb[None, :, :]],
+                          dim=1).to(x.dtype)
+        return x
+
+    def forward(self,
+                x: torch.Tensor,
+                patch_num: Optional[int] = None) -> list[torch.Tensor]:
+        """
+        : param x: (batch_size, num_patch, n_pixels)
+        """
+        if patch_num is None:
+            patch_num = self.patch_num
+        B, N, D = x.shape
+
+        x = self.patch_embedding(x)
+
+        # class embeddings and positional embeddings
+        x = torch.cat(
+            [_expand_token(self.class_embedding, x.shape[0]).to(x.dtype), x],
+            dim=1)
+        x = self.add_pos_emb(x, patch_num)
+
+        x = self.pre_ln(x)
+
+        hidden_states = self.transformer(x)
+        return hidden_states
+
+
+class MolmoAttention(nn.Module):
+    """Molmo's LLM attention."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = config.num_attention_heads
+
+        assert self.hidden_size % self.total_num_heads == 0
+        assert self.total_num_heads % self.tp_size == 0
+
+        self.num_heads = self.total_num_heads // self.tp_size
+        self.total_num_kv_heads = config.num_key_value_heads \
+            or self.total_num_heads
+        if self.total_num_kv_heads >= self.tp_size:
+            assert self.total_num_kv_heads % self.tp_size == 0
+        else:
+            assert self.tp_size % self.total_num_kv_heads == 0
+
+        self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
+        self.head_dim = self.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+
+        # Attention input projection. Projects x -> (q, k, v)
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=config.qkv_bias,
+            quant_config=quant_config,
+        )
+
+        self.tp_rank: Optional[int] = None
+        self.k_norm: Optional[nn.Module] = None
+        self.q_norm: Optional[nn.Module] = None
+        if config.attention_layer_norm:
+            self.tp_rank = get_tensor_model_parallel_rank()
+            self.k_norm = RMSNorm(self.total_num_kv_heads * self.head_dim,
+                                  eps=config.layer_norm_eps)
+            self.q_norm = RMSNorm(config.hidden_size,
+                                  eps=config.layer_norm_eps)
+
+        # Rotary embeddings.
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+        self.scaling = self.head_dim**-0.5
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+        # Attention output projection.
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+    def _apply_qk_norm(self, q: torch.Tensor,
+                       k: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        if self.tp_size > 1:
+            q = tensor_model_parallel_all_gather(q.contiguous())
+            k = tensor_model_parallel_all_gather(k.contiguous())
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+        if self.tp_size > 1:
+            splitter = partial(split_tensor_along_last_dim,
+                               num_partitions=self.tp_size)
+            q = splitter(q)[self.tp_rank]
+            k = splitter(k)[self.tp_rank]
+        return q, k
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if self.q_norm is not None and self.k_norm is not None:
+            q, k = self._apply_qk_norm(q, k)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class LanguageModelMLP(nn.Module):
+    """Molmo's LLM mlp."""
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 input_dim: Optional[int] = None,
+                 quant_config: Optional[QuantizationConfig] = None) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size // 2
+
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_dim or self.hidden_size,
+            [self.intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+        )
+        # Activation function.
+        self.act_fn = MulAndSilu()
+        # Feed-forward output projection.
+        self.down_proj = RowParallelLinear(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class ImageProjectorMLP(nn.Module):
+    """Molmo's image_projector mlp."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        input_dim: Optional[int] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size // 2
+
+        self.merged_linear = MergedColumnParallelLinear(
+            input_dim or self.hidden_size,
+            [self.intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+        )
+        # Activation function.
+        self.act_fn = SiluAndMul()
+
+        # Feed-forward output projection.
+        self.down_proj = RowParallelLinear(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        gate_up, _ = self.merged_linear(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class MolmoDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        # Attention block.
+        self.self_attn = MolmoAttention(config,
+                                        cache_config,
+                                        quant_config,
+                                        prefix=f"{prefix}.self_attn")
+
+        # MLP block.
+        self.mlp = LanguageModelMLP(config, quant_config=quant_config)
+
+        # LayerNorm
+        assert config.layer_norm_type == "rms"
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.layer_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, Optional[tuple[torch.Tensor, torch.Tensor]]]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class MolmoDecoderNormAfterLayer(MolmoDecoderLayer):
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, Optional[tuple[torch.Tensor, torch.Tensor]]]:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = hidden_states + residual
+        residual = hidden_states
+
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = hidden_states + residual
+        residual = None
+        return hidden_states, residual
+
+
+class MolmoVisionBackbone(nn.Module, SupportsQuant):
+    packed_modules_mapping = {"merged_linear": ["gate_proj", "up_proj"]}
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        vision_config: VisionBackboneConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.vit_layers = VIT_LAYERS
+        self.image_num_patch = vision_config.image_num_patch
+        self.llm_patches_per_crop = (
+            (self.image_num_patch[0] + 1) // POOLING_SIZE,
+            (self.image_num_patch[1] + 1) // POOLING_SIZE,
+        )
+        self.image_vit = VisionTransformer(vision_config,
+                                           quant_config=quant_config)
+        self.num_prefix_tokens = self.image_vit.num_prefix_tokens
+        assert self.num_prefix_tokens in {
+            0, 1
+        }, "Only 0 or 1 prefix tokens are supported"
+        self.image_pooling_2d = MultiHeadDotProductAttention(
+            vision_config,
+            nlayers=len(self.vit_layers),
+            quant_config=quant_config)
+        self.image_projector = ImageProjectorMLP(
+            config,
+            input_dim=vision_config.image_emb_dim,
+            quant_config=quant_config,
+        )
+
+        image_dim = vision_config.image_emb_dim * len(self.vit_layers)
+        self.pad_embed = nn.Parameter(torch.zeros((2, image_dim)))
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.image_vit.patch_embedding.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.image_vit.patch_embedding.weight.device
+
+    def encode_image(self, images: torch.Tensor) -> torch.Tensor:
+        """
+        : param images: (batch_size, num_crops, num_patch, n_pixels)
+        """
+        B, T, N, D = images.shape
+
+        mask = ~torch.all(
+            images.view(B * T, N, D) == -1, dim=(1, 2), keepdim=True)
+
+        images = images.view(B * T, N, D)
+        image_features = self.image_vit(images)
+
+        if self.vit_layers is not None:
+            features = []
+            for layer in self.vit_layers:
+                features.append(image_features[layer])
+            image_features = torch.cat(features, dim=-1)
+        else:
+            image_features = image_features[-1]
+
+        if self.num_prefix_tokens > 0:
+            image_features = image_features[:, 1:]
+
+        image_features = image_features * mask
+        image_features = image_features.view(B, T, N, -1)
+
+        return image_features
+
+    def forward(
+        self,
+        images: torch.Tensor,
+        image_masks: torch.Tensor,
+    ) -> torch.Tensor:
+        # image_features: (batch_size, num_crops(=num_image), num_patch, nximage_emb_dim) # noqa: E501
+        batch_size, num_image = images.shape[:2]
+        images = images.to(device=self.device, dtype=self.dtype)
+        image_features = self.encode_image(images)
+
+        og_dtype = image_features.dtype
+        assert image_masks is not None
+        pad_embed = self.pad_embed[:, None, None, None, :]
+        all_pad = image_masks == 0
+        partial_pad = torch.logical_and(
+            image_masks < 1,
+            torch.logical_not(all_pad)).to(dtype=torch.float32)
+        all_pad = all_pad.to(dtype=torch.float32)
+        image_features = image_features + pad_embed[0] * torch.unsqueeze(
+            all_pad, -1)
+        image_features = image_features + pad_embed[1] * torch.unsqueeze(
+            partial_pad, -1)
+
+        image_features = image_features.to(og_dtype)
+
+        image_features = image_features.reshape(
+            (batch_size, num_image) + self.image_num_patch + (-1, ), )
+
+        if (missing_w := self.image_num_patch[0] % POOLING_SIZE):
+            # Padding for image pooling (see below)
+            image_features = F.pad(
+                image_features,
+                (0, 0, 0, missing_w, 0, missing_w, 0, 0, 0, 0),
+            )
+
+        # image pooling
+        image_features = rearrange(
+            image_features,
+            'b n (h dh) (w dw) c -> (b n h w) (dh dw) c',
+            dh=POOLING_SIZE,
+            dw=POOLING_SIZE,
+        )
+
+        query = image_features.mean(-2, keepdim=True)
+        image_features = self.image_pooling_2d(query, image_features)
+
+        h, w = self.llm_patches_per_crop
+        image_features = image_features.view(batch_size, num_image, h * w, -1)
+
+        image_features = self.image_projector(image_features)
+
+        # image_features: (batch_size, num_image, num_patch, d_model)
+        return image_features
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("merged_linear", "gate_proj", 0),
+            ("merged_linear", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+@support_torch_compile
+class MolmoModel(nn.Module, SupportsQuant):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+
+        self.embedding_size = config.embedding_size or config.vocab_size
+        self.embedding_size += ADDITIONAL_VOCAB_SIZE
+        self.embed_tokens = VocabParallelEmbedding(
+            self.embedding_size,
+            config.hidden_size,
+            quant_config=quant_config,
+        )
+
+        decoder_layer = MolmoDecoderNormAfterLayer if config.norm_after \
+            else MolmoDecoderLayer
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: decoder_layer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+
+        assert config.layer_norm_type == "rms"
+        self.norm = RMSNorm(config.hidden_size, config.layer_norm_eps)
+
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_tokens(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        # Apply blocks one-by-one.
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        if residual is not None:
+            hidden_states, _ = self.norm(hidden_states, residual)
+        else:
+            hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+def _lowest_multiple(x: int, k: int) -> int:
+    return (x // k) * k
+
+
+def get_num_patches(
+    num_tiles: int,
+    *,
+    crop_patches: int,
+    left_margin: int,
+    right_margin: int,
+    pooling_size: int,
+) -> int:
+    if num_tiles == 1:
+        return _lowest_multiple(crop_patches + pooling_size - 1, pooling_size)
+
+    crop_window_patches = crop_patches - (left_margin + right_margin)
+
+    left_num = _lowest_multiple(
+        crop_window_patches + left_margin + pooling_size - 1,
+        pooling_size,
+    )
+    middle_num = _lowest_multiple(
+        crop_window_patches + pooling_size - 1,
+        pooling_size,
+    )
+    right_num = _lowest_multiple(
+        crop_window_patches + right_margin + pooling_size - 1,
+        pooling_size,
+    )
+
+    return left_num + (num_tiles - 2) * middle_num + right_num
+
+
+def get_patches_grid_size(
+    *,
+    tiling_h: int,
+    tiling_w: int,
+    crop_patches: int,
+    left_margin: int,
+    right_margin: int,
+    pooling_size: int,
+) -> tuple[int, int]:
+    nrows = get_num_patches(
+        tiling_h,
+        crop_patches=crop_patches,
+        left_margin=left_margin,
+        right_margin=right_margin,
+        pooling_size=pooling_size,
+    )
+    ncols = get_num_patches(
+        tiling_w,
+        crop_patches=crop_patches,
+        left_margin=left_margin,
+        right_margin=right_margin,
+        pooling_size=pooling_size,
+    )
+
+    return nrows, ncols
+
+
+def get_candidate_tilings(max_num: int) -> list[tuple[int, int]]:
+    tilings = [(i, j) for i in range(1, max_num + 1)
+               for j in range(1, max_num + 1) if i * j <= max_num]
+    return sorted(tilings, key=lambda x: x[0] * x[1])
+
+
+def select_tiling(
+    *,
+    height: int,
+    width: int,
+    patch_size: int,
+    max_num_patches: int,
+):
+    tilings = get_candidate_tilings(max_num_patches)
+    candidate_tilings = np.array(tilings, dtype=np.int32)
+    candidate_resolutions = candidate_tilings * patch_size
+
+    original_size = np.array([height, width], dtype=np.float32)
+    required_scale_d = candidate_resolutions.astype(np.float32) / original_size
+    required_scale = required_scale_d.min(axis=-1, keepdims=True)
+
+    if (required_scale < 1).all():
+        ix = required_scale.argmax()
+    else:
+        ix = np.where(required_scale < 1.0, 10e9, required_scale).argmin()
+
+    return candidate_tilings[ix]
+
+
+class MolmoProcessorWrapper:
+    """
+    Wraps `MolmoProcessor` so that it can be called directly.
+
+    The original definition can be found here:
+    https://huggingface.co/allenai/Molmo-7B-D-0924/blob/main/preprocessing_molmo.py
+    """
+
+    def __init__(self, processor: ProcessorMixin):
+        super().__init__()
+
+        self.processor = processor
+
+    @cached_property
+    def vocab(self) -> dict[str, int]:
+        return self.processor.tokenizer.vocab  # type: ignore
+
+    @cached_property
+    def max_crops(self) -> int:
+        image_processor = self.processor.image_processor  # type: ignore
+
+        max_crops = image_processor.max_crops
+        assert isinstance(max_crops, int)
+
+        return max_crops
+
+    @cached_property
+    def base_image_input_size(self) -> tuple[int, int]:
+        image_processor = self.processor.image_processor  # type: ignore
+
+        base_image_input_size = image_processor.base_image_input_size
+        if isinstance(base_image_input_size, int):
+            return base_image_input_size, base_image_input_size
+
+        return tuple(base_image_input_size)
+
+    @cached_property
+    def image_patch_size(self) -> int:
+        image_processor = self.processor.image_processor  # type: ignore
+
+        image_patch_size = image_processor.image_patch_size
+        assert isinstance(image_patch_size, int)
+
+        return image_patch_size
+
+    @cached_property
+    def overlap_margins(self) -> tuple[int, int]:
+        image_processor = self.processor.image_processor  # type: ignore
+
+        left_margin, right_margin = image_processor.overlap_margins
+        assert isinstance(left_margin, int)
+        assert isinstance(right_margin, int)
+
+        return left_margin, right_margin
+
+    @cached_property
+    def image_token_length_w(self) -> int:
+        image_processor = self.processor.image_processor  # type: ignore
+
+        image_token_length_w = image_processor.image_token_length_w
+        assert isinstance(image_token_length_w, int)
+
+        return image_token_length_w
+
+    @cached_property
+    def image_token_length_h(self) -> int:
+        image_processor = self.processor.image_processor  # type: ignore
+
+        image_token_length_h = image_processor.image_token_length_h
+        assert isinstance(image_token_length_h, int)
+
+        return image_token_length_h
+
+    @property
+    def message_format(self) -> Optional[str]:
+        return "role"
+
+    @property
+    def always_start_with_space(self) -> bool:
+        return True
+
+    @cached_property
+    def image_patch_id(self) -> int:
+        return self.vocab[IMAGE_PATCH_TOKEN]
+
+    @cached_property
+    def im_col_id(self) -> int:
+        return self.vocab[IM_COL_TOKEN]
+
+    @cached_property
+    def im_start_id(self) -> int:
+        return self.vocab[IM_START_TOKEN]
+
+    @cached_property
+    def im_end_id(self) -> int:
+        return self.vocab[IM_END_TOKEN]
+
+    @property
+    def pooling_size(self) -> int:
+        return POOLING_SIZE
+
+    def select_tiling(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> tuple[int, int]:
+        max_crops = self.max_crops
+        left_margin, right_margin = self.overlap_margins
+        base_image_input_size = self.base_image_input_size
+        base_image_input_d = self.image_patch_size
+
+        total_margin_pixels = base_image_input_d * (right_margin + left_margin)
+        crop_patches = base_image_input_size[0] // base_image_input_d
+        crop_window_patches = crop_patches - (right_margin + left_margin)
+        crop_window_size = crop_window_patches * base_image_input_d
+        tiling_h, tiling_w = select_tiling(
+            height=image_height - total_margin_pixels,
+            width=image_width - total_margin_pixels,
+            patch_size=crop_window_size,
+            max_num_patches=max_crops,
+        )
+
+        return tiling_w, tiling_h
+
+    def get_patches_grid_size(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> tuple[int, int]:
+        left_margin, right_margin = self.overlap_margins
+        base_image_input_size = self.base_image_input_size
+        base_image_input_d = self.image_patch_size
+        pooling_size = self.pooling_size
+
+        crop_patches = base_image_input_size[0] // base_image_input_d
+        tiling_w, tiling_h = self.select_tiling(
+            image_height=image_height,
+            image_width=image_width,
+        )
+
+        nrows, ncols = get_patches_grid_size(
+            tiling_h=tiling_h,
+            tiling_w=tiling_w,
+            crop_patches=crop_patches,
+            left_margin=left_margin,
+            right_margin=right_margin,
+            pooling_size=pooling_size,
+        )
+
+        return ncols, nrows
+
+    def __call__(
+        self,
+        text: Optional[Union[TextInput, list[TextInput]]] = None,
+        images: Optional[Union[ImageInput, list[ImageInput]]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **kwargs,
+    ) -> BatchFeature:
+        outputs = self.processor.process(  # type: ignore
+            text, images, **kwargs)
+
+        if images is None:
+            images = []
+        if not isinstance(images, list):
+            images = [images]
+
+        input_ids: torch.Tensor = outputs.pop("input_ids")
+        outputs["input_ids"] = input_ids.unsqueeze(0)
+
+        image_input_idx = outputs.pop("image_input_idx", None)
+        if image_input_idx is not None:
+            feat_is_patch = image_input_idx >= 0
+
+            tilings = [
+                self.select_tiling(
+                    image_width=image.size[0],
+                    image_height=image.size[1],
+                ) for image in images
+            ]
+            # For each image: tiling_h * tiling_w + extra
+            num_crops = torch.tensor(tilings).prod(-1) + 1
+            assert num_crops.sum() == len(feat_is_patch)
+
+            outputs["feat_is_patch"] = feat_is_patch
+            outputs["num_crops"] = num_crops
+            outputs["img_patch_id"] = self.image_patch_id
+
+        return BatchFeature(outputs)
+
+
+class MolmoProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_processor(self, **kwargs: object) -> MolmoProcessorWrapper:
+        processor = self.ctx.get_hf_processor(**kwargs)
+        return MolmoProcessorWrapper(processor)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[MolmoProcessorWrapper],
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        ncols, nrows = processor.get_patches_grid_size(
+            image_width=image_width,
+            image_height=image_height,
+        )
+        pooling_size = processor.pooling_size
+
+        image_token_length_w = processor.image_token_length_w
+        image_token_length_h = processor.image_token_length_h
+
+        extra = image_token_length_w * image_token_length_h
+        joint = ((ncols + 1) // pooling_size) * ((nrows + 1) // pooling_size)
+
+        return extra + joint
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        processor = self.get_hf_processor()
+
+        tilings = get_candidate_tilings(processor.max_crops)
+        base_h, base_w = processor.base_image_input_size
+
+        largest_feature_size, largest_feature_pinpoint = 0, None
+        for wr, hr in tilings:
+            width, height = base_w * wr, base_h * hr
+
+            feat_size = self.get_num_image_tokens(
+                image_width=width,
+                image_height=height,
+                processor=processor,
+            )
+            if feat_size > largest_feature_size:
+                largest_feature_size = feat_size
+                largest_feature_pinpoint = ImageSize(width=width,
+                                                     height=height)
+
+        if largest_feature_size == 0 or largest_feature_pinpoint is None:
+            raise ValueError("Cannot have a largest feature size of 0!")
+
+        return largest_feature_pinpoint
+
+
+class MolmoDummyInputsBuilder(BaseDummyInputsBuilder[MolmoProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        return ""
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+        num_images = mm_counts.get("image", 0)
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+
+class MolmoMultiModalProcessor(BaseMultiModalProcessor[MolmoProcessingInfo]):
+
+    def _apply_hf_processor_tokens_only(
+        self,
+        prompt_tokens: list[int],
+    ) -> list[int]:
+        processor = self.info.get_hf_processor()
+
+        # Apply the chat template to the tokens
+        tokens = processor.processor.get_tokens_input(  # type: ignore
+            self.info.get_tokenizer().decode(prompt_tokens),
+            message_format=processor.message_format,
+            always_start_with_space=processor.always_start_with_space,
+        )
+
+        processed_data = self.info.ctx.call_hf_processor(
+            processor,  # type: ignore
+            dict(tokens=tokens),
+        )
+        prompt_ids, = processed_data.pop("input_ids").tolist()
+
+        return prompt_ids
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        num_crops = hf_inputs.get("num_crops", torch.empty(0))
+        num_images = len(num_crops)
+
+        return dict(
+            images=MultiModalFieldConfig.flat_from_sizes("image", num_crops),
+            image_masks=MultiModalFieldConfig.flat_from_sizes(
+                "image", num_crops),
+            feat_is_patch=MultiModalFieldConfig.flat_from_sizes(
+                "image", num_crops),
+            num_crops=MultiModalFieldConfig.batched("image"),
+            img_patch_id=MultiModalFieldConfig.shared("image", num_images),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        image_token_length_w = processor.image_token_length_w
+        image_token_length_h = processor.image_token_length_h
+        pooling_size = processor.pooling_size
+
+        img_patch_id = processor.image_patch_id
+        img_col_id = processor.im_col_id
+        img_start_id = processor.im_start_id
+        img_end_id = processor.im_end_id
+
+        extra_row = [img_patch_id] * image_token_length_w + [img_col_id]
+        extra_joint = ([img_start_id] + extra_row * image_token_length_h +
+                       [img_end_id])
+
+        def get_insertion_molmo(item_idx: int):
+            images = mm_items.get_items("image", ImageProcessorItems)
+            image_size = images.get_image_size(item_idx)
+
+            ncols, nrows = processor.get_patches_grid_size(
+                image_width=image_size.width,
+                image_height=image_size.height,
+            )
+
+            joint_row = ([img_patch_id] * ((ncols + 1) // pooling_size) +
+                         [img_col_id])
+            joint = ([img_start_id] + joint_row *
+                     ((nrows + 1) // pooling_size) + [img_end_id])
+
+            return PromptUpdateDetails.select_token_id(
+                extra_joint + joint,
+                embed_token_id=img_patch_id,
+            )
+
+        return [
+            PromptInsertion(
+                modality="image",
+                target=PromptIndexTargets.prefix("<|endoftext|>"),
+                insertion=get_insertion_molmo,
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(MolmoMultiModalProcessor,
+                                        info=MolmoProcessingInfo,
+                                        dummy_inputs=MolmoDummyInputsBuilder)
+class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA,
+                       SupportsQuant):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_substr={
+            # vision backbone mapping
+            "image_projector.w1.": "image_projector.gate_proj.",
+            "image_projector.w3.": "image_projector.up_proj.",
+            "image_projector.w2.": "image_projector.down_proj.",
+            # language backbone mapping
+            "att_proj": "self_attn.qkv_proj",
+            "attn_out": "self_attn.o_proj",
+            "q_norm": "self_attn.q_norm",
+            "k_norm": "self_attn.k_norm",
+            "ff_proj": "mlp.gate_up_proj",
+            "ff_out": "mlp.down_proj",
+            "attn_norm": "input_layernorm",
+            "ff_norm": "post_attention_layernorm",
+        },
+        orig_to_new_prefix={
+            # vision backbone mapping
+            "model.vision_backbone.": "vision_backbone.",
+            # language backbone mapping
+            "model.transformer.blocks.": "model.layers.",
+            "model.transformer.ln_f.": "model.norm.",
+            # lm_head is renamed to model.transformer.mlp.down_proj firstly,
+            # we need to run a second renaming for it
+            "model.transformer.mlp.down_proj.": "lm_head.",
+        },
+    )
+
+    packed_modules_mapping = {
+        "qkv_proj": ["qkv_proj"],
+        "gate_up_proj": ["gate_up_proj"],  # language model
+        "merged_linear": ["gate_proj", "up_proj"]  # image_projector
+    }
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return None
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.multimodal_config = multimodal_config
+        self.lora_config = lora_config
+
+        vision_config = VisionBackboneConfig()
+        self.vision_backbone = MolmoVisionBackbone(config, vision_config,
+                                                   quant_config)
+        self.model = MolmoModel(vllm_config=vllm_config,
+                                prefix=maybe_prefix(prefix, "model"))
+        self.img_patch_id = None
+
+        if self.config.weight_tying:
+            self.lm_head = self.model.transformer.wte
+        else:
+            self.lm_head = ParallelLMHead(
+                config.embedding_size or config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+            )
+
+        self.logits_processor = LogitsProcessor(config.embedding_size
+                                                or config.vocab_size)
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def _parse_and_validate_image_input(
+        self,
+        **kwargs: object,
+    ) -> Optional[MolmoImageInputs]:
+        images = kwargs.pop("images", None)
+        if images is None:
+            return None
+
+        if not isinstance(images, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of images. "
+                             f"Got type: {type(images)}")
+
+        image_masks = kwargs.pop("image_masks", None)
+        if not (image_masks is None or isinstance(image_masks,
+                                                  (torch.Tensor, list))):
+            raise ValueError("Incorrect type of image_masks. "
+                             f"Got type: {type(image_masks)}")
+
+        feat_is_patch = kwargs.pop("feat_is_patch", None)
+        if not isinstance(feat_is_patch, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of feat_is_patch. "
+                             f"Got type: {type(feat_is_patch)}")
+
+        num_crops = kwargs.pop("num_crops", None)
+        if not isinstance(num_crops, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of num_crops. "
+                             f"Got type: {type(num_crops)}")
+
+        img_patch_id = kwargs.pop("img_patch_id", None)
+        if not isinstance(img_patch_id, torch.Tensor):
+            raise ValueError("Incorrect type of img_patch_id. "
+                             f"Got type: {type(img_patch_id)}")
+        self.img_patch_id = img_patch_id.flatten().unique().item()
+
+        num_crops = flatten_bn(num_crops, concat=True)
+
+        return MolmoImageInputs(
+            images=images,
+            image_masks=image_masks,
+            feat_is_patch=feat_is_patch,
+            num_crops=num_crops,
+        )
+
+    def _process_image_input(
+        self,
+        image_input: MolmoImageInputs,
+    ) -> list[torch.Tensor]:
+        images = image_input["images"]
+        image_masks = image_input["image_masks"]
+        feat_is_patch = image_input["feat_is_patch"]
+        num_crops = image_input["num_crops"]
+
+        # Call the vision backbone on the whole batch at once
+        images_flat = flatten_bn(images, concat=True)
+        image_masks_flat = (None if image_masks is None else flatten_bn(
+            image_masks, concat=True))
+        feat_is_patch_flat = flatten_bn(feat_is_patch, concat=True)
+
+        image_features_flat = self.vision_backbone(
+            images=images_flat.unsqueeze(0),
+            image_masks=(None if image_masks_flat is None else
+                         image_masks_flat.unsqueeze(0)),
+        ).squeeze(0)
+
+        # Only the features corresponding to patch tokens are relevant
+        return [
+            feats[f_is_patch] for feats, f_is_patch in zip(
+                image_features_flat.split(num_crops.tolist()),
+                feat_is_patch_flat.split(num_crops.tolist()),
+            )
+        ]
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+
+        return self._process_image_input(image_input)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            assert self.img_patch_id is not None
+
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                self.img_patch_id,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        positions: torch.LongTensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> torch.Tensor:
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.model(input_ids,
+                                   positions,
+                                   intermediate_tensors,
+                                   inputs_embeds=inputs_embeds)
+
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+
+        loader = AutoWeightsLoader(self)
+        weights = _get_weights_with_merged_embedding(weights)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="model",
+            connector="vision_backbone.image_projector",
+            tower_model="vision_backbone",
+        )
+
+
+def _get_weights_with_merged_embedding(
+    weights: Iterable[tuple[str, torch.Tensor]]
+) -> Iterable[tuple[str, torch.Tensor]]:
+    embedding_weights = {}
+    for name, weight in weights:
+        if "wte.embedding" in name:
+            embedding_weights["embedding"] = weight
+        elif "wte.new_embedding" in name:
+            embedding_weights["new_embedding"] = weight
+        else:
+            yield (name, weight)
+    # this is compatible with most of quantization,
+    # because they won't quantize embed_tokens
+    embedding_weights = torch.cat(
+        [embedding_weights["embedding"], embedding_weights["new_embedding"]],
+        dim=0,
+    )
+    yield ("model.embed_tokens.weight", embedding_weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/moonvit.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/moonvit.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0fdab13ef0c95921bb52c06d3c3eb569795477e
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/moonvit.py
@@ -0,0 +1,630 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# ruff: noqa: E501
+# Adapted from https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct/blob/main/modeling_kimi_vl.py
+# This file is meant to be used in kimi_vl.py only
+# Copyright 2025 The Moonshot AI Team, DeepSeek-AI, and HuggingFace Inc. team. All rights reserved.
+#
+# The code is based on llava (llava/modeling_llava.py) and DeepSeek-V3 (DeepSeek-V3/modeling_deepseek.py), but modified for KimiVL.
+#
+# Licensing Information:
+# - Code derived from llava (llava/modeling_llava.py) and DeepSeek-V3 (DeepSeek-V3/modeling_deepseek.py) is licensed under the Apache License, Version 2.0.
+# - Other parts of the code are licensed under the MIT License.
+#
+# Apache License, Version 2.0:
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# MIT License:
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import math
+from collections.abc import Sequence
+from copy import deepcopy
+from functools import cached_property
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.activations import ACT2FN, PytorchGELUTanh
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import is_flash_attn_2_available
+
+from vllm.transformers_utils.configs.moonvit import MoonViTConfig
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_varlen_func
+else:
+    flash_attn_varlen_func = None
+
+
+def multihead_attention(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    q_cu_seqlens: Optional[torch.Tensor] = None,
+    k_cu_seqlens: Optional[torch.Tensor] = None,
+):
+    """Multi-head attention using flash attention 2.
+
+    Args:
+        q, k, v: tensor of shape (batch_size, seqlen, num_heads, head_dim),
+            or (tot_seqlens, num_heads, head_dim) if packing.
+        q_cu_seqlens (torch.Tensor): cumulative sequence lengths of q.
+            The first element should be 0 and the last element should be q.shape[0].
+        k_cu_seqlens (torch.Tensor): cumulative sequence lengths of k.
+            The first element should be 0 and the last element should be k.shape[0].
+
+    Returns:
+        output: shape (batch_size, seqlen, dim) or (tot_seqlens, dim) if packing,
+            where dim = num_heads * head_dim
+    """
+    # Unified format legal check
+    assert q.dim() == k.dim() == v.dim() == 3, "q, k, v must have 3 dims"
+    assert q_cu_seqlens[-1] == q.shape[
+        0], "q_cu_seqlens must sum to q.shape[0]"
+    assert (k_cu_seqlens[-1] == k.shape[0] ==
+            v.shape[0]), "k_cu_seqlens must sum to k.shape[0]"
+    assert q.dtype in [
+        torch.bfloat16,
+        torch.float16,
+    ], f"unsupported dtype {q.dtype} for multihead attn"
+
+    max_seqlen_q = (q_cu_seqlens[1:] - q_cu_seqlens[:-1]).max().item()
+    max_seqlen_k = (k_cu_seqlens[1:] - k_cu_seqlens[:-1]).max().item()
+    attn_out = flash_attn_varlen_func(
+        q,
+        k,
+        v,
+        q_cu_seqlens,
+        k_cu_seqlens,
+        max_seqlen_q,
+        max_seqlen_k,
+        causal=False,
+    )
+    attn_out = attn_out.flatten(start_dim=-2)
+
+    return attn_out
+
+
+def sdpa_attention(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    q_cu_seqlens: Optional[torch.Tensor] = None,
+    k_cu_seqlens: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    """SDPA attention.
+
+    Args:
+        q, k, v: tensor of shape (batch_size, seqlen, num_heads, head_dim),
+            or (tot_seqlens, num_heads, head_dim) if packing.
+    """
+    seq_length = q.shape[0]
+    attention_mask = torch.zeros([1, seq_length, seq_length],
+                                 device=q.device,
+                                 dtype=torch.bool)
+    for i in range(1, len(q_cu_seqlens)):
+        attention_mask[
+            ...,
+            q_cu_seqlens[i - 1]:q_cu_seqlens[i],
+            q_cu_seqlens[i - 1]:q_cu_seqlens[i],
+        ] = True
+    q = q.transpose(0, 1)
+    k = k.transpose(0, 1)
+    v = v.transpose(0, 1)
+    attn_output = F.scaled_dot_product_attention(q,
+                                                 k,
+                                                 v,
+                                                 attention_mask,
+                                                 dropout_p=0.0)
+    attn_output = attn_output.transpose(0, 1)
+    attn_output = attn_output.reshape(seq_length, -1)
+    return attn_output
+
+
+VL_VISION_ATTENTION_FUNCTIONS = {
+    "flash_attention_2": multihead_attention,
+    "sdpa": sdpa_attention,
+}
+
+
+def _apply_rope_input_validation(x, freqs_cis):
+    assert x.ndim == freqs_cis.ndim + 1, (x.shape, freqs_cis.shape)
+    assert x.shape[:-2] == freqs_cis.shape[:-1], (x.shape, freqs_cis.shape)
+    assert x.shape[-1] == 2 * freqs_cis.shape[-1], (x.shape, freqs_cis.shape)
+    assert freqs_cis.dtype == torch.complex64, freqs_cis.dtype
+
+
+def apply_rope(xq: torch.Tensor, xk: torch.Tensor,
+               freqs_cis: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Args: (The leading dimensions of all inputs should be the same)
+        xq: query, tensor of shape (..., num_heads, head_dim)
+        xk: key, tensor of shape (..., num_heads, head_dim)
+        freqs_cis: tensor of shape (..., head_dim/2), dtype=torch.complex64. It contains the precomputed cis(freqs) for each position in the 2D grid.
+    Returns:
+        xq_out, xk_out: tensors of shape (..., num_heads, head_dim)
+    """
+    _apply_rope_input_validation(xq, freqs_cis)
+    _apply_rope_input_validation(xk, freqs_cis)
+
+    freqs_cis = freqs_cis.unsqueeze(-2)  # ..., 1, head_dim/2
+    # ..., num_heads, head_dim/2
+    xq_ = torch.view_as_complex(xq.float().view(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().view(*xq.shape[:-1], -1, 2))
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(
+        -2)  # ..., num_heads, head_dim
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(
+        -2)  # ..., num_heads, head_dim
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+class Learnable2DInterpPosEmb(nn.Module):
+
+    def __init__(self,
+                 height: int,
+                 width: int,
+                 dim: int,
+                 interpolation_mode: str = "bicubic") -> None:
+        super().__init__()
+        self.height = height
+        self.width = width
+        self.interpolation_mode = interpolation_mode
+        self.weight = nn.Parameter(torch.empty(height, width, dim))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.normal_(self.weight)
+
+    def forward(self, x: torch.Tensor, grid_hws: torch.Tensor) -> torch.Tensor:
+        pos_embs = []
+        for shape in grid_hws.tolist():
+            if shape == self.weight.shape[:-1]:
+                pos_embs.append(self.weight.flatten(end_dim=1))
+            else:
+                pos_embs.append(
+                    F.interpolate(
+                        self.weight.permute((2, 0, 1)).unsqueeze(0),
+                        size=shape,
+                        mode=self.interpolation_mode,
+                    ).squeeze(0).permute((1, 2, 0)).flatten(end_dim=1))
+        out = x + torch.cat(pos_embs)
+        return out
+
+
+class MoonVisionPatchEmbed(nn.Module):
+
+    def __init__(
+        self,
+        out_dim: int,
+        in_dim: int = 3,
+        patch_size: Union[int, tuple[int, int]] = (14, 14),
+        pos_emb_height: int = 14,
+        pos_emb_width: int = 14,
+    ):
+        super().__init__()
+        assert isinstance(
+            patch_size,
+            (int, Sequence)), f"Invalid patch_size type: {type(patch_size)}"
+        if isinstance(patch_size, int):
+            patch_size = (patch_size, patch_size)
+        assert (len(patch_size) == 2
+                ), f"Expected patch_size to be a tuple of 2, got {patch_size}"
+        self.patch_size = patch_size
+
+        self.proj = nn.Conv2d(in_dim,
+                              out_dim,
+                              kernel_size=patch_size,
+                              stride=patch_size)
+
+        self.pos_emb = Learnable2DInterpPosEmb(height=pos_emb_height,
+                                               width=pos_emb_width,
+                                               dim=out_dim)
+
+    def forward(self, x: torch.Tensor, grid_hw: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x (L, Channels): input tensor
+            grid_hw (N, 2): grid height and width
+
+        Returns:
+            (L, Cout) tensor
+        """
+        x = self.proj(x).view(x.size(0), -1)
+        # apply positional embedding
+        x = self.pos_emb(x, grid_hw)
+        return x
+
+
+class Rope2DPosEmb(nn.Module):
+    """2D rotary position embedding with multi-resolution support.
+
+    This class is intended to be used in the following way:
+    1. Before training, create an instance of Rope2DPosEmb. This instance will hold the precomputed cis.
+    2. Before each forward pass, call `get_freqs_cis_by_*` to get the `freqs_cis` tensor for this iteration.
+    3. During the forward pass, pass the `freqs_cis` tensor to each attention layer, and call `apply` just before each attention operation.
+        The rope is shared across all attention layers and all heads.
+
+    Refs:
+    - RoFormer: https://arxiv.org/abs/2104.09864
+    - VisionLLaMA: https://arxiv.org/abs/2403.00522
+    - https://github.com/Meituan-AutoML/VisionLLaMA/blob/main/dit/models.py
+
+    Args:
+        dim (int): usually the multi-head attention dimension, should be divisible by 4 (TODO: relax this constraint if needed)
+        max_height (int): the maximum height of the 2D grid
+        max_width (int): the maximum width of the 2D grid
+        theta_base (float): the base of the theta
+        device (str): the device to store the precomputed cis
+    """
+
+    def __init__(self,
+                 dim: int,
+                 max_height: int,
+                 max_width: int,
+                 theta_base=10000,
+                 device="cuda"):
+        super().__init__()
+        self.dim = dim
+        assert self.dim % 4 == 0, "dim must be divisible by 4"
+        self.max_height = max_height
+        self.max_width = max_width
+        self.theta_base = theta_base
+        self.device = device
+
+    def extra_repr(self):
+        return f"dim={self.dim}, max_height={self.max_height}, max_width={self.max_width}, theta_base={self.theta_base}"
+
+    @cached_property
+    def precomputed_freqs_cis(self) -> torch.Tensor:
+        """Calculate the cis(freqs) for each position in the 2D grid.
+
+        Return: complex tensor of shape (max_height, max_width, dim//2) and value:
+            height axis: ret[h, w, 2*i] = cis(h * theta_base**(-4*i/dim))
+            weight axis: ret[h, w, 2*i+1] = cis(w * theta_base**(-4*i/dim))   with (i in [0, dim//4))
+            note: `cis` is a mathematical notation defined by cis x = cos x + i sin x,
+        """
+        N = self.max_height * self.max_width
+        flat_pos = torch.arange(0, N).float().to(self.device)
+        x_pos = flat_pos % self.max_width
+        y_pos = flat_pos // self.max_width
+        dim_range = (torch.arange(0, self.dim,
+                                  4)[:(self.dim // 4)].float().to(self.device)
+                     )  # C/4
+        freqs = 1.0 / (self.theta_base**(dim_range / self.dim))
+        x_freqs = torch.outer(x_pos, freqs).float()  # N, C/4
+        y_freqs = torch.outer(y_pos, freqs).float()  # N, C/4
+        x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs)  # N, C/4
+        y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs)  # N, C/4
+        # N, C/4, 2
+        freqs_cis = torch.cat(
+            [x_cis.unsqueeze(dim=-1),
+             y_cis.unsqueeze(dim=-1)], dim=-1)
+        # max_height, max_width, C/2
+        freqs_cis = freqs_cis.reshape(self.max_height, self.max_width, -1)
+        return freqs_cis
+
+    def get_freqs_cis_by_seqlens(self, grid_hws: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            grid_hws (torch.Tensor): containing list of (height, width) or (t, height, width) tuples.
+        Returns:
+            freqs_cis: tensor of shape (sum(t * height * width), dim//2)
+        """
+        shapes = grid_hws.tolist()
+        assert all(1 <= h <= self.max_height and 1 <= w <= self.max_width
+                   for h, w in shapes), (
+                       shapes,
+                       self.max_height,
+                       self.max_width,
+                   )
+        freqs_cis = torch.cat(
+            [
+                self.precomputed_freqs_cis[:h, :w].reshape(-1, self.dim // 2)
+                for h, w in shapes
+            ],
+            dim=0,
+        )
+        return freqs_cis
+
+    def get_freqs_cis_by_idx(self, pos_idx: torch.Tensor,
+                             pos_idx_mask: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            pos_idx: tensor of shape (..., 2), It contains the (h, w) position indices of each 2D token.
+            pos_idx_mask: a mask of shape (...), the leading dimensions should be the same as pos_idx.
+                Rope will only be applied to the tokens with True mask. `freqs_cis` for the tokens with False mask with be ones.
+        Return:
+            freqs_cis: tensor of shape (..., dim//2)
+        """
+        assert (pos_idx.shape[:-1] == pos_idx_mask.shape
+                and pos_idx.shape[-1] == 2 and pos_idx.ndim
+                == pos_idx_mask.ndim + 1), (pos_idx.shape, pos_idx_mask.shape)
+        assert pos_idx_mask.dtype == torch.bool, pos_idx_mask.dtype
+
+        shp = pos_idx_mask.shape + (self.dim // 2, )  # ..., head_dim/2
+        freqs_cis = torch.ones(shp, dtype=torch.complex64,
+                               device=self.device)  # ..., head_dim/2
+        freqs_cis[pos_idx_mask] = self.precomputed_freqs_cis[pos_idx[
+            ..., 0][pos_idx_mask], pos_idx[..., 1][pos_idx_mask]]
+        return freqs_cis
+
+
+class MLP2(nn.Module):
+    """
+    Args:
+        dims: [in_dim, hidden_dim, out_dim]
+        bias: whether to use bias in linear layer.
+    """
+
+    def __init__(self, dims: list[int], activation, bias=True):
+        super().__init__()
+        assert len(dims) == 3
+        self.fc0 = nn.Linear(dims[0], dims[1], bias=bias)
+        self.fc1 = nn.Linear(dims[1], dims[2], bias=bias)
+        self.activation = activation
+        for m in [self.fc0, self.fc1]:
+            nn.init.trunc_normal_(m.weight, std=math.sqrt(2 / m.in_features))
+            if m.bias is not None:
+                nn.init.zeros_(m.bias)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.fc0(x)
+        x = self.activation(x)
+        return self.fc1(x)
+
+
+class MoonVitEncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        num_heads: int,
+        hidden_dim: int,
+        mlp_dim: int,
+        *,
+        attn_implementation: str = "sdpa",
+        activation=F.gelu,
+        attn_bias: bool = False,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.hidden_dim = hidden_dim
+        self.hidden_size_per_attention_head = self.hidden_dim // self.num_heads
+        self.attn_implementation = attn_implementation
+        # use fa2 in vllm by default
+        if is_flash_attn_2_available():
+            self.attn_implementation = "flash_attention_2"
+
+        self.norm0 = nn.LayerNorm(hidden_dim)
+        self.norm1 = nn.LayerNorm(hidden_dim)
+        self.mlp = MLP2([hidden_dim, mlp_dim, hidden_dim], activation)
+        self.wqkv = nn.Linear(hidden_dim, hidden_dim * 3, bias=attn_bias)
+        self.wo = nn.Linear(hidden_dim, hidden_dim, bias=attn_bias)
+
+    def attention_qkvpacked(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rope_freqs_cis: Optional[torch.Tensor] = None,
+    ):
+        """
+        Args:
+            x (torch.Tensor): (batch_size, seqlen, hidden_dim)
+            cu_seqlens (torch.Tensor):
+        """
+        xqkv = self.wqkv(x)
+
+        qkv_shape = xqkv.size()[:-1] + (
+            3,
+            self.num_heads,
+            self.hidden_size_per_attention_head,
+        )
+        # xqkv: (batch_size, seqlen, 3, nheads, headdim)
+        xqkv = xqkv.view(*qkv_shape)
+        xq, xk, xv = torch.unbind(xqkv, dim=-3)
+
+        xq, xk = apply_rope(xq, xk, rope_freqs_cis)
+
+        attn_func = VL_VISION_ATTENTION_FUNCTIONS[self.attn_implementation]
+        attn_out = attn_func(xq,
+                             xk,
+                             xv,
+                             q_cu_seqlens=cu_seqlens,
+                             k_cu_seqlens=cu_seqlens)
+
+        attn_out = self.wo(attn_out)
+        return attn_out
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rope_freqs_cis: Union[torch.Tensor, None] = None,
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: non-packed (B, N, D) or packed (L, D). if non-packed, seqlens should be None, if packed, seqlens should be set
+
+        Returns:
+            output: same shape of input, non-packed (B, N, D) for non-packed input, (L, D) for packed input
+        """
+        residual = hidden_states
+        hidden_states = self.norm0(hidden_states)
+        attn_out = self.attention_qkvpacked(hidden_states,
+                                            cu_seqlens,
+                                            rope_freqs_cis=rope_freqs_cis)
+        hidden_states = residual + attn_out
+
+        residual = hidden_states
+        hidden_states = self.mlp(self.norm1(hidden_states))
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class MoonVitEncoder(nn.Module):
+
+    def __init__(
+        self,
+        hidden_dim: int,
+        num_layers: int,
+        block_cfg: dict,
+    ) -> None:
+        super().__init__()
+
+        self.rope_2d = Rope2DPosEmb(
+            block_cfg["hidden_dim"] // block_cfg["num_heads"], 512, 512)
+        self.blocks = nn.ModuleList(
+            [MoonVitEncoderLayer(**block_cfg) for _ in range(num_layers)])
+        self.final_layernorm = nn.LayerNorm(hidden_dim)
+
+    def forward(self, hidden_states: torch.Tensor,
+                grid_hw: torch.Tensor) -> torch.Tensor:
+        rope_freqs_cis = self.rope_2d.get_freqs_cis_by_seqlens(
+            grid_hws=grid_hw)
+
+        lengths = torch.cat((
+            torch.zeros(1, device=hidden_states.device, dtype=grid_hw.dtype),
+            grid_hw[:, 0] * grid_hw[:, 1],
+        ))
+        cu_seqlens = lengths.cumsum(dim=0, dtype=torch.int32)
+
+        for _, block in enumerate(self.blocks):
+            hidden_states = block(hidden_states,
+                                  cu_seqlens,
+                                  rope_freqs_cis=rope_freqs_cis)
+
+        hidden_states = self.final_layernorm(hidden_states)
+
+        return hidden_states
+
+
+def patch_merger(
+        x: torch.Tensor,
+        grid_hw: torch.Tensor,
+        merge_kernel_size: list[int, int] = (2, 2),
+) -> list[torch.Tensor]:
+    d_model = x.size(-1)
+
+    outputs = []
+    pre_sum = 0
+    for x_shape in grid_hw.tolist():
+        height, width = x_shape[0], x_shape[1]
+        # Get the current sequence
+        seq = x[pre_sum:pre_sum + height * width]
+        # Reshape along self.merge_kernel_size and concat to the last dimension
+        kernel_height, kernel_width = merge_kernel_size
+        new_height, new_width = height // kernel_height, width // kernel_width
+        reshaped_seq = seq.view(new_height, kernel_height, new_width,
+                                kernel_width, d_model)
+        reshaped_seq = reshaped_seq.permute(0, 2, 1, 3, 4).contiguous()
+        padded_seq = reshaped_seq.view(new_height * new_width,
+                                       kernel_height * kernel_width, -1)
+        outputs.append(padded_seq)
+        pre_sum += height * width
+
+    return outputs
+
+
+class MoonVitVLProjector(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int,
+        merge_kernel_size: list[int, int],
+        hidden_act: str = "gelu",
+        ln_eps: float = 1e-5,
+        out_dim: int = 4096,
+    ):
+        super().__init__()
+        self.hidden_size = in_channels * merge_kernel_size[
+            0] * merge_kernel_size[1]
+
+        self.pre_norm = nn.nn.LayerNorm(in_channels, eps=ln_eps)
+        self.linear_1 = nn.Linear(self.hidden_size,
+                                  self.hidden_size,
+                                  bias=True)
+        self.act = ACT2FN[hidden_act]
+        self.linear_2 = nn.Linear(self.hidden_size, out_dim, bias=True)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.pre_norm(hidden_states).view(-1, self.hidden_size)
+        hidden_states = self.linear_1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class MoonVitPretrainedModel(PreTrainedModel):
+    config_class = MoonViTConfig
+    model_type = "moonvit"
+    _no_split_modules = ["PackingTransformer"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+
+    def __init__(self, config: MoonViTConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        config = deepcopy(config)
+        self.merge_kernel_size = config.merge_kernel_size
+        self.patch_size = config.patch_size
+        self.patch_embed = MoonVisionPatchEmbed(
+            out_dim=config.hidden_size,
+            patch_size=config.patch_size,
+            pos_emb_height=config.init_pos_emb_height,
+            pos_emb_width=config.init_pos_emb_width,
+        )
+
+        self.encoder = MoonVitEncoder(
+            hidden_dim=config.hidden_size,
+            num_layers=config.num_hidden_layers,
+            block_cfg={
+                "num_heads": config.num_attention_heads,
+                "hidden_dim": config.hidden_size,
+                "mlp_dim": config.intermediate_size,
+                "activation": PytorchGELUTanh(),
+                "attn_bias": True,
+                "attn_implementation": config._attn_implementation,
+            },
+        )
+
+    def forward(self, pixel_values: torch.Tensor,
+                grid_hw: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            pixel_values (torch.Tensor): The input pixel values.
+            grid_hw (torch.Tensor): The grid height and width.
+
+        Returns:
+            torch.Tensor: The output tokens.
+        """
+        hidden_states = self.patch_embed(pixel_values, grid_hw)
+        hidden_states = self.encoder(hidden_states, grid_hw)
+        hidden_states = patch_merger(hidden_states,
+                                     grid_hw,
+                                     merge_kernel_size=self.merge_kernel_size)
+        return hidden_states
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mpt.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..8db52a69924c9cfbe16956d12c0efb33527f2324
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/mpt.py
@@ -0,0 +1,331 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from https://huggingface.co/mosaicml/mpt-7b/tree/main
+import math
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+from transformers import MptConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+def _get_alibi_slopes(
+    total_num_heads: int,
+    alibi_bias_max: int,
+) -> torch.Tensor:
+    next_power_of_2 = 2**math.ceil(math.log2(total_num_heads))
+    m = torch.arange(1, next_power_of_2 + 1, dtype=torch.float32)
+    m = m.mul(alibi_bias_max / next_power_of_2)
+    slopes = 1.0 / torch.pow(2, m)
+    if next_power_of_2 != total_num_heads:
+        slopes = torch.concat([slopes[1::2], slopes[::2]])[:total_num_heads]
+    return slopes
+
+
+class MPTAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: MptConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.d_model = config.d_model
+        self.total_num_heads = config.n_heads
+        self.head_dim = self.d_model // self.total_num_heads
+        self.clip_qkv = config.attn_config.clip_qkv
+        self.qk_ln = config.attn_config.qk_ln
+        self.alibi_bias_max = config.attn_config.alibi_bias_max
+        if "kv_n_heads" in config.attn_config:
+            self.total_num_kv_heads = config.attn_config.kv_n_heads
+        else:
+            self.total_num_kv_heads = self.total_num_heads
+        assert not config.attn_config.prefix_lm
+        assert config.attn_config.alibi
+
+        # pylint: disable=invalid-name
+        self.Wqkv = QKVParallelLinear(
+            self.d_model,
+            self.d_model // self.total_num_heads,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=not config.no_bias,
+            quant_config=quant_config,
+        )
+        if self.qk_ln:
+            self.q_ln = nn.LayerNorm(self.d_model)
+            self.k_ln = nn.LayerNorm(self.d_model)
+        self.out_proj = RowParallelLinear(
+            self.d_model,
+            self.d_model,
+            bias=not config.no_bias,
+            quant_config=quant_config,
+        )
+
+        tp_world_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_world_size == 0
+        self.num_heads = self.total_num_heads // tp_world_size
+
+        if self.total_num_kv_heads >= tp_world_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_world_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_world_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_world_size)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        # Create the alibi slopes and slice them.
+        tp_rank = get_tensor_model_parallel_rank()
+        head_start = tp_rank * self.num_heads
+        head_end = (tp_rank + 1) * self.num_heads
+        alibi_slopes = _get_alibi_slopes(self.total_num_heads,
+                                         self.alibi_bias_max)
+        alibi_slopes = alibi_slopes[head_start:head_end].tolist()
+
+        self.head_dim = self.d_model // self.total_num_heads
+        scaling = self.head_dim**-0.5
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              scaling,
+                              alibi_slopes=alibi_slopes,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        del position_ids  # unused.
+        qkv, _ = self.Wqkv(hidden_states)
+        if self.clip_qkv is not None:
+            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if self.qk_ln:
+            q = self.q_ln(q)
+            k = self.k_ln(k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.out_proj(attn_output)
+        return output
+
+
+class MPTMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: MptConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        hidden_size = config.d_model
+        expansion_ratio = config.expansion_ratio
+        intermediate_size = expansion_ratio * hidden_size
+        self.up_proj = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=not config.no_bias,
+            quant_config=quant_config,
+        )
+        self.act = get_act_fn("gelu")
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=not config.no_bias,
+            quant_config=quant_config,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.up_proj(x)
+        x = self.act(x)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class MPTBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: MptConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        hidden_size = config.d_model
+        self.norm_1 = nn.LayerNorm(hidden_size)
+        self.attn = MPTAttention(config,
+                                 cache_config,
+                                 quant_config,
+                                 prefix=f"{prefix}.attn")
+        self.norm_2 = nn.LayerNorm(hidden_size)
+        self.ffn = MPTMLP(config, quant_config)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        x = self.norm_1(hidden_states)
+        x = self.attn(
+            position_ids=position_ids,
+            hidden_states=x,
+        )
+        hidden_states = hidden_states + x
+        x = self.norm_2(hidden_states)
+        x = self.ffn(x)
+        hidden_states = hidden_states + x
+        return hidden_states
+
+
+@support_torch_compile
+class MPTModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        assert config.embedding_fraction == 1.0
+        assert config.norm_type == "low_precision_layernorm"
+
+        self.wte = VocabParallelEmbedding(
+            config.vocab_size,
+            config.d_model,
+        )
+        self.start_layer, self.end_layer, self.blocks = make_layers(
+            config.n_layers,
+            lambda prefix: MPTBlock(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.blocks")
+        self.norm_f = nn.LayerNorm(config.d_model)
+        if config.no_bias:
+            for module in self.modules():
+                if hasattr(module, "bias") and isinstance(
+                        module.bias, nn.Parameter):
+                    # Remove the bias term in Linear and LayerNorm.
+                    module.register_parameter("bias", None)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.d_model))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.wte(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+
+        for block in self.blocks[self.start_layer:self.end_layer]:
+            hidden_states = block(position_ids, hidden_states)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+        hidden_states = self.norm_f(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            if is_pp_missing_parameter(name, self):
+                continue
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class MPTForCausalLM(nn.Module, SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        assert config.tie_word_embeddings
+        self.quant_config = quant_config
+
+        self.transformer = MPTModel(vllm_config=vllm_config,
+                                    prefix=maybe_prefix(prefix, "transformer"))
+        self.lm_head = self.transformer.wte
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.transformer.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.transformer(input_ids, positions,
+                                         intermediate_tensors, inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nemotron_h.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nemotron_h.py
new file mode 100644
index 0000000000000000000000000000000000000000..07cd5a4c6e24f8c97702acb4208075a7399380f7
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nemotron_h.py
@@ -0,0 +1,632 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from https://github.com/vllm-project/vllm/blob/94d8ec8d2bcb4ec55e33022b313c7e978edf05e1/vllm/model_executor/models/bamba.py
+# Copyright 2024 HuggingFace Inc. team. All rights reserved.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only NemotronH model."""
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+from torch import nn
+
+from vllm import envs
+from vllm.attention.layer import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.distributed.parallel_state import get_pp_group
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.activation import ReLUSquaredActivation
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mamba.mamba2_metadata import (
+    Mamba2Metadata, prepare_mamba2_metadata)
+from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
+from vllm.model_executor.layers.mamba.mamba_utils import (
+    MambaStateDtypeCalculator, MambaStateShapeCalculator)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.interfaces import (HasInnerState, IsHybrid,
+                                                   SupportsLoRA, SupportsPP,
+                                                   SupportsQuant)
+from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
+                                                    MambaCacheParams)
+from vllm.model_executor.models.utils import (
+    AutoWeightsLoader, make_empty_intermediate_tensors_factory, make_layers,
+    maybe_prefix)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs import NemotronHConfig
+from vllm.utils import LayerBlockType
+
+
+class NemotronHMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: NemotronHConfig,
+        layer_idx: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        hybrid_override_pattern = config.hybrid_override_pattern
+        mlp_index = hybrid_override_pattern[:layer_idx + 1].count("-") - 1
+        if isinstance(config.intermediate_size, list):
+            if len(config.intermediate_size) == 1:
+                intermediate_size = config.intermediate_size[0]
+            else:
+                intermediate_size = config.intermediate_size[mlp_index]
+        else:
+            intermediate_size = config.intermediate_size
+
+        self.up_proj = ColumnParallelLinear(
+            input_size=config.hidden_size,
+            output_size=intermediate_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            input_size=intermediate_size,
+            output_size=config.hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+        self.act_fn = ReLUSquaredActivation()
+
+    def forward(self, x: torch.Tensor):
+        x, _ = self.up_proj(x)
+        x = self.act_fn(x)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class NemotronHMLPDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: NemotronHConfig,
+        layer_idx: int,
+        model_config: Optional[ModelConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+
+        self.mixer = NemotronHMLP(
+            config,
+            quant_config=quant_config,
+            bias=config.mlp_bias,
+            prefix=f"{prefix}.mixer",
+            layer_idx=layer_idx,
+        )
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        **kwargs,
+    ):
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.norm(hidden_states)
+        else:
+            hidden_states, residual = self.norm(hidden_states, residual)
+
+        hidden_states = self.mixer(hidden_states)
+        return hidden_states, residual
+
+
+class NemotronHMambaDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: NemotronHConfig,
+        layer_idx: int,
+        model_config: Optional[ModelConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.mixer = MambaMixer2(
+            hidden_size=config.hidden_size,
+            ssm_state_size=config.ssm_state_size,
+            conv_kernel_size=config.conv_kernel,
+            intermediate_size=config.mamba_num_heads * config.mamba_head_dim,
+            use_conv_bias=config.use_conv_bias,
+            use_bias=config.use_bias,
+            n_groups=config.n_groups,
+            num_heads=config.mamba_num_heads,
+            head_dim=config.mamba_head_dim,
+            rms_norm_eps=config.rms_norm_eps,
+            activation=config.mamba_hidden_act,
+            model_config=model_config,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mixer",
+        )
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        mamba_cache_params: MambaCacheParams,
+        mamba2_metadata: Mamba2Metadata,
+        **kwargs,
+    ):
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.norm(hidden_states)
+        else:
+            hidden_states, residual = self.norm(hidden_states, residual)
+
+        output = torch.empty_like(hidden_states)
+        self.mixer(hidden_states, output, mamba_cache_params, mamba2_metadata)
+        return output, residual
+
+
+class NemotronHAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: NemotronHConfig,
+        layer_idx: int,
+        model_config: Optional[ModelConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = config.num_key_value_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        if hasattr(config, "head_dim") and config.head_dim is not None:
+            self.head_dim = config.head_dim
+        else:
+            self.head_dim = config.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            config.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class NemotronHAttentionDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: NemotronHConfig,
+        layer_idx: int,
+        model_config: Optional[ModelConfig] = None,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.mixer = NemotronHAttention(
+            config,
+            layer_idx,
+            model_config,
+            cache_config,
+            quant_config,
+            prefix=f"{prefix}.mixer",
+        )
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        **kwargs,
+    ):
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.norm(hidden_states)
+        else:
+            hidden_states, residual = self.norm(hidden_states, residual)
+
+        hidden_states = self.mixer(hidden_states=hidden_states)
+        return hidden_states, residual
+
+
+ALL_DECODER_LAYER_TYPES = {
+    "M": NemotronHMambaDecoderLayer,
+    "-": NemotronHMLPDecoderLayer,
+    "*": NemotronHAttentionDecoderLayer,
+}
+
+
+@support_torch_compile
+class NemotronHModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config: NemotronHConfig = vllm_config.model_config.hf_config
+        model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        lora_vocab = ((lora_config.lora_extra_vocab_size *
+                       (lora_config.max_loras or 1)) if lora_config else 0)
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+
+        def get_layer(prefix: str):
+            layer_idx = int(prefix.rsplit(".", 1)[1])
+            layer_class = ALL_DECODER_LAYER_TYPES[
+                config.hybrid_override_pattern[layer_idx]]
+            return layer_class(
+                config,
+                layer_idx,
+                model_config,
+                cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+            )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            len(config.hybrid_override_pattern),
+            get_layer,
+            prefix=f"{prefix}.layers")
+        self.make_empty_intmd_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size)
+
+        self.norm_f = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        mamba_cache_params: MambaCacheParams,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        attn_metadata = get_forward_context().attn_metadata
+
+        if not envs.VLLM_USE_V1:
+            mamba2_metadata = prepare_mamba2_metadata(
+                chunk_size=self.config.chunk_size,
+                attn_metadata=attn_metadata,
+            )
+        else:
+            # v1 get mamba2_metadata from forward_context
+            mamba2_metadata = None
+
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        residual = None
+        num_non_mamba_layers = 0
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            layer_mamba_cache_params = None
+            if isinstance(layer,
+                          NemotronHMambaDecoderLayer) and mamba_cache_params:
+                layer_mamba_cache_params = mamba_cache_params.at_layer_idx(
+                    i - num_non_mamba_layers)
+            else:
+                num_non_mamba_layers += 1
+
+            hidden_states, residual = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                residual=residual,
+                mamba_cache_params=layer_mamba_cache_params,
+                mamba2_metadata=mamba2_metadata,
+            )
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm_f(hidden_states, residual)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        attb_params_mapping = {
+            "q_proj": "q",
+            "k_proj": "k",
+            "v_proj": "v",
+        }
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "embeddings" in name:
+                name = name.replace("embeddings", "embed_tokens")
+
+            if "A_log" in name:
+                name = name.replace("A_log", "A")
+                loaded_weight = loaded_weight.to(torch.float32)
+
+            if "D" in name:
+                loaded_weight = loaded_weight.to(torch.float32)
+
+            if "dt_bias" in name:
+                loaded_weight = loaded_weight.to(torch.float32)
+
+            # load attn params
+            if any(proj in name for proj in ["q_proj", "k_proj", "v_proj"]):
+                weight_name = next(proj
+                                   for proj in ["q_proj", "k_proj", "v_proj"]
+                                   if proj in name)
+                name = name.replace(weight_name, "qkv_proj")
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight,
+                              attb_params_mapping[weight_name])
+            # load other params
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+            loaded_params.add(name)
+        return loaded_params
+
+
+class NemotronHForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
+                           IsHybrid, SupportsQuant):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    @classmethod
+    def get_mamba_state_dtype_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+    ) -> tuple[torch.dtype, torch.dtype]:
+
+        return MambaStateDtypeCalculator.mamba2_state_dtype(
+            vllm_config.model_config.dtype,
+            vllm_config.cache_config.mamba_cache_dtype,
+            vllm_config.cache_config.mamba_ssm_cache_dtype,
+        )
+
+    @classmethod
+    def get_mamba_state_shape_from_config(
+        cls,
+        vllm_config: "VllmConfig",
+        use_v1: bool = True,
+    ) -> tuple[tuple[int, int], tuple[int, int, int]]:
+        """Calculate shapes for Mamba's convolutional and state caches.
+
+        Args:
+            vllm_config: vLLM config
+            use_v1: Get shapes for V1 (or V0)
+
+        Returns:
+            Tuple containing:
+            - conv_state_shape: Shape for convolutional state cache
+            - temporal_state_shape: Shape for state space model cache
+        """
+        parallel_config = vllm_config.parallel_config
+        hf_config = vllm_config.model_config.hf_config
+        intermediate_size = hf_config.mamba_num_heads * hf_config.mamba_head_dim
+
+        return MambaStateShapeCalculator.mamba2_state_shape(
+            intermediate_size=intermediate_size,
+            tp_world_size=parallel_config.tensor_parallel_size,
+            n_groups=hf_config.n_groups,
+            num_heads=hf_config.mamba_num_heads,
+            head_dim=hf_config.mamba_head_dim,
+            state_size=hf_config.ssm_state_size,
+            conv_kernel=hf_config.conv_kernel,
+            use_v1=use_v1,
+        )
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config = vllm_config.model_config.hf_config
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+        cache_config = vllm_config.cache_config
+        lora_config = vllm_config.lora_config
+        scheduler_config = vllm_config.scheduler_config
+        assert not cache_config.enable_prefix_caching, \
+            "NemotronH currently does not support prefix caching"
+
+        self.quant_config = vllm_config.quant_config
+
+        super().__init__()
+        self.config = config
+        self.scheduler_config = scheduler_config
+        self.model = NemotronHModel(vllm_config=vllm_config,
+                                    prefix=maybe_prefix(prefix, "model"))
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE
+            # We need bigger padding if using lora for kernel
+            # compatibility
+            if not lora_config else lora_config.lora_vocab_padding_size,
+        )
+        # Used to track and store by the Mamba cache between steps.
+        self.mamba_cache: Optional[MambaCacheManager] = None
+
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size)
+
+        self.make_empty_intmd_tensors = (self.model.make_empty_intmd_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs):
+
+        mamba_cache_params = None
+        if not envs.VLLM_USE_V1:
+            if self.mamba_cache is None:
+
+                num_mamba_layers = \
+                    self.model_config.get_num_layers_by_block_type(
+                        self.vllm_config.parallel_config,
+                        LayerBlockType.mamba
+                    )
+                mamba_state_shape = \
+                    self.get_mamba_state_shape_from_config(
+                        self.vllm_config, use_v1=False)
+                mamba_state_dtype = \
+                    self.get_mamba_state_dtype_from_config(
+                    self.vllm_config)
+                self.mamba_cache = MambaCacheManager(self.vllm_config,
+                                                     num_mamba_layers,
+                                                     *mamba_state_shape,
+                                                     *mamba_state_dtype)
+
+            mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
+
+        hidden_states = self.model(input_ids, positions, mamba_cache_params,
+                                   intermediate_tensors, inputs_embeds)
+
+        return hidden_states
+
+    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
+        return self.mamba_cache.copy_inputs_before_cuda_graphs(
+            input_buffers, **kwargs)
+
+    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
+        return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        # update name in weights before passing to loader
+        updated_weights = []
+        for name, loaded_weight in weights:
+            name = name.replace("backbone", "model")
+            updated_weights.append((name, loaded_weight))
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(updated_weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nemotron_nas.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nemotron_nas.py
new file mode 100644
index 0000000000000000000000000000000000000000..a766ed9476a657dba04e9d29f694b0f0730e9959
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nemotron_nas.py
@@ -0,0 +1,484 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only deci model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from transformers import LlamaConfig
+
+from vllm.attention import AttentionType
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.models.llama import LlamaAttention, LlamaMLP
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import HasNoOps, SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
+    # DeciLM-specific code
+    intermediate_size = int(2 * ffn_mult * n_embd / 3)
+    return _find_multiple(intermediate_size, 256)
+
+
+def _find_multiple(n: int, k: int) -> int:
+    # DeciLM-specific code
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+class DeciLMAttention(LlamaAttention):
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        bias_o_proj: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "",
+        attn_type: str = AttentionType.DECODER,
+    ) -> None:
+        super().__init__(config, hidden_size, num_heads, num_kv_heads,
+                         rope_theta, rope_scaling, max_position_embeddings,
+                         quant_config, bias, bias_o_proj, cache_config, prefix,
+                         attn_type)
+
+    def _init_rotary_emb(self, config, rope_scaling: Optional[dict[str, Any]],
+                         quant_config: Optional[QuantizationConfig]) -> None:
+        # Enables YARN for Mistral and LLaMA4 derivatives.
+        is_neox_style = True
+        if hasattr(config, "position_embedding_type"):
+            is_neox_style = config.position_embedding_type not in [
+                "mistral_yarn", "rope_llama4"
+            ]
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=self.rope_theta,
+            rope_scaling=rope_scaling,
+            is_neox_style=is_neox_style,
+            partial_rotary_factor=self.partial_rotary_factor)
+
+
+class DeciLMDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        layer_idx: int,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        block_config = config.block_configs[layer_idx]
+        self._is_no_op_attention = block_config.attention.no_op
+        self._is_no_op_ffn = block_config.ffn.no_op
+
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        if rope_scaling is not None and getattr(
+                config, "original_max_position_embeddings", None):
+            rope_scaling["original_max_position_embeddings"] = (
+                config.original_max_position_embeddings)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        # Support abacusai/Smaug-72B-v0.1 with attention_bias
+        # Support internlm/internlm-7b with bias
+        attention_bias = getattr(config, "attention_bias", False) or getattr(
+            config, "bias", False)
+        bias_o_proj = attention_bias
+        # support internlm/internlm3-8b with qkv_bias
+        if hasattr(config, "qkv_bias"):
+            attention_bias = config.qkv_bias
+
+        if not self._is_no_op_attention:
+            num_kv_heads = (config.num_attention_heads //
+                            block_config.attention.n_heads_in_group)
+            self.self_attn = DeciLMAttention(
+                config=config,
+                hidden_size=self.hidden_size,
+                num_heads=config.num_attention_heads,
+                num_kv_heads=num_kv_heads,
+                rope_theta=rope_theta,
+                rope_scaling=rope_scaling,
+                max_position_embeddings=max_position_embeddings,
+                quant_config=quant_config,
+                bias=attention_bias,
+                bias_o_proj=bias_o_proj,
+                cache_config=cache_config,
+                prefix=f"{prefix}.self_attn",
+            )
+            self.input_layernorm = RMSNorm(config.hidden_size,
+                                           eps=config.rms_norm_eps)
+
+        if not self._is_no_op_ffn:
+            ffn_mult = block_config.ffn.ffn_mult
+            intermediate_size = _ffn_mult_to_intermediate_size(
+                ffn_mult, config.hidden_size)
+
+            self.mlp = LlamaMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                bias=getattr(config, "mlp_bias", False),
+                prefix=f"{prefix}.mlp",
+            )
+            self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                    eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+
+        if self._is_no_op_attention:
+            pass
+        else:
+            if (residual is None):
+                residual = hidden_states
+                hidden_states = self.input_layernorm(hidden_states)
+            else:
+                hidden_states, residual = self.input_layernorm(
+                    hidden_states, residual)
+            hidden_states = self.self_attn(
+                positions=positions,
+                hidden_states=hidden_states,
+            )
+
+        # Fully Connected
+        if not self._is_no_op_ffn:
+            hidden_states, residual = self.post_attention_layernorm(
+                hidden_states, residual)
+            hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class DeciModel(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: type[DeciLMDecoderLayer] = DeciLMDecoderLayer,
+    ):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.quant_config = quant_config
+        self.padding_idx = config.pad_token_id
+        lora_vocab = ((lora_config.lora_extra_vocab_size *
+                       (lora_config.max_loras or 1)) if lora_config else 0)
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+        if get_pp_group().is_first_rank or (config.tie_word_embeddings
+                                            and get_pp_group().is_last_rank):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                quant_config=quant_config,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        def get_layer(prefix: str):
+            layer_idx = int(prefix.rsplit(".", 1)[1])
+            return layer_type(
+                config,
+                layer_idx,
+                cache_config,
+                quant_config=quant_config,
+                prefix=prefix,
+            )
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            get_layer,
+            prefix=f"{prefix}.layers",
+        )
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        kv_cache_index = 0
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            if not layer._is_no_op_attention:
+                hidden_states, residual = layer(positions, hidden_states,
+                                                residual)
+                kv_cache_index += 1
+            else:
+                hidden_states, residual = layer(positions, hidden_states,
+                                                residual)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if ("rotary_emb.cos_cached" in name
+                    or "rotary_emb.sin_cached" in name):
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            if self.quant_config is not None and (
+                    scale_name := self.quant_config.get_cache_scale(name)):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+            if "scale" in name:
+                # Remapping the name of FP8 kv-scale.
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class DeciLMForCausalLM(nn.Module, SupportsLoRA, SupportsPP, HasNoOps):
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    # Mistral/Llama models can also be loaded with --load-format mistral
+    # from consolidated.safetensors checkpoints
+    mistral_mapping = {
+        "layers": "model.layers",
+        "attention": "self_attn",
+        "wq": "q_proj",
+        "wk": "k_proj",
+        "wv": "v_proj",
+        "wo": "o_proj",
+        "attention_norm": "input_layernorm",
+        "feed_forward": "mlp",
+        "w1": "gate_proj",
+        "w2": "down_proj",
+        "w3": "up_proj",
+        "ffn_norm": "post_attention_layernorm",
+        "tok_embeddings": "model.embed_tokens",
+        "output": "lm_head",
+        "norm": "model.norm",
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.lora_config = lora_config
+
+        self.model = self._init_model(vllm_config=vllm_config,
+                                      prefix=maybe_prefix(prefix, "model"))
+
+        if get_pp_group().is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            if lora_config:
+                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=(
+                    DEFAULT_VOCAB_PADDING_SIZE
+                    # We need bigger padding if using lora for kernel
+                    # compatibility
+                    if not lora_config else
+                    lora_config.lora_vocab_padding_size),
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+            if config.tie_word_embeddings:
+                self.lm_head = self.lm_head.tie_weights(
+                    self.model.embed_tokens)
+
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    config.vocab_size,
+                                                    logit_scale)
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def _init_model(self, vllm_config: VllmConfig, prefix: str = ""):
+        return DeciModel(vllm_config=vllm_config, prefix=prefix)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        model_output = self.model(input_ids, positions, intermediate_tensors,
+                                  inputs_embeds)
+        return model_output
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nemotron_vl.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nemotron_vl.py
new file mode 100644
index 0000000000000000000000000000000000000000..82bcd064624f39ee87c4e2f78159d6ba86545b8d
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nemotron_vl.py
@@ -0,0 +1,673 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://huggingface.co/OpenGVLab/InternVL2-4B/blob/main/modeling_internvl_chat.py
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from abc import ABC
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torchvision.transforms as T
+from PIL import Image
+from transformers import AutoModel, PretrainedConfig
+from transformers.image_processing_utils_fast import BaseImageProcessorFast
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.awq import AWQConfig
+from vllm.model_executor.models.internvl import (
+    BaseInternVLDummyInputsBuilder, BaseInternVLMultiModalProcessor,
+    BaseInternVLProcessingInfo, InternVLImageEmbeddingInputs,
+    InternVLImageInputs, InternVLImagePixelInputs, InternVLProcessor)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.image import convert_image_mode
+from vllm.multimodal.inputs import NestedTensors
+from vllm.multimodal.processing import PromptUpdateDetails
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.processor import (
+    cached_image_processor_from_config)
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
+                    maybe_prefix, merge_multimodal_embeddings)
+
+IMG_START = '<img>'
+IMG_END = '</img>'
+IMG_CONTEXT = '<image>'
+
+
+def build_transform(input_size: int):
+    return T.Compose([
+        T.Lambda(lambda img: convert_image_mode(img, 'RGB')),
+        T.Resize((input_size, input_size),
+                 interpolation=T.InterpolationMode.BICUBIC),
+        T.ToTensor(),
+    ])
+
+
+# adapted from https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1
+def find_closest_aspect_ratio(
+    aspect_ratio: float,
+    target_ratios: list[tuple[int, int]],
+    *,
+    width: int,
+    height: int,
+    image_size: int,
+) -> tuple[int, int]:
+    best_factor = float('-inf')
+    best_ratio = (1, 1)
+    area = width * height
+
+    for rw, rh in target_ratios:
+        target_aspect_ratio = rw / rh
+        size_factor = min((rw * rh * image_size * image_size) / area, 0.6)
+        ratio_closeness = min(target_aspect_ratio / aspect_ratio,
+                              aspect_ratio / target_aspect_ratio)
+        factor = size_factor * ratio_closeness
+
+        if factor > best_factor:
+            best_factor = factor
+            best_ratio = (rw, rh)
+
+    return best_ratio
+
+
+def calculate_nemotron_vl_targets(
+    *,
+    orig_width: int,
+    orig_height: int,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> tuple[int, int, int]:
+    aspect_ratio = orig_width / orig_height
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio,
+        target_ratios,
+        width=orig_width,
+        height=orig_height,
+        image_size=image_size,
+    )
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # add thumbnail image if num_blocks != 1
+    if use_thumbnail and blocks != 1:
+        blocks += 1
+
+    return blocks, target_width, target_height
+
+
+def dynamic_preprocess_nemotron_vl(
+    image: Image.Image,
+    *,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> list[Image.Image]:
+    orig_width, orig_height = image.size
+
+    # calculate the number of blocks without thumbnail
+    blocks, target_width, target_height = calculate_nemotron_vl_targets(
+        orig_width=orig_width,
+        orig_height=orig_height,
+        target_ratios=target_ratios,
+        image_size=image_size,
+        use_thumbnail=False,
+    )
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = ((i % (target_width // image_size)) * image_size,
+               (i // (target_width // image_size)) * image_size,
+               ((i % (target_width // image_size)) + 1) * image_size,
+               ((i // (target_width // image_size)) + 1) * image_size)
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+
+    assert len(processed_images) == blocks
+
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+
+    return processed_images
+
+
+def get_nemotron_vl_target_ratios(
+    min_num: int,
+    max_num: int,
+) -> list[tuple[int, int]]:
+    target_ratios = {(i, j)
+                     for n in range(min_num, max_num + 1)
+                     for i in range(1, n + 1)
+                     for j in range(1, n + 1) if min_num <= i * j <= max_num}
+    return sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+
+def image_to_pixel_values_nemotron_vl(
+    image: Image.Image,
+    *,
+    input_size: int,
+    min_num: int,
+    max_num: int,
+    use_thumbnail: bool,
+) -> torch.Tensor:
+    target_ratios = get_nemotron_vl_target_ratios(min_num, max_num)
+
+    transform = build_transform(input_size=input_size)
+
+    images = dynamic_preprocess_nemotron_vl(
+        image,
+        target_ratios=target_ratios,
+        image_size=input_size,
+        use_thumbnail=use_thumbnail,
+    )
+
+    pixel_values = torch.stack([transform(image) for image in images])
+    return pixel_values
+
+
+class NemotronVLProcessor(InternVLProcessor):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        tokenizer: AnyTokenizer,
+        image_processor: BaseImageProcessorFast,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> None:
+        ABC.__init__(self)
+        self.config = config
+        self.tokenizer = tokenizer
+        self.image_processor = image_processor
+        image_size: int = config.force_image_size
+        patch_size: int = config.patch_size
+
+        if min_dynamic_patch is None:
+            min_dynamic_patch = 1
+        assert isinstance(min_dynamic_patch, int)
+
+        if max_dynamic_patch is None:
+            max_dynamic_patch = self.image_processor.max_num_tiles
+        assert isinstance(max_dynamic_patch, int)
+
+        if dynamic_image_size is None:
+            dynamic_image_size = True
+        assert isinstance(dynamic_image_size, bool)
+
+        self.num_image_token = int(
+            (image_size // patch_size)**2 * (config.downsample_ratio**2))
+        self.image_size = image_size
+        self.min_dynamic_patch = min_dynamic_patch
+        self.max_dynamic_patch = max_dynamic_patch
+        self.dynamic_image_size = dynamic_image_size
+        self.use_thumbnail: bool = self.image_processor.use_thumbnail
+
+    @property
+    def image_token_id(self) -> int:
+        return self.tokenizer.get_vocab()[IMG_CONTEXT]
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        target_ratios = self.resolve_target_ratios(
+            use_thumbnail=False,  # Applied in calculate_targets
+        )
+
+        num_patches, _, _ = calculate_nemotron_vl_targets(
+            orig_width=image_width,
+            orig_height=image_height,
+            image_size=self.image_size,
+            target_ratios=target_ratios,
+            use_thumbnail=self.use_thumbnail,
+        )
+
+        return num_patches * self.num_image_token
+
+    def _images_to_pixel_values_lst(
+        self,
+        images: list[Image.Image],
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> list[torch.Tensor]:
+        min_num, max_num = self.resolve_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=False,  # Applied in image_to_pixel_values
+        )
+
+        return [
+            image_to_pixel_values_nemotron_vl(
+                image,
+                input_size=self.image_size,
+                min_num=min_num,
+                max_num=max_num,
+                use_thumbnail=self.use_thumbnail,
+            ) for image in images
+        ]
+
+    def _preprocess_image(
+        self,
+        text: list[str],
+        images: list[Image.Image],
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> tuple[list[str], dict[str, torch.Tensor]]:
+        if len(images) == 0:
+            image_inputs = {}
+        else:
+            pixel_values_lst = self._images_to_pixel_values_lst(
+                images,
+                min_dynamic_patch=min_dynamic_patch,
+                max_dynamic_patch=max_dynamic_patch,
+                dynamic_image_size=dynamic_image_size,
+            )
+            image_inputs: dict[str, NestedTensors] = {
+                "pixel_values_flat":
+                torch.cat(pixel_values_lst),
+                "image_num_patches":
+                torch.tensor([len(item) for item in pixel_values_lst]),
+            }
+
+            for pixel_values in pixel_values_lst:
+                num_patches = pixel_values.shape[0]
+                feature_size = num_patches * self.num_image_token
+                image_repl = self.get_image_repl(feature_size, num_patches)
+                NVL_IMAGE_CONTEXT = image_repl.full.replace(
+                    "<image>", "<NVL_IMG_CONTEXT>")
+                text = [
+                    t.replace('<image>', NVL_IMAGE_CONTEXT, 1) for t in text
+                ]
+            text = [t.replace("<NVL_IMG_CONTEXT>", IMG_CONTEXT) for t in text]
+        return text, image_inputs
+
+    def get_image_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int],
+    ) -> PromptUpdateDetails[str]:
+        repl_features = IMG_CONTEXT * feature_size
+        repl_full = IMG_START + repl_features + IMG_END
+
+        return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
+
+
+class NemotronVLProcessingInfo(BaseInternVLProcessingInfo):
+    """Processing info for Nemotron VL models."""
+
+    def get_hf_processor(self, **kwargs: object) -> NemotronVLProcessor:
+        return self.ctx.init_processor(
+            NemotronVLProcessor,
+            config=self.get_hf_config(),
+            tokenizer=self.get_tokenizer(),
+            image_processor=self.get_image_processor(),
+            **kwargs,
+        )
+
+    def get_image_processor(self, **kwargs: object):
+        return cached_image_processor_from_config(
+            self.ctx.model_config,
+            **kwargs,
+        )
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    BaseInternVLMultiModalProcessor[NemotronVLProcessingInfo],
+    info=NemotronVLProcessingInfo,
+    dummy_inputs=BaseInternVLDummyInputsBuilder[NemotronVLProcessingInfo])
+class LlamaNemotronVLChatModel(nn.Module, SupportsMultiModal, SupportsPP,
+                               SupportsLoRA):
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<image>"
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+        self._patch_quant_config(config, quant_config)
+
+        image_size = config.force_image_size or config.vision_config.image_size
+        patch_size = config.vision_config.patch_size
+        self.patch_size = patch_size
+        self.num_image_token = int(
+            (image_size // patch_size)**2 * (config.downsample_ratio**2))
+        self.downsample_ratio = config.downsample_ratio
+        self.ps_version = config.ps_version
+
+        self.llm_arch_name = config.text_config.architectures[0]
+        self.vision_model = self._init_vision_model(
+            config,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "vision_model"),
+        )
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        self.mlp1 = self._init_mlp1(config)
+
+        self.img_context_token_id = None
+
+        self.visual_token_mask = None
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _patch_quant_config(self, config: PretrainedConfig,
+                            quant_config: QuantizationConfig):
+        # the awq models from OpenGVLab missing `modules_to_not_convert`
+        # patch the quant_config to add `modules_to_not_convert` back
+        if isinstance(quant_config, AWQConfig):
+            text_config = config.text_config
+            llm_quant_config = getattr(text_config, "quantization_config",
+                                       None)
+            if (not quant_config.modules_to_not_convert) and \
+                (llm_quant_config is not None):
+                quant_config.modules_to_not_convert.append("vision_model")
+
+    def _init_vision_model(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig],
+        *,
+        prefix: str,
+    ):
+        return AutoModel.from_config(config.vision_config,
+                                     trust_remote_code=True)
+
+    def _init_mlp1(self, config: PretrainedConfig) -> nn.Sequential:
+        vit_hidden_size = config.vit_hidden_size
+        vision_projection_hidden_size = config.projector_hidden_size
+        llm_hidden_size = config.text_config.hidden_size
+
+        return nn.Sequential(
+            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio)**2,
+                         bias=True),
+            nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio)**2,
+                      vision_projection_hidden_size,
+                      bias=True),
+            nn.GELU(),
+            nn.Linear(vision_projection_hidden_size, llm_hidden_size),
+        )
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        x = x.view(n, int(h * scale_factor), int(w * scale_factor),
+                   int(c / (scale_factor * scale_factor)))
+        if self.ps_version == 'v1':
+            pass
+        else:
+            x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        # https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1/blob/main/modeling.py#L177
+        vit_embeds = self.vision_model(x=pixel_values).features
+        vit_embeds = vit_embeds.to(dtype=torch.bfloat16)
+
+        h = w = int(vit_embeds.shape[1]**0.5)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.pixel_shuffle(vit_embeds,
+                                        scale_factor=self.downsample_ratio)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1,
+                                        vit_embeds.shape[-1])
+        vit_embeds = self.mlp1(vit_embeds)
+        return vit_embeds
+
+    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
+
+        #use force_image_size to get image_size
+        h = w = self.config.force_image_size
+        expected_dims = (3, h, w)
+
+        def _validate_shape(d: torch.Tensor):
+            actual_dims = tuple(d.shape)
+
+            if actual_dims != expected_dims:
+                expected_expr = str(expected_dims)
+                raise ValueError(
+                    "The expected shape of pixel values per image per batch "
+                    f" per patch is {expected_expr}. "
+                    f"You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
+
+        return data
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[InternVLImageInputs]:
+        pixel_values_flat = kwargs.pop("pixel_values_flat", None)
+        image_num_patches = kwargs.pop("image_num_patches", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values_flat is None and image_embeds is None:
+            return None
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+
+            return InternVLImageEmbeddingInputs(
+                type="image_embeds",
+                data=flatten_bn(image_embeds),
+            )
+
+        image_token_id = kwargs["image_token_id"]
+        assert isinstance(image_token_id, torch.Tensor)
+        self.img_context_token_id = image_token_id.flatten().unique().item()
+
+        if pixel_values_flat is not None:
+            if not isinstance(pixel_values_flat, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values_flat)}")
+
+            if not isinstance(image_num_patches, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image_num_patches. "
+                                 f"Got type: {type(image_num_patches)}")
+
+            pixel_values_flat = flatten_bn(pixel_values_flat, concat=True)
+            image_num_patches = flatten_bn(image_num_patches, concat=True)
+
+            return InternVLImagePixelInputs(
+                type="pixel_values",
+                pixel_values_flat=self._validate_pixel_values(
+                    pixel_values_flat),
+                num_patches=image_num_patches,
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _process_image_input(
+        self,
+        image_input: InternVLImageInputs,
+    ) -> tuple[torch.Tensor, ...]:
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
+        assert self.vision_model is not None
+
+        image_embeds = self.extract_feature(image_input["pixel_values_flat"])
+
+        num_patches = image_input["num_patches"]
+
+        # Only one image in the current batch
+        if len(num_patches) == 1:
+            return (image_embeds.view(-1,
+                                      self.config.text_config.hidden_size), )
+
+        # NOTE: Image embeddings are split into separate tensors for each image
+        # by the size of each embedding.
+        feature_size = image_embeds.shape[1]
+        image_embeds = image_embeds.view(-1,
+                                         self.config.text_config.hidden_size)
+        image_feature_sizes = [
+            num_patches * feature_size for num_patches in num_patches
+        ]
+        return image_embeds.split(image_feature_sizes)
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        modalities = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if input_key in ("pixel_values_flat",
+                             "image_embeds") and "images" not in modalities:
+                modalities["images"] = self._parse_and_validate_image_input(
+                    **kwargs)
+
+        return modalities
+
+    def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None:
+        self.visual_token_mask = None
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+
+        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not modalities:
+            return []
+
+        # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor correspoending to a multimodal data item (image).
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in modalities:
+            if modality == "images":
+                image_input = modalities["images"]
+                vision_embeddings = self._process_image_input(image_input)
+                multimodal_embeddings += vision_embeddings
+
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            context_token_ids = [self.img_context_token_id]
+            assert len(context_token_ids) >= 1
+            self._set_visual_token_mask(input_ids)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                context_token_ids,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> IntermediateTensors:
+
+        if intermediate_tensors is not None:
+            input_ids = None
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        forward_kwargs = {
+            "input_ids": input_ids,
+            "positions": positions,
+            "intermediate_tensors": intermediate_tensors,
+            "inputs_embeds": inputs_embeds,
+        }
+
+        # Only required if the model is mono-architecture
+        if self.visual_token_mask is not None:
+            forward_kwargs.update(
+                {"visual_token_mask": self.visual_token_mask})
+            self.visual_token_mask = None
+
+        hidden_states = self.language_model.model(**forward_kwargs)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        ## Ignore registered_buffers
+        ## see https://huggingface.co/nvidia/C-RADIOv2-H/blob/main/input_conditioner.py#L28 # noqa: E501
+        skip_substrs = ["norm_mean", "norm_std"]
+        loader = AutoWeightsLoader(self, skip_substrs=skip_substrs)
+        return loader.load_weights(weights)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="mlp1",
+            tower_model="vision_model")
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nvlm_d.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nvlm_d.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bea1392a6814972f39f2f0d1215a864c70374c8
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/nvlm_d.py
@@ -0,0 +1,202 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://huggingface.co/nvidia/NVLM-D-72B/blob/main/modeling_nvlm_d.py
+# --------------------------------------------------------
+# NVLM-D
+# Copyright (c) 2024 NVIDIA
+# Licensed under Apache 2.0 License [see LICENSE for details]
+# --------------------------------------------------------
+from collections.abc import Mapping, Sequence
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalDataDict, MultiModalKwargs
+from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
+                                   MultiModalDataItems)
+from vllm.multimodal.processing import (PromptReplacement, PromptUpdate,
+                                        PromptUpdateDetails)
+
+from .intern_vit import InternVisionModel
+from .internvl import (BaseInternVLDummyInputsBuilder,
+                       BaseInternVLMultiModalProcessor,
+                       BaseInternVLProcessingInfo, BaseInternVLProcessor,
+                       InternVLChatModel)
+
+IMG_PAD = "<|vision_pad|>"
+
+
+class NVLMProcessor(BaseInternVLProcessor):
+
+    @property
+    def image_token_id(self) -> int:
+        return self.tokenizer.get_vocab()[IMG_PAD]
+
+    def get_image_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int],
+    ) -> PromptUpdateDetails[str]:
+        if num_patches is None:
+            raise NotImplementedError("Embedding inputs are not supported")
+
+        tile_pos_identifiers = [f"<tile_{i}>" for i in range(1, num_patches)]
+        if self.use_thumbnail:
+            tile_pos_identifiers += ["<tile_global_thumbnail>"]
+
+        context_size = feature_size // num_patches
+        features = "".join(identifier + IMG_PAD * context_size
+                           for identifier in tile_pos_identifiers)
+
+        # We include the start and end as well because "<Image><tile" is
+        # tokenized as ["<Image", "><", "tile"], resulting in assertion error
+        # when trying to find "<tile" as a subsequence of "<Image><tile"
+        repl = "<Image>" + features + "</Image>"
+
+        return PromptUpdateDetails.select_text(repl, IMG_PAD)
+
+
+class NVLMProcessingInfo(BaseInternVLProcessingInfo):
+
+    def get_hf_processor(self, **kwargs: object) -> NVLMProcessor:
+        return self.ctx.init_processor(
+            NVLMProcessor,
+            config=self.get_hf_config(),
+            tokenizer=self.get_tokenizer(),
+            **kwargs,
+        )
+
+
+class NVLMDummyInputsBuilder(BaseInternVLDummyInputsBuilder[NVLMProcessingInfo]
+                             ):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        # The newline is necessary to separate ">" of the current item
+        # and "<" of the next item
+        return "<image>\n" * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+        num_images = mm_counts.get("image", 0)
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+
+class NVLMMultiModalProcessor(
+        BaseInternVLMultiModalProcessor[NVLMProcessingInfo]):
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        if "image_num_patches" in out_mm_kwargs:
+            image_num_patches = out_mm_kwargs["image_num_patches"]
+            assert isinstance(image_num_patches, torch.Tensor)
+            image_num_patches = image_num_patches.tolist()
+        elif "image_embeds" in out_mm_kwargs:
+            # TODO: Use image size information in dictionary embedding inputs
+            # to compute num_patches (similar to Qwen2-VL)
+            image_num_patches = [None] * len(out_mm_kwargs["image_embeds"])
+        else:
+            image_num_patches = []
+
+        def get_replacement_nvlm(item_idx: int):
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems))
+
+            if isinstance(images, ImageEmbeddingItems):
+                feature_size = images.get_feature_size(item_idx)
+            else:
+                image_size = images.get_image_size(item_idx)
+                feature_size = self.info.get_num_image_tokens(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                    processor=hf_processor,
+                )
+
+            num_patches = image_num_patches[item_idx]
+            if num_patches is not None:
+                assert isinstance(num_patches, int)
+
+            repl = hf_processor.get_image_repl(feature_size, num_patches)
+
+            return PromptUpdateDetails.select_text(repl.full + "\n", IMG_PAD)
+
+        # See note in dummy data regarding why we have the extra newline
+        return [
+            PromptReplacement(
+                modality="image",
+                target="<image>\n",
+                replacement=get_replacement_nvlm,
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(NVLMMultiModalProcessor,
+                                        info=NVLMProcessingInfo,
+                                        dummy_inputs=NVLMDummyInputsBuilder)
+class NVLM_D_Model(InternVLChatModel):
+
+    def _init_mlp1(self, config: PretrainedConfig) -> nn.Sequential:
+        vit_hidden_size = config.vision_config.hidden_size
+        llm_intermediate_size = config.text_config.intermediate_size
+        llm_hidden_size = config.text_config.hidden_size
+
+        return nn.Sequential(
+            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio)**2),
+            nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio)**2,
+                      llm_intermediate_size,
+                      bias=False),
+            nn.GELU(),
+            nn.Linear(llm_intermediate_size, llm_hidden_size, bias=False),
+        )
+
+    def _init_vision_model(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig],
+        *,
+        is_mono: bool,
+        prefix: str,
+    ):
+        if not is_mono:
+            vision_feature_layer = config.select_layer
+            if vision_feature_layer < 0:
+                num_hidden_layers = config.vision_config.num_hidden_layers \
+                    + vision_feature_layer + 1
+            else:
+                num_hidden_layers = vision_feature_layer + 1
+
+            # We added additional dummy heads to the original num of heads to
+            # make the number of heads divisible by 8.
+            return InternVisionModel(
+                config.vision_config,
+                quant_config=quant_config,
+                num_hidden_layers_override=num_hidden_layers,
+                num_dummy_heads=7,
+                prefix=prefix,
+            )
+        else:
+            msg = "Monolith mode is not applicable to NVLM_D"
+            raise NotImplementedError(msg)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/olmo.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/olmo.py
new file mode 100644
index 0000000000000000000000000000000000000000..1dc4df85c1bc4016225263ac0da531bc6f9e6325
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/olmo.py
@@ -0,0 +1,389 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/models/olmo/modeling_olmo.py
+# Copyright 2024 The vLLM team.
+# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only OLMo model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import OlmoConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class OlmoAttention(nn.Module):
+    """
+    This is the attention block where the output is computed as
+    ``Attention(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
+    (plus another skip connection).
+    """
+
+    def __init__(
+        self,
+        config: OlmoConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        self.total_num_heads = config.num_attention_heads
+
+        assert self.hidden_size % self.total_num_heads == 0
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
+        self.head_dim = self.hidden_size // self.total_num_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.clip_qkv = config.clip_qkv
+
+        # Attention input projection. Projects x -> (q, k, v)
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+        )
+
+        # Rotary embeddings.
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+        self.scaling = self.head_dim**-0.5
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              scale=self.scaling,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+        # Attention output projection.
+        self.o_proj = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        if self.clip_qkv is not None:
+            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class OlmoMLP(nn.Module):
+    """
+    This is the MLP block where the output is computed as
+    ``MLP(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))``
+    (plus another skip connection).
+    """
+
+    def __init__(
+        self,
+        config: OlmoConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+
+        # Feed-forward input projection.
+        self.gate_up_proj = MergedColumnParallelLinear(
+            self.hidden_size,
+            [self.intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+        # Activation function.
+        self.act_fn = SiluAndMul()
+
+        # Feed-forward output projection.
+        self.down_proj = RowParallelLinear(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class OlmoDecoderLayer(nn.Module):
+    """
+    This is a typical transformer block where the output is
+    computed as ``MLP(LN(x + Attention(LN(x))))``
+    (plus another skip connection).
+    """
+
+    def __init__(self,
+                 config: OlmoConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        # Attention block.
+        self.self_attn = OlmoAttention(config,
+                                       cache_config,
+                                       quant_config,
+                                       prefix=f"{prefix}.self_attn")
+
+        # MLP block.
+        self.mlp = OlmoMLP(config, quant_config)
+
+        # LayerNorm
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            elementwise_affine=False,
+                                            bias=False)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
+                                                     elementwise_affine=False,
+                                                     bias=False)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> tuple[torch.Tensor, Optional[tuple[torch.Tensor, torch.Tensor]]]:
+        # Attention block.
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(positions, hidden_states)
+        hidden_states = hidden_states + residual
+
+        # MLP block.
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@support_torch_compile
+class OlmoModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+
+        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
+                                                   config.hidden_size)
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: OlmoDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers")
+        self.norm = nn.LayerNorm(config.hidden_size,
+                                 elementwise_affine=False,
+                                 bias=False)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        """
+        :param input_ids: A tensor of shape `(batch_size, seq_len)`.
+        """
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+
+        # Apply blocks one-by-one.
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            # shape: (batch_size, seq_len, d_model)
+            hidden_states = layer(positions, hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+        # Apply final layer norm.
+        # shape: (batch_size, seq_len or 1, d_model)
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class OlmoForCausalLM(nn.Module, SupportsPP):
+    """
+    Extremely barebones HF model wrapper.
+    """
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.model = OlmoModel(vllm_config=vllm_config,
+                               prefix=maybe_prefix(prefix, "model"))
+        if config.tie_word_embeddings:
+            self.lm_head = self.model.embed_tokens
+        else:
+            self.unpadded_vocab_size = config.vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                quant_config=quant_config,
+            )
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head.weight"]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/opt.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..9eaac1e28dcd87eb09a94149c938c690be754132
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/opt.py
@@ -0,0 +1,412 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/opt/modeling_opt.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights
+# reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only OPT model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import OPTConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class OPTLearnedPositionalEmbedding(nn.Embedding):
+
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        # OPT is set up so that if padding_idx is specified then offset the
+        # embedding ids by 2 and adjust num_embeddings appropriately. Other
+        # models don't have this hack
+        self.offset = 2
+        super().__init__(num_embeddings + self.offset, embedding_dim)
+
+    def forward(self, positions: torch.Tensor):
+        return super().forward(positions + self.offset)
+
+
+class OPTAttention(nn.Module):
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        bias: bool = True,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        total_num_heads = num_heads
+        assert num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = total_num_heads // tensor_model_parallel_world_size
+        self.head_dim = embed_dim // total_num_heads
+        self.scaling = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            embed_dim,
+            self.head_dim,
+            total_num_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.out_proj = RowParallelLinear(
+            embed_dim,
+            embed_dim,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              scale=self.scaling,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.out_proj(attn_output)
+        return output
+
+
+class OPTDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: OPTConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.self_attn = OPTAttention(
+            embed_dim=self.embed_dim,
+            num_heads=config.num_attention_heads,
+            bias=config.enable_bias,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.do_layer_norm_before = config.do_layer_norm_before
+
+        self.self_attn_layer_norm = nn.LayerNorm(
+            self.embed_dim,
+            elementwise_affine=config.layer_norm_elementwise_affine)
+        self.fc1 = ColumnParallelLinear(
+            self.embed_dim,
+            config.ffn_dim,
+            bias=config.enable_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+        )
+        self.activation_fn = get_act_fn(config.activation_function)
+        self.fc2 = RowParallelLinear(
+            config.ffn_dim,
+            self.embed_dim,
+            bias=config.enable_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+        )
+        self.final_layer_norm = nn.LayerNorm(
+            self.embed_dim,
+            elementwise_affine=config.layer_norm_elementwise_affine)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+        if self.do_layer_norm_before:
+            hidden_states = self.self_attn_layer_norm(hidden_states)
+        hidden_states = self.self_attn(hidden_states=hidden_states)
+        hidden_states = residual + hidden_states
+        # 350m applies layer norm AFTER attention
+        if not self.do_layer_norm_before:
+            hidden_states = self.self_attn_layer_norm(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+        if self.do_layer_norm_before:
+            hidden_states = self.final_layer_norm(hidden_states)
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        hidden_states = residual + hidden_states
+        # 350m applies layer norm AFTER attention
+        if not self.do_layer_norm_before:
+            hidden_states = self.final_layer_norm(hidden_states)
+        return hidden_states
+
+
+class OPTDecoder(nn.Module):
+
+    def __init__(
+        self,
+        config: OPTConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.max_target_positions = config.max_position_embeddings
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.word_embed_proj_dim,
+        )
+        # Positional embeddings are replicated (not sharded).
+        self.embed_positions = OPTLearnedPositionalEmbedding(
+            config.max_position_embeddings, config.hidden_size)
+
+        # Project out & in will be replicated if they exist.
+        if config.word_embed_proj_dim != config.hidden_size:
+            self.project_out = ReplicatedLinear(config.hidden_size,
+                                                config.word_embed_proj_dim,
+                                                bias=False,
+                                                quant_config=quant_config,
+                                                prefix=f"{prefix}.project_out")
+        else:
+            self.project_out = None
+
+        if config.word_embed_proj_dim != config.hidden_size:
+            self.project_in = ReplicatedLinear(config.word_embed_proj_dim,
+                                               config.hidden_size,
+                                               bias=False,
+                                               quant_config=quant_config,
+                                               prefix=f"{prefix}.project_in")
+        else:
+            self.project_in = None
+
+        # Note that the only purpose of `config._remove_final_layer_norm` is to
+        # keep backward compatibility with checkpoints that have been fine-tuned
+        # before transformers v4.20.1
+        # see https://github.com/facebookresearch/metaseq/pull/164
+        if config.do_layer_norm_before and not config._remove_final_layer_norm:
+            self.final_layer_norm = nn.LayerNorm(
+                config.hidden_size,
+                elementwise_affine=config.layer_norm_elementwise_affine)
+        else:
+            self.final_layer_norm = None
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: OPTDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers")
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is None:
+                inputs_embeds = self.get_input_embeddings(input_ids)
+            pos_embeds = self.embed_positions(positions)
+            if self.project_in is not None:
+                inputs_embeds, _ = self.project_in(inputs_embeds)
+            hidden_states = inputs_embeds + pos_embeds
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states = layer(hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+        if self.final_layer_norm is not None:
+            hidden_states = self.final_layer_norm(hidden_states)
+        if self.project_out is not None:
+            hidden_states, _ = self.project_out(hidden_states)
+        return hidden_states
+
+
+@support_torch_compile
+class OPTModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.decoder = OPTDecoder(config,
+                                  cache_config,
+                                  quant_config,
+                                  prefix=f"{prefix}.decoder")
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.decoder.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        return self.decoder(input_ids,
+                            positions,
+                            intermediate_tensors,
+                            inputs_embeds=inputs_embeds)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class OPTForCausalLM(nn.Module, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"]
+    }
+
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
+        "decoder.": "model.decoder.",
+    })
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = OPTModel(vllm_config=vllm_config,
+                              prefix=maybe_prefix(prefix, "model"))
+        if self.config.tie_word_embeddings:
+            self.lm_head = self.model.decoder.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(config.vocab_size,
+                                          config.word_embed_proj_dim)
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head.weight"]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/orion.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/orion.py
new file mode 100644
index 0000000000000000000000000000000000000000..d121188ba5d4a91ca435274f6a0b74b590e84b15
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/orion.py
@@ -0,0 +1,349 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://huggingface.co/OrionStarAI/Orion-14B-Base/blob/main/modeling_orion.py
+# Copyright (c) OrionStar Inc.
+# LICENSE: https://huggingface.co/OrionStarAI/Orion-14B-Base/blob/main/LICENSE
+"""Inference-only Orion-14B model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class OrionMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class OrionAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class OrionDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = OrionAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.mlp = OrionMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+        )
+
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.rms_norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
+                                                     eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@support_torch_compile
+class OrionModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: OrionDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers")
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory([
+                "hidden_states",
+            ], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states = layer(positions, hidden_states)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+            })
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class OrionForCausalLM(nn.Module, SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = OrionModel(vllm_config=vllm_config,
+                                prefix=maybe_prefix(prefix, "model"))
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config)
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/persimmon.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/persimmon.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8db99eb92ba87b64147b5c985e7a74200f435be
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/persimmon.py
@@ -0,0 +1,344 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://github.com/huggingface/transformers/blob/v4.39.3/src/transformers/models/persimmon/modeling_persimmon.py
+# Copyright 2023 The vLLM team.
+# Copyright 2023 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only persimmon model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import PersimmonConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class PersimmonMLP(nn.Module):
+
+    def __init__(self,
+                 config: PersimmonConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+        self.dense_h_to_4h = ColumnParallelLinear(config.hidden_size,
+                                                  config.intermediate_size,
+                                                  quant_config=quant_config)
+        self.dense_4h_to_h = RowParallelLinear(config.intermediate_size,
+                                               config.hidden_size,
+                                               quant_config=quant_config)
+        self.act = get_act_fn(config.hidden_act)
+
+    def forward(self, hidden_states) -> torch.Tensor:
+        hidden_states, _ = self.dense_h_to_4h(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.dense_4h_to_h(hidden_states)
+        return hidden_states
+
+
+class PersimmonAttention(nn.Module):
+
+    def __init__(self,
+                 config: PersimmonConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.config = config
+        tensor_parallel_world_size = get_tensor_model_parallel_world_size()
+
+        self.hidden_size = config.hidden_size
+        self.total_num_heads = config.num_attention_heads
+        self.num_heads = self.total_num_heads // tensor_parallel_world_size
+        self.head_dim = self.hidden_size // self.total_num_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.partial_rotary_factor = config.partial_rotary_factor
+        self.is_causal = True
+
+        assert (self.head_dim * self.total_num_heads) == self.hidden_size
+        assert self.total_num_heads % tensor_parallel_world_size == 0
+
+        self.query_key_value = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.dense = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.is_qk_layernorm = config.qk_layernorm
+
+        if self.is_qk_layernorm:
+            self.q_layernorm = nn.LayerNorm(self.head_dim)
+            self.k_layernorm = nn.LayerNorm(self.head_dim)
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=self.rope_theta,
+            partial_rotary_factor=self.partial_rotary_factor,
+        )
+        self.scaling = self.head_dim**-0.5
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              scale=self.scaling,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def _split_heads(self, x: torch.Tensor) -> torch.Tensor:
+        # [seq_length, hidden_size] -> [seq_length, num_heads, head_dim]
+        seq_length = x.shape[0]
+        return x.view(seq_length, self.num_heads, self.head_dim)
+
+    def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
+        # [seq_length, num_heads, head_dim] -> [seq_length, hidden_size]
+        seq_length = x.shape[0]
+        return x.view(seq_length, self.num_heads * self.head_dim)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        # [seq_length, 3 x hidden_size]
+        qkv, _ = self.query_key_value(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+
+        if self.is_qk_layernorm:
+            # [seq_length, num_heads, head_dim]
+            q = self._split_heads(q)
+            k = self._split_heads(k)
+
+            q = self.q_layernorm(q)
+            k = self.k_layernorm(k)
+
+            q = self._merge_heads(q)
+            k = self._merge_heads(k)
+
+        q, k = self.rotary_emb(position_ids, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.dense(attn_output)
+        return output
+
+
+class PersimmonDecoderLayer(nn.Module):
+
+    def __init__(self,
+                 config: PersimmonConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = PersimmonAttention(config=config,
+                                            cache_config=cache_config,
+                                            quant_config=quant_config,
+                                            prefix=f"{prefix}.self_attn")
+        self.mlp = PersimmonMLP(config, quant_config=quant_config)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
+                                                     eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states = self.self_attn(
+            position_ids=position_ids,
+            hidden_states=hidden_states,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        hidden_states = hidden_states + residual
+
+        outputs = hidden_states
+        return outputs
+
+
+@support_torch_compile
+class PersimmonModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.vocab_size = config.vocab_size
+        self.config = config
+        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
+                                                   config.hidden_size)
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: PersimmonDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers")
+        self.final_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states = layer(positions, hidden_states)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+        hidden_states = self.final_layernorm(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if is_pp_missing_parameter(name, self):
+                continue
+            param = params_dict[name]
+
+            if "query_key_value" in name:
+                # copy from vllm/model_executor/models/bloom.py
+                # NOTE: Persimmon's fused QKV's output_dim has the shape of
+                # (num_heads * 3 * head_size), while the
+                # required shape is (3 * num_heads * head_size).
+                # Thus, we need weight conversion.
+                output_dim = getattr(param, "output_dim", None)
+                num_heads = self.config.num_attention_heads
+                if output_dim is not None:
+                    loaded_weight_shape = loaded_weight.shape
+                    loaded_weight = loaded_weight.view(
+                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
+                        loaded_weight_shape[output_dim + 1:])
+                    loaded_weight = loaded_weight.transpose(
+                        output_dim, output_dim + 1)
+                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)
+
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class PersimmonForCausalLM(nn.Module, SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.config = config
+        self.vocab_size = config.vocab_size
+        self.model = PersimmonModel(vllm_config=vllm_config,
+                                    prefix=maybe_prefix(prefix, "model"))
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      bias=False)
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ):
+        hidden_states = self.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi.py
new file mode 100644
index 0000000000000000000000000000000000000000..21d517b3a490fc958f05c10bd88b5cacd2c6ce2d
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi.py
@@ -0,0 +1,356 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_phi.py
+# Copyright 2023 The vLLM team.
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+#
+# BSD 3-Clause License
+#
+# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""Inference-only Phi-1.5 model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import PhiConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class PhiAttention(nn.Module):
+
+    def __init__(self,
+                 config: PhiConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.total_num_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_size = self.hidden_size // self.total_num_heads
+
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
+
+        # pylint: disable=C0103
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_size,
+            self.total_num_heads,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.dense = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            quant_config=quant_config,
+        )
+
+        scaling = self.head_size**-0.5
+        rotary_dim = int(config.partial_rotary_factor *
+                         (config.hidden_size // config.num_attention_heads))
+        assert rotary_dim % 2 == 0
+
+        # pylint: disable=C0301
+        # Refer to:
+        # https://huggingface.co/microsoft/phi-1_5/blob/d212a789620c380ff32ca1d1ee9943a777360987/modeling_phi.py#L518
+        rope_theta = getattr(config, "rope_theta", 10000.0)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          2048)
+        self.rotary_emb = get_rope(
+            self.head_size,
+            rotary_dim=rotary_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_size,
+                              scaling,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k = self.rotary_emb(position_ids, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.dense(attn_output)
+        return output
+
+
+class PhiMLP(nn.Module):
+
+    def __init__(self,
+                 config: PhiConfig,
+                 quant_config: Optional[QuantizationConfig] = None):
+        super().__init__()
+
+        n_inner = getattr(config, "n_inner", None)
+        n_inner = n_inner if n_inner is not None else 4 * config.hidden_size
+
+        self.fc1 = ColumnParallelLinear(
+            config.hidden_size,
+            n_inner,
+            quant_config=quant_config,
+        )
+        self.fc2 = RowParallelLinear(
+            n_inner,
+            config.hidden_size,
+            quant_config=quant_config,
+        )
+        self.act = get_act_fn(config.hidden_act)
+
+    def forward(self, hidden_states):
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        return hidden_states
+
+
+class PhiLayer(nn.Module):
+
+    def __init__(self,
+                 config: PhiConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
+        self.self_attn = PhiAttention(config,
+                                      cache_config,
+                                      quant_config,
+                                      prefix=f"{prefix}.self_attn")
+        self.mlp = PhiMLP(config, quant_config)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        attn_outputs = self.self_attn(
+            position_ids=position_ids,
+            hidden_states=hidden_states,
+        )
+        feed_forward_hidden_states = self.mlp(hidden_states)
+        hidden_states = attn_outputs + feed_forward_hidden_states + residual
+        return hidden_states
+
+
+@support_torch_compile
+class PhiModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.quant_config = quant_config
+        self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
+                                                   config.hidden_size)
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: PhiLayer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers")
+        self.final_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.layer_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states = layer(positions, hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+
+        hidden_states = self.final_layernorm(hidden_states)
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v")
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # pylint: disable=E1136
+
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class PhiForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ]
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        # lm_head use bias, cannot share word embeddings
+        assert not config.tie_word_embeddings
+        self.lora_config = lora_config
+
+        self.quant_config = quant_config
+
+        self.model = PhiModel(vllm_config=vllm_config,
+                              prefix=maybe_prefix(prefix, "model"))
+
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      bias=True,
+                                      quant_config=quant_config)
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata, self.lm_head.bias)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi3.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi3.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4e870c530309eb96012b78ed84e706abe4dfc1a
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi3.py
@@ -0,0 +1,19 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from llama.py
+"""Inference-only Phi3 model code inherit from Llama.py"""
+
+from vllm.model_executor.models.llama import LlamaForCausalLM
+
+
+class Phi3ForCausalLM(LlamaForCausalLM):
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "qkv_proj",
+        ],
+        "gate_up_proj": [
+            "gate_up_proj",
+        ],
+    }
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi3v.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi3v.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ef4f8371eb3d9bc5969c1efc679d6b5cf4ee72c
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi3v.py
@@ -0,0 +1,686 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2024 The vLLM team.
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Any, Literal, Optional, Union
+
+import regex as re
+import torch
+import torch.nn as nn
+from transformers import (BatchFeature, CLIPVisionConfig, PretrainedConfig,
+                          ProcessorMixin)
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
+                                   ImageSize, MultiModalDataItems)
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, BoundPromptUpdate,
+                                        PlaceholderFeaturesInfo,
+                                        PromptReplacement, PromptUpdate)
+# yapf: enable
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.utils import is_list_of
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .clip import CLIPVisionModel
+from .interfaces import (MultiModalEmbeddings, SupportsMultiModal, SupportsPP,
+                         SupportsQuant)
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+logger = init_logger(__name__)
+
+# Cannot find the following 2 numbers from hf config.
+_IMAGE_TOKEN_ID = 32044
+
+CLIP_VIT_LARGE_PATCH14_336_CONFIG = CLIPVisionConfig(dropout=0.0,
+                                                     hidden_act="quick_gelu",
+                                                     hidden_size=1024,
+                                                     image_size=336,
+                                                     intermediate_size=4096,
+                                                     num_attention_heads=16,
+                                                     num_channels=3,
+                                                     num_hidden_layers=24,
+                                                     patch_size=14,
+                                                     projection_dim=768)
+
+
+def _init_img_processor(hf_config: PretrainedConfig,
+                        quant_config: Optional[QuantizationConfig],
+                        prefix: str = "") -> CLIPVisionModel:
+    clip_config = CLIP_VIT_LARGE_PATCH14_336_CONFIG
+    layer_idx = hf_config.img_processor.get('layer_idx', -2)
+
+    # Initialize the CLIP only up to the required feature layer
+    if layer_idx < 0:
+        num_hidden_layers = clip_config.num_hidden_layers + \
+            layer_idx + 1
+    else:
+        num_hidden_layers = layer_idx + 1
+
+    img_processor = CLIPVisionModel(
+        clip_config,
+        quant_config,
+        num_hidden_layers_override=num_hidden_layers,
+        prefix=prefix,
+    )
+
+    return img_processor
+
+
+class Phi3VImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - b: Batch size
+        - n: Number of images
+        - p: Number of patches
+        - h: Height of each patch
+        - w: Width of each patch
+    """
+
+    type: Literal["pixel_values", "image_embeds"] = "pixel_values"
+
+    # Supports either a stacked tensor or a list of (p, 3, h, w) tensors
+    data: Annotated[
+        Union[torch.Tensor, list[torch.Tensor]],
+        TensorShape("bn", "p", 3, "h", "w", dynamic_dims={"p"}
+                    ),  # 'p' may vary across items
+    ]
+
+    # Stacked tensor with height and width for each image
+    image_sizes: Annotated[Optional[torch.Tensor], TensorShape("bn", 2)]
+
+
+class Phi3VImageEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - b: Batch size
+        - n: Number of images
+        - f: Image feature size (e.g., number of tokens per image)
+        - h: Hidden size (must match language model backbone)
+    """
+    type: Literal["image_embeds"] = "image_embeds"
+    data: Annotated[
+        Union[torch.Tensor, list[torch.Tensor]],
+        TensorShape("bn", "f", "h"),
+    ]
+
+
+Phi3VImageInputs = Union[Phi3VImagePixelInputs, Phi3VImageEmbeddingInputs]
+
+
+class Phi3ImageEmbeddingBase(nn.Module):
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.layer_idx: int
+        self.type_feature: str
+        self.img_processor: CLIPVisionModel
+
+    def get_img_features(self,
+                         img_embeds: torch.FloatTensor) -> torch.FloatTensor:
+        TYPE_FEATURE = self.type_feature
+
+        # NOTE: we skip the step to select the vision feature layer since
+        # this is already done inside the img_processor
+        img_feature = self.img_processor(img_embeds)
+
+        if TYPE_FEATURE == "patch":
+            patch_feature = img_feature[:, 1:]
+            return patch_feature
+
+        if TYPE_FEATURE == "cls_patch":
+            return img_feature
+
+        raise NotImplementedError
+
+
+# adapted from https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/image_embedding_phi3_v.py
+class Phi3HDImageEmbedding(Phi3ImageEmbeddingBase):
+    """Phi3 Image embedding with HD transform."""
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 quant_config: Optional[QuantizationConfig],
+                 prefix: str = "") -> None:
+        super().__init__()
+
+        # n_embed or hidden_size
+        hidden_size = config.n_embd if hasattr(
+            config, 'n_embd') else config.hidden_size
+
+        self.img_processor = _init_img_processor(
+            config, quant_config, prefix=f"{prefix}.img_processor")
+
+        image_dim_out = config.img_processor['image_dim_out']
+        self.num_img_tokens = config.img_processor['num_img_tokens']
+
+        self.image_dim_out = image_dim_out
+
+        # global_gn and sub_gn for hd transform, serves as line separator
+        self.use_hd_transform = config.embd_layer.get('use_hd_transform',
+                                                      False)
+        self.with_learnable_separator = config.embd_layer.get(
+            'with_learnable_separator', False)
+        self.hd_transform_order = config.embd_layer.get(
+            'hd_transform_order', 'glb_sub')
+        # with_hd_transform and with_learnable_separator should have same value
+        assert self.use_hd_transform and self.with_learnable_separator
+
+        # 1024 * 4, merge spatial to channel dimension
+        self.glb_GN = nn.Parameter(torch.empty([1, 1, self.image_dim_out * 4]))
+        self.sub_GN = nn.Parameter(
+            torch.empty([1, 1, 1, self.image_dim_out * 4]))
+
+        dim_projection = hidden_size
+        depth = 2
+        layers = [nn.Linear(image_dim_out * 4, dim_projection)]
+        for _ in range(1, depth):
+            layers.extend(
+                [nn.GELU(),
+                 nn.Linear(dim_projection, dim_projection)])
+        self.img_projection = nn.Sequential(*layers)
+
+        self.type_feature = config.img_processor.get('type_feature', 'patch')
+
+    def forward(self, pixel_values: torch.FloatTensor,
+                image_sizes: torch.Tensor) -> torch.FloatTensor:
+        """
+        process image and return vision embeddings.
+
+        pixel_values: (num_images, num_crops, c, h, w)
+        output: (num_images, num_img_tokens, hidden_size)
+        """
+        num_images, num_crops, c, h, w = pixel_values.shape
+        pixel_values = pixel_values.flatten(0, 1)
+        img_features = self.get_img_features(pixel_values)
+        img_features = img_features.reshape(num_images, num_crops, -1,
+                                            self.image_dim_out)
+        image_features_proj = self.hd_feature_transform(
+            img_features, image_sizes)
+        return image_features_proj
+
+    def hd_feature_transform(self, image_features, image_sizes):
+        """
+        image_features: (num_images, num_crops+1, 24*24, 1024)
+        """
+        assert (
+            self.hd_transform_order == 'sub_glb'
+        ), f'hd_transform_order `{self.hd_transform_order}` not implemented'
+        if isinstance(self.img_projection, nn.Sequential):
+            target_device = self.img_projection[0].bias.device
+            target_dtype = self.img_projection[0].bias.dtype
+        else:  # It's a single nn.Linear layer
+            target_device = self.img_projection.bias.device
+            target_dtype = self.img_projection.bias.dtype
+
+        global_image_features = image_features[:,
+                                               0]  # (num_images, 24*24, 1024)
+        # global feature can be viewed as a special HD case with num_crops 1x1
+        global_image_features_hd = self.reshape_hd_patches_2x2merge(
+            global_image_features, 1, 1)
+        global_image_features_hd_newline = self.add_image_newline(
+            global_image_features_hd)
+
+        batch_image_features_proj = []
+        # need a for loop to process each image because of different image sizes
+        # (patch arrangement is different for each image)
+        for i, img_size in enumerate(image_sizes):
+            h, w = img_size
+            h_crop = h // 336
+            w_crop = w // 336
+            num_crops = h_crop * w_crop
+
+            # NOTE: real num_crops is padded
+            # (num_crops, 24*24, 1024)
+            sub_image_features = image_features[i, 1:1 + num_crops]
+            sub_image_features_hd = self.reshape_hd_patches_2x2merge(
+                sub_image_features, h_crop, w_crop)
+            sub_image_features_hd_newline = self.add_image_newline(
+                sub_image_features_hd)
+
+            # [sub features, separator, global features]
+            image_embeddings = torch.cat([
+                sub_image_features_hd_newline.squeeze(
+                    0),  # (h_crop*12*(w_crop*12+1), 4096)
+                self.glb_GN.squeeze(0),
+                global_image_features_hd_newline[i],
+            ])
+            img_proj = self.img_projection(
+                image_embeddings.to(target_device, target_dtype))
+            batch_image_features_proj.append(img_proj)
+
+        return batch_image_features_proj
+
+    def reshape_hd_patches_2x2merge(self, image_features, h_crop, w_crop):
+        """
+        image_features: (num_images*num_crops, 24*24, 1024)
+        output: (num_images, h_crop*12, w_crop*12, 4096)
+        where h_crop*w_crop == num_crops
+        """
+        N, L, C = image_features.shape
+        assert L == 576 and C == 1024 and N % (h_crop * w_crop) == 0
+        num_images = N // (h_crop * w_crop)
+        H = int(L**0.5)
+        image_features_hd = (
+            image_features.reshape(N, H, H, C)  # N, 24, 24, 1024
+            .reshape(N, H // 2, 2, H // 2, 2, C)  # N, 12, 2, 12, 2, 1024
+            .permute(0, 1, 3, 2, 4, 5)  # N, 12, 12, 2, 2, 1024
+            .reshape(N, -1, 4 * C)  # N, 144, 4096
+            .reshape(num_images, h_crop, w_crop, H // 2, H // 2,
+                     -1)  # n_img, h_crop, w_crop, 12, 12, 4096
+            .permute(0, 1, 3, 2, 4, 5)  # n_img, h_crop, 12, w_crop, 12, 4096
+            .reshape(num_images, h_crop * H // 2, w_crop * H // 2,
+                     4 * C)  # n_img, h_crop*12, w_crop*12, 4096
+        )
+        return image_features_hd
+
+    def add_image_newline(self, image_features_hd):
+        """
+        image_features_hd: (num_images, h_crop*12, w_crop*12, 4096)
+        output: (num_images, (h_crop*12) * (w_crop*12+1), 4096)
+        """
+        num_images, h, w, hid_dim = image_features_hd.shape
+        # add the newline token to the HD image feature patches
+        newline_embeddings = self.sub_GN.expand(num_images, h, -1,
+                                                -1)  # (n_img, h, 1, hid_dim)
+        image_features_hd_newline = torch.cat(
+            [image_features_hd, newline_embeddings],
+            dim=2).reshape(num_images, -1, hid_dim)
+        return image_features_hd_newline
+
+
+class Phi3VProcessingInfo(BaseProcessingInfo):
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[ProcessorMixin] = None,
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        return processor.calc_num_image_tokens_from_image_size(  # type: ignore
+            width=image_width,
+            height=image_height,
+        )
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        # Result in the max possible feature size (h:w = 16:1)
+        return ImageSize(height=8000, width=50)
+
+
+class Phi3VDummyInputsBuilder(BaseDummyInputsBuilder[Phi3VProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        hf_processor = self.info.get_hf_processor()
+        image_tokens: list[str] = hf_processor.img_tokens  # type: ignore
+
+        return "".join(image_tokens[:num_images])
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+
+class Phi3VMultiModalProcessor(BaseMultiModalProcessor[Phi3VProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+
+        input_ids = processed_outputs["input_ids"]
+        assert isinstance(input_ids, torch.Tensor)
+
+        # Phi3v processor has inserted -1, -2 etc as placeholder in prompt_ids,
+        # which will cause OverflowError when decoding the prompt_ids.
+        # Therefore, we need to do an early replacement here
+        input_ids.masked_fill_(input_ids < 0, _IMAGE_TOKEN_ID)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            pixel_values=MultiModalFieldConfig.batched("image"),
+            image_sizes=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_tokens: list[str] = hf_processor.img_tokens  # type: ignore
+
+        def get_replacement_phi3v(item_idx: int):
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems))
+
+            if isinstance(images, ImageEmbeddingItems):
+                num_image_tokens = images.get_feature_size(item_idx)
+            else:
+                image_size = images.get_image_size(item_idx)
+                num_image_tokens = self.info.get_num_image_tokens(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                    processor=hf_processor,
+                )
+
+            return [_IMAGE_TOKEN_ID] * num_image_tokens
+
+        num_images = mm_items.get_count("image", strict=False)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=image_token,
+                replacement=get_replacement_phi3v,
+            ) for image_token in image_tokens[:num_images]
+        ]
+
+    def _apply_prompt_updates(
+        self,
+        token_ids: list[int],
+        mm_prompt_updates: Mapping[str, Sequence[BoundPromptUpdate]],
+        mm_item_counts: Mapping[str, int],
+    ) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
+        # align to hf behavior when there are images
+        if len(mm_item_counts):
+            tokenizer = self.info.get_tokenizer()
+            # to decode token_ids to the original text, we need to
+            # 1. remove the first bos token
+            # 2. remove space after each special token
+            #    introduced by the tokenizer
+            if len(token_ids) and token_ids[0] == tokenizer.bos_token_id:
+                token_ids = token_ids[1:]
+            text = tokenizer.decode(token_ids)
+            for special_tokens in tokenizer.special_tokens_map.values():
+                if isinstance(special_tokens, str):
+                    text = text.replace(f"{special_tokens} ", special_tokens)
+                elif isinstance(special_tokens, list):
+                    for special_token in special_tokens:
+                        text = text.replace(f"{special_token} ", special_token)
+            # perform hf behavior
+            # https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/64f88b6/processing_phi3_v.py#L407
+            pattern = r"<\|image_\d+\|>"
+            prompt_chunks = [
+                tokenizer(chunk).input_ids
+                for chunk in re.split(pattern, text)
+            ]
+            image_tags = [
+                tokenizer(chunk, add_special_tokens=False).input_ids
+                for chunk in re.findall(pattern, text)
+            ]
+            if len(prompt_chunks) > len(image_tags):
+                image_tags.append([])
+            token_ids = [
+                e for sublist in zip(prompt_chunks, image_tags)
+                for ele in sublist for e in ele
+            ]
+
+        token_ids, text, placeholders = super()._apply_prompt_updates(
+            token_ids=token_ids,
+            mm_prompt_updates=mm_prompt_updates,
+            mm_item_counts=mm_item_counts,
+        )
+
+        # Keep the behavior in line with HF processor
+        if text.startswith("<s> <|image|>"):
+            text = text.replace("<s> <|image|>", "<s><|image|>", 1)
+            token_ids = [token_ids[0], *token_ids[2:]]
+            placeholders = {
+                modality: [
+                    PlaceholderFeaturesInfo(
+                        modality=p.modality,
+                        item_idx=p.item_idx,
+                        start_idx=p.start_idx - 1,
+                        tokens=p.tokens,
+                        is_embed=p.is_embed,
+                    ) for p in ps
+                ]
+                for modality, ps in placeholders.items()
+            }
+
+        return token_ids, text, placeholders
+
+
+@MULTIMODAL_REGISTRY.register_processor(Phi3VMultiModalProcessor,
+                                        info=Phi3VProcessingInfo,
+                                        dummy_inputs=Phi3VDummyInputsBuilder)
+class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP,
+                       SupportsQuant):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "model.vision_embed_tokens.wte": "embed_tokens",
+            "model.vision_embed_tokens.": "vision_embed_tokens.",
+            "lm_head.": "language_model.lm_head.",
+            "model.": "language_model.model.",
+        })
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return f"<|image_{i}|>"
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.multimodal_config = multimodal_config
+        self.image_token_id = _IMAGE_TOKEN_ID
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            quant_config=self.quant_config,
+            prefix=maybe_prefix(prefix, "model.embed_tokens"),
+        )
+
+        # TODO: Optionally initializes this for supporting input embeddings.
+        self.vision_embed_tokens = Phi3HDImageEmbedding(
+            config,
+            self.quant_config,
+            prefix=maybe_prefix(prefix, "model.vision_embed_tokens"))
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            # The prefix is empty intentionally because default prefix of
+            # LlamaForCausalLM is "model"
+            prefix="",
+            # We don't directly initialize vLLM's LlamaForCausalLM so we
+            # can automatically apply embedding wrapper if this model is
+            # initialized as an embedding model
+            architectures=["LlamaForCausalLM"],
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Phi3VImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_sizes = kwargs.pop("image_sizes", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            return Phi3VImagePixelInputs(
+                type="pixel_values",
+                data=flatten_bn(pixel_values),
+                image_sizes=flatten_bn(image_sizes, concat=True),
+                resolve_bindings={
+                    "h": CLIP_VIT_LARGE_PATCH14_336_CONFIG.image_size,
+                    "w": CLIP_VIT_LARGE_PATCH14_336_CONFIG.image_size
+                })
+
+        if image_embeds is not None:
+            return Phi3VImageEmbeddingInputs(
+                type="image_embeds",
+                data=flatten_bn(image_embeds),
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _process_image_input(
+        self,
+        image_input: Phi3VImageInputs,
+    ) -> torch.Tensor:
+
+        if image_input["type"] == "image_embeds":
+            image_data = image_input["data"]
+            if is_list_of(image_data, torch.Tensor):
+                # it's already a list of tensors
+                return image_data
+            if len(image_data.shape) == 3:
+                # 3D tensor
+                return list(torch.unbind(image_data, dim=0))
+            raise ValueError(
+                "We expect batched 2D tensors; "
+                "this can be either a list of 2D tensors or a single 3D tensor."
+            )
+
+        assert self.vision_embed_tokens is not None
+        image_embeds = self.vision_embed_tokens(image_input["data"],
+                                                image_input["image_sizes"])
+
+        return image_embeds
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.embed_tokens(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.image_token_id)
+        return inputs_embeds
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs: object):
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model.model(input_ids,
+                                                  positions,
+                                                  intermediate_tensors,
+                                                  inputs_embeds=inputs_embeds)
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+
+        loader = AutoWeightsLoader(self)
+        autoloaded_weights = loader.load_weights(weights,
+                                                 mapper=self.hf_to_vllm_mapper)
+
+        # The HF config doesn't specify whether these are tied,
+        # so we detect it this way
+        if "embed_tokens.weight" not in autoloaded_weights:
+            self.embed_tokens = self.language_model.model.embed_tokens
+            autoloaded_weights.add("embed_tokens.weight")
+        return autoloaded_weights
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi4_multimodal.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi4_multimodal.py
new file mode 100644
index 0000000000000000000000000000000000000000..e13b8276bf17aae7cc01cfab02923a330c2ef198
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi4_multimodal.py
@@ -0,0 +1,1449 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Any, Literal, Optional, TypedDict, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import (BatchFeature, Phi4MultimodalAudioConfig,
+                          Phi4MultimodalConfig, Phi4MultimodalFeatureExtractor,
+                          Phi4MultimodalImageProcessorFast)
+from transformers import Phi4MultimodalProcessor as Phi4MMProcessor
+from transformers.models.phi4_multimodal.modeling_phi4_multimodal import (
+    Phi4MultimodalAudioConvModule, Phi4MultimodalAudioNemoConvSubsampling,
+    Phi4MultimodalAudioRelativeAttentionBias, adaptive_enc_mask, unfold_tensor)
+
+from vllm.config import VllmConfig
+from vllm.distributed import (divide, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.layers.activation import MulAndSilu, get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs, NestedTensors)
+from vllm.multimodal.parse import (AudioProcessorItems, ImageEmbeddingItems,
+                                   ImageProcessorItems, ImageSize,
+                                   MultiModalDataItems, MultiModalDataParser)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.utils import is_list_of
+
+from .idefics2_vision_model import Idefics2VisionTransformer
+from .interfaces import MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+# <|endoftext10|> (see vocab.json in hf model)
+_IMAGE_PLACEHOLDER_TOKEN_ID = 200010
+# <|endoftext11|>
+_AUDIO_PLACEHOLDER_TOKEN_ID = 200011
+
+_AUDIO_MAX_SOUNDFILE_SIZE = 241_000
+
+
+def _get_padding_size(orig_width: int, orig_height: int, target_height: int,
+                      target_width: int):
+    ratio_width = target_width / orig_width
+    ratio_height = target_height / orig_height
+
+    if ratio_width < ratio_height:
+        padding_width = 0
+        padding_height = target_height - int(orig_height * ratio_width)
+    else:
+        padding_width = target_width - int(orig_width * ratio_height)
+        padding_height = 0
+    return padding_height, padding_width
+
+
+class Phi4MMProjector(nn.Module):
+
+    def __init__(self, input_size: int, hidden_size: int):
+        super().__init__()
+        self.up = ColumnParallelLinear(input_size, hidden_size)
+        self.down = RowParallelLinear(hidden_size, hidden_size)
+        self.act = get_act_fn("gelu")
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.up(x)
+        x = self.act(x)
+        x, _ = self.down(x)
+        return x
+
+
+class Phi4MMImageEmbedding(nn.Module):
+    """Image embedding."""
+
+    def __init__(self, config: Phi4MultimodalConfig):
+        super().__init__()
+        self.config = config
+        self.layer_idx = config.vision_config.feature_layer
+        self.crop_size = config.vision_config.crop_size
+        self.image_dim_out = config.vision_config.hidden_size
+
+        n_patches = (config.vision_config.image_size //
+                     config.vision_config.patch_size)
+        if n_patches % 2 != 0:
+            self.img_processor_padding = nn.ReflectionPad2d((0, 1, 0, 1))
+            n_patches += 1
+        self.num_img_tokens = (n_patches // 2)**2
+
+        num_hidden_layers = (config.vision_config.num_hidden_layers +
+                             self.layer_idx +
+                             1 if self.layer_idx < 0 else self.layer_idx + 1)
+        self.img_processor = Idefics2VisionTransformer(
+            config.vision_config,
+            require_post_norm=False,
+            num_hidden_layers_override=num_hidden_layers)
+        self.image_token_compression = nn.AvgPool2d(kernel_size=2, stride=2)
+        self.img_projection = Phi4MMProjector(self.image_dim_out,
+                                              config.hidden_size)
+        self.global_img_feature_extensor = nn.Parameter(
+            torch.zeros([1, 1, self.image_dim_out]))
+        self.sub_img_feature_extensor = nn.Parameter(
+            torch.zeros([1, 1, 1, self.image_dim_out]))
+
+    def get_img_features(
+        self,
+        img_embeds: torch.FloatTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        img_feature = self.img_processor(img_embeds,
+                                         patch_attention_mask=attention_mask)
+
+        patch_feature = img_feature
+        # reshape to 2D tensor
+        width = int(math.sqrt(patch_feature.size(1)))
+        patch_feature = patch_feature.view(-1, width, width,
+                                           patch_feature.size(-1))
+        # convert to NCHW
+        patch_feature = patch_feature.permute(0, 3, 1, 2)
+        if getattr(self, "img_processor_padding", None) is not None:
+            patch_feature = self.img_processor_padding(patch_feature)
+        patch_feature = self.image_token_compression(patch_feature)
+        # convert to NHWC
+        patch_feature = patch_feature.permute(0, 2, 3, 1)
+        patch_feature = patch_feature.view(
+            -1,
+            patch_feature.size(1) * patch_feature.size(2),
+            patch_feature.size(-1))
+        return patch_feature
+
+    def forward(
+        self,
+        image_pixel_values: torch.FloatTensor,
+        image_sizes: Optional[torch.Tensor] = None,
+        image_attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        image_pixel_values = image_pixel_values.to(
+            self.img_processor.embeddings.patch_embedding.weight.dtype)
+
+        target_device = self.img_projection.up.bias.device
+        target_dtype = self.img_projection.up.bias.dtype
+
+        batch_size = image_pixel_values.shape[0]
+
+        img_features = self.get_img_features(
+            image_pixel_values.flatten(0, 1),
+            attention_mask=image_attention_mask.flatten(0, 1).to(
+                dtype=bool, device=target_device),
+        )
+        base_feat_size = int(np.sqrt(img_features.shape[1]))
+        img_features = img_features.view(batch_size, -1, base_feat_size**2,
+                                         self.image_dim_out)
+        image_sizes = image_sizes.view(-1, 2)
+
+        output_imgs = []
+        for idx in range(batch_size):
+            height, width = image_sizes[idx]
+            height_ratio = height // self.crop_size
+            width_ratio = width // self.crop_size
+            area_ratio = height_ratio * width_ratio
+
+            global_img = img_features[idx, :1]
+            global_img = global_img.reshape(1, base_feat_size, base_feat_size,
+                                            self.image_dim_out).contiguous()
+            temporary_extensor = self.sub_img_feature_extensor.repeat(
+                1, base_feat_size, 1, 1)
+            global_img = torch.cat([global_img, temporary_extensor],
+                                   dim=2).reshape(1, -1, self.image_dim_out)
+
+            sub_img = img_features[idx, 1:]
+            sub_img = sub_img[:area_ratio]
+            sub_img = (sub_img.reshape(
+                height_ratio, width_ratio, base_feat_size, base_feat_size,
+                self.image_dim_out).transpose(1, 2).reshape(
+                    1, height_ratio * base_feat_size,
+                    width_ratio * base_feat_size,
+                    self.image_dim_out).contiguous())
+
+            if image_attention_mask is not None:
+                reshaped_image_attention_mask = (
+                    image_attention_mask[idx, 1:area_ratio + 1,
+                                         0::2, 0::2].reshape(
+                                             height_ratio, width_ratio,
+                                             base_feat_size,
+                                             base_feat_size).transpose(
+                                                 1, 2).reshape(
+                                                     1, height_ratio *
+                                                     base_feat_size,
+                                                     width_ratio *
+                                                     base_feat_size))
+                useful_height = int(
+                    reshaped_image_attention_mask[0, :, 0].sum().item())
+                useful_width = int(
+                    reshaped_image_attention_mask[0, 0, :].sum().item())
+                sub_img = sub_img[:, :useful_height, :useful_width]
+                temporary_extensor = self.sub_img_feature_extensor.repeat(
+                    1, useful_height, 1, 1)
+            else:
+                temporary_extensor = self.sub_img_feature_extensor.repeat(
+                    1, height_ratio * base_feat_size, 1, 1)
+
+            sub_img = torch.cat([sub_img, temporary_extensor],
+                                dim=2).reshape(1, -1, self.image_dim_out)
+
+            # Merge global and sub
+            output_imgs.append(
+                torch.cat(
+                    [sub_img, self.global_img_feature_extensor, global_img],
+                    dim=1))
+
+        img_set_tensor = []
+        for output_img in output_imgs:
+            output_img = output_img.to(device=target_device,
+                                       dtype=target_dtype)
+            img_feature_proj = self.img_projection(output_img)
+            img_set_tensor.append(img_feature_proj.flatten(0, 1))
+
+        return img_set_tensor
+
+
+class Phi4MultimodalAudioMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: Phi4MultimodalAudioConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.layer_norm = nn.LayerNorm(config.hidden_size)
+        self.act_fn = MulAndSilu()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            config.hidden_size, [config.intermediate_size] * 2,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(config.intermediate_size,
+                                           config.hidden_size,
+                                           bias=True,
+                                           quant_config=quant_config,
+                                           prefix=f"{prefix}.down_proj")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.layer_norm(hidden_states)
+        hidden_states, _ = self.gate_up_proj(hidden_states)
+        hidden_states = self.act_fn(hidden_states)
+        hidden_states, _ = self.down_proj(hidden_states)
+        return hidden_states
+
+
+class Phi4MultimodalAudioAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: Phi4MultimodalAudioConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.total_num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.total_num_heads
+        if self.head_dim * self.total_num_heads != self.embed_dim:
+            raise ValueError(
+                "embed_dim must be divisible by num_heads "
+                f"(got `embed_dim`: {self.embed_dim} and `num_heads`:"
+                f" {self.num_heads}).")
+        self.scale = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.embed_dim,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.o_proj = RowParallelLinear(
+            input_size=self.embed_dim,
+            output_size=self.embed_dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
+        self.num_heads = divide(self.total_num_heads, self.tp_size)
+
+    def split_attn_mask(self, attention_mask: torch.Tensor) -> torch.Tensor:
+        start_idx = self.num_heads * self.tp_rank
+        end_idx = self.num_heads * (self.tp_rank + 1)
+        return attention_mask[:, start_idx:end_idx]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv_states, _ = self.qkv_proj(hidden_states)
+        query, key, value = qkv_states.chunk(3, dim=-1)
+
+        bsz, seq_len, _ = query.size()
+        query = query.view(bsz, seq_len, self.num_heads, self.head_dim)
+        key = key.view(bsz, seq_len, self.num_heads, self.head_dim)
+        value = value.view(bsz, seq_len, self.num_heads, self.head_dim)
+        query, key, value = (x.transpose(1, 2) for x in (query, key, value))
+
+        attention_mask = self.split_attn_mask(attention_mask)
+        out = F.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            scale=self.scale,
+            attn_mask=attention_mask,
+        )
+        out = out.transpose(1, 2).reshape(bsz, seq_len, -1)
+
+        attn_output, _ = self.o_proj(out)
+
+        return attn_output
+
+
+class Phi4MultimodalAudioConformerEncoderLayer(nn.Module):
+
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__()
+
+        self.feed_forward_in = Phi4MultimodalAudioMLP(config)
+        self.self_attn = Phi4MultimodalAudioAttention(config)
+        self.conv = Phi4MultimodalAudioConvModule(config)
+        self.feed_forward_out = Phi4MultimodalAudioMLP(config)
+        self.layer_norm_att = nn.LayerNorm(config.hidden_size)
+        self.layer_norm = nn.LayerNorm(config.hidden_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> torch.Tensor:
+        residual = hidden_states + 0.5 * self.feed_forward_in(hidden_states)
+        hidden_states = self.layer_norm_att(residual)
+
+        hidden_states = residual + self.self_attn(hidden_states,
+                                                  attention_mask)
+        hidden_states = hidden_states + self.conv(hidden_states)
+        hidden_states = hidden_states + 0.5 * self.feed_forward_out(
+            hidden_states)
+
+        out = self.layer_norm(hidden_states)
+
+        return out
+
+
+class Phi4MMAudioMeanVarianceNormLayer(nn.Module):
+    """Mean/variance normalization layer.
+
+    Will subtract mean and multiply input by inverted standard deviation.
+    Typically used as a very first layer in a model.
+
+    Args:
+        input_size: int
+            layer input size.
+    """
+
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__()
+        self.global_mean = nn.Parameter(torch.zeros(config.input_size))
+        self.global_invstd = nn.Parameter(torch.ones(config.input_size))
+
+    def forward(self, input_: torch.Tensor) -> torch.Tensor:
+        """MeanVarianceNormLayer Forward
+
+        Args:
+            input_: torch.Tensor
+                input tensor.
+        """
+        return (input_ - self.global_mean) * self.global_invstd
+
+
+class Phi4MultimodalAudioModel(nn.Module):
+
+    def __init__(self, config: Phi4MultimodalAudioConfig):
+        super().__init__()
+        self.config = config
+
+        self.encoder_embedding = Phi4MMAudioMeanVarianceNormLayer(config)
+        self.embed = Phi4MultimodalAudioNemoConvSubsampling(config)
+        self.relative_attention_bias_layer = (
+            Phi4MultimodalAudioRelativeAttentionBias(config))
+        self.encoders = nn.ModuleList([
+            Phi4MultimodalAudioConformerEncoderLayer(config)
+            for _ in range(config.num_blocks)
+        ])
+
+    def _streaming_mask(
+        self,
+        seq_len: int,
+        batch_size: int,
+        chunk_size: int,
+        left_chunk: int,
+    ):
+        # Create mask matrix for streaming
+        # S stores start index. if chunksize is 18, s is [0,18,36,....]
+        chunk_start_idx = np.arange(0, seq_len, chunk_size)
+
+        enc_streaming_mask = (adaptive_enc_mask(
+            seq_len, chunk_start_idx,
+            left_window=left_chunk).unsqueeze(0).expand([batch_size, -1, -1]))
+        return enc_streaming_mask
+
+    def forward_embeddings(
+        self,
+        hidden_states: torch.Tensor,
+        masks: torch.Tensor,
+    ):
+        """Forwarding the inputs through the top embedding layers"""
+        seq_len = math.ceil(hidden_states.shape[1] /
+                            self.config.time_reduction)
+        if seq_len <= 0:
+            raise ValueError(
+                f"Sequence length after time reduction is invalid: {seq_len}."
+                "Your input feature is too short.")
+
+        batch_size = hidden_states.shape[0]
+
+        enc_streaming_mask = self._streaming_mask(seq_len, batch_size,
+                                                  self.config.chunk_size,
+                                                  self.config.left_chunk)
+        enc_streaming_mask = enc_streaming_mask.to(hidden_states.device)
+
+        hidden_states, masks = self.embed(hidden_states, masks)
+
+        streaming_mask = enc_streaming_mask
+        if streaming_mask is not None and masks is not None:
+            hs_mask = masks & streaming_mask
+        elif masks is not None:
+            hs_mask = masks
+        else:
+            hs_mask = streaming_mask
+
+        return hidden_states, hs_mask, masks
+
+    def calculate_hs_mask(self, hidden_states: torch.Tensor,
+                          device: torch.device, mask: torch.Tensor):
+        max_audio_length = hidden_states.shape[1]
+        batch_size = hidden_states.shape[0]
+        enc_streaming_mask = self._streaming_mask(max_audio_length, batch_size,
+                                                  self.config.chunk_size,
+                                                  self.config.left_chunk)
+        enc_streaming_mask = enc_streaming_mask.to(device)
+        if mask is None:
+            return enc_streaming_mask
+
+        feature_lens = mask.sum(1)
+        padding_length = feature_lens
+        pad_mask = torch.arange(0, max_audio_length, device=device).expand(
+            padding_length.size(0), -1) < padding_length.unsqueeze(1)
+        pad_mask = pad_mask.unsqueeze(1)
+        pad_mask = pad_mask & enc_streaming_mask
+        return pad_mask
+
+    def forward(self,
+                hidden_states: torch.Tensor,
+                mask: Optional[torch.Tensor] = None):
+        hidden_states = self.encoder_embedding(hidden_states)
+        hidden_states, hs_mask, mask = self.forward_embeddings(
+            hidden_states, mask)
+
+        unfolded = False
+        bs, seq_len, _ = hidden_states.shape
+        max_seq_len = 500  # maximum position for absolute positional encoding
+        if seq_len > max_seq_len:
+            # audio sequence is longer than max_seq_len,
+            # unfold it into chunks of max_seq_len
+            unfolded = True
+            # the unfold op will drop residual frames,
+            # pad it to the multiple of max_seq_len
+            if seq_len % max_seq_len > 0:
+                chunk_pad_size = max_seq_len - (seq_len % max_seq_len)
+            else:
+                chunk_pad_size = 0
+            if chunk_pad_size > 0:
+                hidden_states_pad = F.pad(hidden_states,
+                                          (0, 0, 0, chunk_pad_size),
+                                          "constant", 0)
+                hidden_states = hidden_states_pad.to(hidden_states.device)
+
+            hidden_states = unfold_tensor(hidden_states, max_seq_len)
+            masks_unfold = None
+            if mask is not None:
+                # revise hs_mask here because the previous calculated hs_mask
+                # did not consider extra pad
+                subsampled_pad_mask = mask.squeeze(
+                    1)  # [bz, subsampled_unmask_seq_len]
+                extra_padded_subsamlped_pad_mask = F.pad(
+                    subsampled_pad_mask, (0, chunk_pad_size), "constant",
+                    False)  # extra padding to the pad mask
+                extra_padded_subsamlped_pad_mask = (
+                    extra_padded_subsamlped_pad_mask.unsqueeze(-1).float())
+                masks_unfold = unfold_tensor(
+                    extra_padded_subsamlped_pad_mask, max_seq_len
+                )  # unfold the pad mask like we did to the input tensor
+                masks_unfold = masks_unfold.squeeze(
+                    -1).bool()  # unfold op does not support bool tensor
+            hs_mask = self.calculate_hs_mask(
+                hidden_states, hidden_states.device, masks_unfold
+            )  # calculate hs_mask based on the unfolded pad mask
+
+        relative_attention_bias = self.relative_attention_bias_layer(
+            hidden_states)
+        attention_mask = hs_mask.unsqueeze(1) + relative_attention_bias
+
+        for layer in self.encoders:
+            hidden_states = layer(hidden_states, attention_mask)
+
+        if unfolded:
+            embed_dim = hidden_states.shape[-1]
+            hidden_states = hidden_states.reshape(bs, -1, embed_dim)
+            # if we ever padded before unfolding, we need to remove the padding
+            if chunk_pad_size > 0:
+                hidden_states = hidden_states[:, :-chunk_pad_size, :]
+
+        return hidden_states
+
+
+class Phi4MMAudioEmbedding(nn.Module):
+
+    def __init__(self, config: Phi4MultimodalConfig):
+        super().__init__()
+        self.config = config
+        self.layer_idx = config.audio_config.feature_layer
+
+        self.encoder = Phi4MultimodalAudioModel(config.audio_config)
+
+        audio_config = config.audio_config
+        proj_input_size = (audio_config.hidden_size *
+                           audio_config.downsample_rate)
+        self.vision_speech_projection = Phi4MMProjector(
+            proj_input_size, config.hidden_size)
+        self.speech_projection = Phi4MMProjector(proj_input_size,
+                                                 config.hidden_size)
+
+    def get_projection(
+        self,
+        audio_projection_mode: Literal["speech", "vision"],
+    ) -> Phi4MMProjector:
+        if audio_projection_mode == "speech":
+            return self.speech_projection
+        elif audio_projection_mode == "vision":
+            return self.vision_speech_projection
+
+    def forward(
+        self,
+        audio_input_features: torch.FloatTensor,
+        audio_embed_sizes=None,
+        audio_attention_mask=None,
+        audio_projection_mode="speech",
+    ) -> torch.FloatTensor:
+
+        audio_projection = self.get_projection(audio_projection_mode)
+
+        target_device = audio_projection.up.bias.device
+        target_dtype = audio_projection.up.bias.dtype
+
+        audio_input_features = audio_input_features.to(device=target_device,
+                                                       dtype=target_dtype)
+
+        audio_encoder_hidden_states = self.encoder(audio_input_features,
+                                                   audio_attention_mask)
+        audio_embeds = audio_projection(audio_encoder_hidden_states)
+
+        return audio_embeds.flatten(0, 1)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class Phi4MMImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    data: Union[torch.Tensor, list[torch.Tensor]]
+    """
+    Shape:
+    `(batch_size * num_images, 1 + num_patches, num_channels, height, width)`
+
+    Note that `num_patches` may be different per batch and image,
+    in which case the data is passed as a list instead of a batched tensor.
+    """
+
+    image_sizes: torch.Tensor
+    """
+    Shape: `(batch_size * num_images, 2)`
+
+    This should be in `(height, width)` format.
+    """
+
+    num_img_tokens: list[int]
+    """Shape: `(batch_size * num_images)`"""
+
+    image_attention_mask: torch.Tensor
+    """Shape: `(batch_size * num_images, H_mask, W_mask)`"""
+
+
+class Phi4MMImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: Union[torch.Tensor, list[torch.Tensor]]
+    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
+
+    `hidden_size` must match the hidden size of language model backbone.
+    """
+
+
+class Phi4MMAudioFeatureInputs(TypedDict):
+    type: Literal["audio_features"]
+    data: Union[torch.Tensor, list[torch.Tensor]]
+    """Shape: `(batch_size * num_audios, 80, M)"""
+
+
+class Phi4MMAudioEmbeddingInputs(TypedDict):
+    type: Literal["audio_embeds"]
+    data: NestedTensors
+    """Shape: `(batch_size, num_audios, audio_feature_size, hidden_size)"""
+
+
+Phi4MMImageInput = Union[Phi4MMImagePixelInputs, Phi4MMImageEmbeddingInputs]
+Phi4MMAudioInputs = Union[Phi4MMAudioFeatureInputs, Phi4MMAudioEmbeddingInputs]
+
+
+def cat_with_pad(tensors, dim, padding_value=0):
+    """
+    cat along dim, while pad to max for all other dims
+    """
+    ndim = tensors[0].dim()
+    assert all(
+        t.dim() == ndim for t in
+        tensors[1:]), "All tensors must have the same number of dimensions"
+
+    out_size = [max(t.shape[i] for t in tensors) for i in range(ndim)]
+    out_size[dim] = sum(t.shape[dim] for t in tensors)
+    output = tensors[0].new_full(out_size, padding_value)
+
+    index = 0
+    for t in tensors:
+        # Create a slice list where every dimension except dim is full slice
+        slices = [slice(0, t.shape[d]) for d in range(ndim)]
+        # Update only the concat dimension slice
+        slices[dim] = slice(index, index + t.shape[dim])
+
+        output[slices] = t
+        index += t.shape[dim]
+
+    return output
+
+
+class Phi4MMProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self) -> Phi4MultimodalConfig:
+        return self.ctx.get_hf_config(Phi4MultimodalConfig)
+
+    def get_hf_processor(self, **kwargs: object) -> Phi4MMProcessor:
+        return self.ctx.get_hf_processor(Phi4MMProcessor, **kwargs)
+
+    def get_feature_extractor(
+            self, **kwargs: object) -> Phi4MultimodalFeatureExtractor:
+        return self.get_hf_processor(**kwargs).audio_processor
+
+    def get_image_processor(
+        self,
+        processor: Optional[Phi4MMProcessor] = None,
+    ) -> Phi4MultimodalImageProcessorFast:
+        if processor is None:
+            processor = self.get_hf_processor()
+        return processor.image_processor
+
+    def get_dynamic_hd(
+        self,
+        processor: Optional[Phi4MMProcessor] = None,
+    ) -> int:
+        return self.get_image_processor(processor).dynamic_hd
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"audio": None, "image": None}
+
+    def _find_target_aspect_ratio(
+        self,
+        orig_width: int,
+        orig_height: int,
+        image_size: int,
+        max_num: int,
+        min_num: int,
+    ):
+        w_crop_num = math.ceil(orig_width / float(image_size))
+        h_crop_num = math.ceil(orig_height / float(image_size))
+        if w_crop_num * h_crop_num > max_num:
+            aspect_ratio = orig_width / orig_height
+
+            # calculate the existing image aspect ratio
+            target_ratios = set((i, j) for i in range(1, max_num + 1)
+                                for j in range(1, max_num + 1)
+                                if i * j <= max_num and i * j >= min_num)
+            target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+            # find the closest aspect ratio to the target
+            image_processor = self.get_image_processor()
+            target_aspect_ratio = image_processor.find_closest_aspect_ratio(
+                aspect_ratio,
+                target_ratios,
+                orig_width,
+                orig_height,
+            )
+
+            # calculate the target width and height
+            target_width = image_size * target_aspect_ratio[0]
+            target_height = image_size * target_aspect_ratio[1]
+        else:
+            target_width = image_size * w_crop_num
+            target_height = image_size * h_crop_num
+            target_aspect_ratio = (w_crop_num, h_crop_num)
+        return target_aspect_ratio, target_height, target_width
+
+    def _compute_num_image_tokens(
+        self,
+        orig_width: int,
+        orig_height: int,
+        dynamic_hd_size: int,
+        vit_image_size: int,
+        vit_patch_size: int,
+        token_compression_factor: int = 2,
+    ):
+        """
+        compute the number of tokens an image is expected to take up considering
+        the image encoder architecture and exclude output features containing 
+        only padding pixels
+
+        for siglip, vit_image_size=448, vit_patch_size=14, so output will be 
+        32x32 feature map
+        NOTE right now, Phi4MM uses hard-coded token_compression_factor=2
+        """
+        assert vit_image_size % vit_patch_size == 0, (
+            "vit_image_size must be divisible by vit_patch_size")
+        assert (vit_image_size // vit_patch_size %
+                token_compression_factor == 0), (
+                    "vit_image_size // vit_patch_size must be divisible by "
+                    "token_compression_factor")
+
+        target_aspect_ratio, target_height, target_width = (
+            self._find_target_aspect_ratio(orig_width,
+                                           orig_height,
+                                           vit_image_size,
+                                           dynamic_hd_size,
+                                           min_num=1))
+        assert target_aspect_ratio[0] * vit_image_size == target_width, (
+            f"{target_aspect_ratio[0]} * {vit_image_size} != {target_width}")
+        assert target_aspect_ratio[1] * vit_image_size == target_height, (
+            f"{target_aspect_ratio[1]} * {vit_image_size} != {target_height}")
+        assert (target_height % vit_image_size == 0
+                and target_width % vit_image_size == 0)
+
+        padding_height, padding_width = _get_padding_size(
+            orig_width, orig_height, target_height, target_width)
+        assert padding_width == 0 or padding_height == 0, \
+            "padding_width or padding_height must be 0"
+
+        target_feat_width = target_width // vit_patch_size
+        target_feat_height = target_height // vit_patch_size
+        if padding_width >= vit_patch_size:
+            assert padding_height == 0, "padding_height not 0"
+            non_pad_feat_width = target_feat_width - math.floor(
+                padding_width / vit_patch_size)
+            non_pad_feat_height = target_feat_height
+        elif padding_height >= vit_patch_size:
+            assert padding_width == 0, "padding_width not 0"
+            non_pad_feat_height = target_feat_height - math.floor(
+                padding_height / vit_patch_size)
+            non_pad_feat_width = target_feat_width
+        else:
+            # small padding shorter than a vit patch
+            non_pad_feat_width = target_feat_width
+            non_pad_feat_height = target_feat_height
+
+        feat_width = non_pad_feat_width // token_compression_factor
+        feat_height = non_pad_feat_height // token_compression_factor
+        # NOTE it's possible that the non-padding feature is not divisible
+        if non_pad_feat_width % token_compression_factor != 0:
+            feat_width += 1
+        if non_pad_feat_height % token_compression_factor != 0:
+            feat_height += 1
+        num_hd_patch_tokens = feat_width * feat_height
+        num_hd_newline_tokens = feat_height
+        vit_feature_size = vit_image_size // vit_patch_size
+        num_global_image_tokens = (vit_feature_size //
+                                   token_compression_factor)**2
+        num_sep_tokens = 1
+        num_global_image_newline_tokens = \
+            vit_feature_size // token_compression_factor
+
+        return (num_global_image_tokens + num_sep_tokens +
+                num_hd_patch_tokens + num_hd_newline_tokens +
+                num_global_image_newline_tokens)
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Phi4MMProcessor] = None,
+    ) -> int:
+        hf_config = self.get_hf_config()
+        vision_config = hf_config.vision_config
+        vit_image_size = vision_config.image_size
+        vit_patch_size = vision_config.patch_size
+
+        dynamic_hd_size = self.get_dynamic_hd(processor=processor)
+
+        # we use default `token_compression_factor=2`,
+        # since it's not in HF vision config.
+        image_num_tokens = self._compute_num_image_tokens(
+            image_width,
+            image_height,
+            dynamic_hd_size=dynamic_hd_size,
+            vit_image_size=vit_image_size,
+            vit_patch_size=vit_patch_size,
+        )
+
+        return image_num_tokens
+
+    def get_image_size_with_most_features(
+        self,
+        processor: Optional[Phi4MMProcessor] = None,
+    ) -> ImageSize:
+        vit_image_size = self.get_hf_config().vision_config.image_size
+
+        max_side = vit_image_size * self.get_dynamic_hd(processor=processor)
+        return ImageSize(height=max_side, width=vit_image_size)
+
+    def get_audio_num_frames(self, audio_len: int, sr: float) -> int:
+        """
+        Compute the output size of the `extract_features` method.
+
+        Args:
+            audio_len (int): Length of the input waveform in samples.
+            sr (float): Sampling rate of the waveform, either 16000 or 8000.
+
+        Returns:
+            tuple (int, int): Output size as (T, D), where:
+                T: Number of time frames.
+                D: Number of Mel filterbank bins (80).
+        """
+
+        # Resample to 16000 or 8000 if needed
+        if sr > 16000:
+            audio_len //= sr // 16000
+        elif 8000 <= sr < 16000:
+            # We'll resample to 16K from 8K
+            audio_len *= 2
+        elif sr < 8000:
+            raise RuntimeError(f"Unsupported sample rate {sr}")
+
+        # Spectrogram parameters for 16 kHz
+        win_length = 400  # Frame length in samples
+        hop_length = 160  # Frame shift in samples
+
+        # Calculate number of frames (T)
+        num_frames = (audio_len - win_length) // hop_length + 1
+        if num_frames < 1:
+            raise ValueError("Waveform too short for given parameters.")
+
+        # Return time frames (T)
+        return num_frames
+
+    def _compute_audio_embed_size(self, audio_frames: int) -> int:
+        """
+        Compute the size of audio embeddings from the number of audio frames.
+        """
+        # `_compute_audio_embed_size` in audio_processor use torch for
+        # computation, therefore we re-implement it to use pythonic
+        # numeric computation to avoid extra tensor conversion.
+        audio_processor = self.get_feature_extractor()
+        audio_compression_rate = audio_processor.audio_compression_rate
+        audio_downsample_rate = audio_processor.audio_downsample_rate
+
+        integer = audio_frames // audio_compression_rate
+        remainder = audio_frames % audio_compression_rate
+        result = integer + int(remainder > 0)
+
+        integer = result // audio_downsample_rate
+        remainder = result % audio_downsample_rate
+        result = integer + int(remainder > 0)  # qformer compression
+
+        return result
+
+
+class Phi4MMDummyInputsBuilder(BaseDummyInputsBuilder[Phi4MMProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_audios = mm_counts.get("audio", 0)
+        num_images = mm_counts.get("image", 0)
+
+        tokenizer = self.info.get_tokenizer()
+        image_tokens: str = tokenizer.image_token * num_images
+        audio_tokens: str = tokenizer.audio_token * num_audios
+
+        return image_tokens + audio_tokens
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_audios = mm_counts.get("audio", 0)
+        num_images = mm_counts.get("image", 0)
+
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+
+        mm_data = {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images),
+            "audio":
+            self._get_dummy_audios(length=_AUDIO_MAX_SOUNDFILE_SIZE,
+                                   num_audios=num_audios),
+        }
+
+        return mm_data
+
+
+class Phi4MMMultiModalProcessor(BaseMultiModalProcessor[Phi4MMProcessingInfo]):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        feature_extractor = self.info.get_feature_extractor()
+        return MultiModalDataParser(target_sr=feature_extractor.sampling_rate)
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        if not mm_data:
+            prompt_ids = self.info.get_tokenizer().encode(prompt)
+            prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
+            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
+
+        audio_data = mm_data.pop("audios", [])
+        if audio_data:
+            mm_data['audio'] = audio_data
+
+        processed_outputs = super()._call_hf_processor(prompt, mm_data,
+                                                       mm_kwargs, tok_kwargs)
+
+        if "image_pixel_values" in processed_outputs:
+            num_img_tokens = [
+                self.info.get_num_image_tokens(image_width=img_size[0],
+                                               image_height=img_size[1])
+                for img_size in processed_outputs["image_sizes"]
+            ]
+            processed_outputs["num_img_tokens"] = num_img_tokens
+
+        if audio_data:
+            audio_features = processed_outputs['audio_input_features']
+            sr = self.info.get_feature_extractor(**mm_kwargs).sampling_rate
+            feature_sizes = [
+                self.info.get_audio_num_frames(len(audio), sr)
+                for audio in audio_data
+            ]
+            processed_outputs['audio_input_features'] = [
+                audio_features[idx, :size]
+                for idx, size in enumerate(feature_sizes)
+            ]
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            image_pixel_values=MultiModalFieldConfig.batched("image"),
+            image_attention_mask=MultiModalFieldConfig.batched("image"),
+            image_sizes=MultiModalFieldConfig.batched("image"),
+            num_img_tokens=MultiModalFieldConfig.batched("image"),
+            audio_input_features=MultiModalFieldConfig.batched("audio"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        tokenizer = self.info.get_tokenizer()
+        image_token_id = tokenizer.vocab[tokenizer.image_token]
+        audio_token_id = tokenizer.vocab[tokenizer.audio_token]
+
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        audio_processor = self.info.get_feature_extractor(
+            **hf_processor_mm_kwargs)
+
+        def get_image_replacement_phi4mm(item_idx: int):
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems))
+
+            if isinstance(images, ImageEmbeddingItems):
+                num_image_tokens = images.get_feature_size(item_idx)
+            else:
+                image_size = images.get_image_size(item_idx)
+                num_image_tokens = self.info.get_num_image_tokens(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                    processor=hf_processor,
+                )
+
+            image_tokens = [image_token_id] * num_image_tokens
+
+            return image_tokens
+
+        def get_audio_replacement_phi4mm(item_idx: int):
+            audios = mm_items.get_items("audio", AudioProcessorItems)
+            # TODO(Isotr0py): support embedding inputs
+            audio_len = audios.get_audio_length(item_idx)
+            audio_frames = self.info.get_audio_num_frames(
+                audio_len, audio_processor.sampling_rate)
+            audio_embed_size = self.info._compute_audio_embed_size(
+                audio_frames)
+
+            audio_tokens = [audio_token_id] * audio_embed_size
+
+            return audio_tokens
+
+        return [
+            PromptReplacement(
+                modality="audio",
+                target=[audio_token_id],
+                replacement=get_audio_replacement_phi4mm,
+            ),
+            PromptReplacement(
+                modality="image",
+                target=[image_token_id],
+                replacement=get_image_replacement_phi4mm,
+            ),
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Phi4MMMultiModalProcessor,
+    info=Phi4MMProcessingInfo,
+    dummy_inputs=Phi4MMDummyInputsBuilder,
+)
+class Phi4MultimodalForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
+    """
+    Implements the Phi-4-multimodal-instruct model in vLLM.
+    """
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "qkv_proj",
+        ],
+        "gate_up_proj": [
+            "gate_up_proj",
+        ],
+    }
+
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            # Multimodal embedding
+            "model.embed_tokens_extend.": "",
+            # LLM backbone
+            "model.": "language_model.model.",
+        },
+        orig_to_new_substr={
+            # projection
+            ".img_projection_": ".img_projection.",
+            ".up_proj_for_speech.": ".speech_projection.up.",
+            ".up_proj_for_vision_speech.": ".vision_speech_projection.up.",
+            ".down_proj_for_speech.": ".speech_projection.down.",
+            ".down_proj_for_vision_speech.": ".vision_speech_projection.down.",
+        },
+    )
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<|image|>"
+        if modality.startswith("audio"):
+            return "<|audio|>"
+
+        raise ValueError("Only image or audio modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        # TODO: Optionally initializes these for supporting input embeddings.
+        self.image_embed = Phi4MMImageEmbedding(
+            config,
+            # prefix=maybe_prefix(prefix, "image_embed"),
+        )
+        self.audio_embed = Phi4MMAudioEmbedding(
+            config,
+            # prefix=maybe_prefix(prefix, "audio_embed"),
+        )
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=["Phi3ForCausalLM"],
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _parse_and_validate_audio_input(
+            self, **kwargs: object) -> Optional[Phi4MMAudioInputs]:
+        """
+        Parse and validate the audio input to the model.  This handles both 
+        audio features and audio embeddings, but only the former is used for
+        now.
+
+        Args:
+            kwargs (object): Keyword arguments.
+
+        Returns:
+            Optional[Phi4MMAudioInputs]: Parsed and validated audio inputs.
+        """
+        audio_features = kwargs.pop("audio_input_features", None)
+        audio_embeds = kwargs.pop("audio_embeds", None)
+
+        if audio_features is None and audio_embeds is None:
+            return None
+
+        if audio_features is not None:
+            if not isinstance(audio_features, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of audio features. "
+                                 f"Got type: {type(audio_features)}")
+
+            return Phi4MMAudioFeatureInputs(type="audio_features",
+                                            data=flatten_bn(audio_features))
+
+        if audio_embeds is not None:
+            if not isinstance(audio_embeds, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of audio embeds. "
+                                 f"Got type: {type(audio_embeds)}")
+
+            return Phi4MMAudioEmbeddingInputs(type="audio_embeds",
+                                              data=audio_embeds)
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _process_audio_input(self, audio_input: Phi4MMAudioInputs,
+                             audio_projection_mode: str) -> NestedTensors:
+        """
+        Create the audio embeddings from the audio input, where the audio input
+        is pairs of audio features and audio embed lengths.  The audio input is
+        created by `input_mapper_for_phi4mm_audio`.
+
+        Args:
+            audio_input (Phi4MMAudioInputs): Audio input.
+
+        Returns:
+            NestedTensors: Audio embeddings
+        """
+        if audio_input["type"] == "audio_embeds":
+            return audio_input["data"]
+
+        audio_features = audio_input["data"]
+        # (e.g. multiple examples) and the second dim is the multi-audio dim
+        # (e.g. multiple audios in the same example)
+
+        dtype = next(self.audio_embed.parameters()).dtype
+        audio_embeds = [
+            self.audio_embed(
+                features.unsqueeze(0).to(dtype),
+                audio_projection_mode=audio_projection_mode,
+            ) for features in audio_features
+        ]
+        return audio_embeds
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Phi4MMImagePixelInputs]:
+        image_pixel_values: NestedTensors = kwargs.get("image_pixel_values")
+        if image_pixel_values is None:
+            return None
+
+        image_sizes = kwargs.get("image_sizes")
+        image_attention_mask = kwargs.get("image_attention_mask")
+        num_img_tokens = kwargs.get("num_img_tokens")
+        assert image_sizes is not None and image_attention_mask is not None\
+              and num_img_tokens is not None, "Missing image inputs"
+
+        if is_list_of(image_pixel_values, torch.Tensor):
+            assert all(p.dim() == 5
+                       for p in image_pixel_values), "Incorrect image inputs"
+            # list len is batch_size.
+            # each tensor has dimension: num_img_per_example, num_hd_patches,
+            # channels, height, width.
+            # need to pad along num_hd_patches.
+            # mask size num_img_per_prompt, num_hd_patches, feat_h, heat_w.
+            image_pixel_values = cat_with_pad(image_pixel_values, dim=0)
+        elif isinstance(image_pixel_values, torch.Tensor):
+            # dimension: batch_size, num_img_per_example, num_hd_patches,
+            # channels, height, width.
+            # we flatten first 2 dims to make it a single large batch for
+            # SigLIP Encoder.
+            assert image_pixel_values.dim() == 6, "Incorrect image inputs"
+            image_pixel_values = image_pixel_values.flatten(0, 1)
+        else:
+            raise ValueError("Incorrect image_pixel_values inputs")
+
+        if isinstance(image_attention_mask, list):
+            image_attention_mask = cat_with_pad(image_attention_mask, dim=0)
+        elif isinstance(image_attention_mask, torch.Tensor):
+            image_attention_mask = image_attention_mask.flatten(0, 1)
+        else:
+            raise ValueError("Incorrect image_attention_mask inputs")
+
+        if isinstance(image_sizes, list):
+            image_sizes = torch.cat(image_sizes, dim=0)
+        elif isinstance(image_sizes, torch.Tensor):
+            image_sizes = image_sizes.flatten(0, 1)
+        else:
+            raise ValueError("Incorrect image_attention_mask inputs")
+
+        if isinstance(num_img_tokens, list):
+            num_img_tokens = [
+                n for num_tensor in num_img_tokens
+                for n in num_tensor.tolist()
+            ]
+        elif isinstance(num_img_tokens, torch.Tensor):
+            num_img_tokens = num_img_tokens.flatten(0, 1).tolist()
+        else:
+            raise ValueError("Incorrect image_attention_mask inputs")
+
+        return Phi4MMImagePixelInputs(
+            type="pixel_values",
+            data=image_pixel_values,
+            image_sizes=image_sizes,
+            image_attention_mask=image_attention_mask,
+            num_img_tokens=num_img_tokens,
+        )
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        modalities = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if input_key in ("image_pixel_values",
+                             "image_embeds") and "images" not in modalities:
+                modalities["images"] = self._parse_and_validate_image_input(
+                    **kwargs)
+            if input_key in ("audio_input_features",
+                             "audio_embeds") and "audios" not in modalities:
+                modalities["audios"] = self._parse_and_validate_audio_input(
+                    **kwargs)
+
+        return modalities
+
+    def _process_image_input(
+            self, image_input: Phi4MMImagePixelInputs) -> list[torch.Tensor]:
+        if image_input["type"] == "image_embeds":
+            image_embeds = image_input["image_embeds"].type(self.visual.dtype)
+        else:
+            dtype = next(self.image_embed.parameters()).dtype
+            pixel_values = image_input['data'].to(dtype)
+            image_sizes = image_input['image_sizes']
+            image_attention_mask = image_input['image_attention_mask']
+            image_embeds = self.image_embed(pixel_values, image_sizes,
+                                            image_attention_mask)
+        return image_embeds
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+
+        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not modalities:
+            return None
+
+        # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor correspoending to a multimodal data item (image or video).
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        audio_projection_mode = 'speech'
+        for modality in modalities:
+            # make sure process images first
+            if modality == "images":
+                audio_projection_mode = "vision"
+                image_input = modalities["images"]
+                vision_embeddings = self._process_image_input(image_input)
+                multimodal_embeddings += tuple(vision_embeddings)
+            if modality == "audios":
+                audio_input = modalities["audios"]
+                audio_embeddings = self._process_audio_input(
+                    audio_input, audio_projection_mode=audio_projection_mode)
+                multimodal_embeddings += tuple(audio_embeddings)
+
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                [_IMAGE_PLACEHOLDER_TOKEN_ID, _AUDIO_PLACEHOLDER_TOKEN_ID])
+        return inputs_embeds
+
+    def get_input_embeddings_v0(
+        self,
+        input_ids: torch.Tensor,
+        image_input: Optional[Phi4MMImagePixelInputs] = None,
+        audio_input: Optional[Phi4MMAudioFeatureInputs] = None,
+    ) -> torch.Tensor:
+        audio_projection_mode = 'speech'
+        inputs_embeds = self.get_input_embeddings(input_ids)
+        if image_input is not None:
+            image_embeds = self._process_image_input(image_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                image_embeds,
+                placeholder_token_id=_IMAGE_PLACEHOLDER_TOKEN_ID,
+            )
+            audio_projection_mode = 'vision'
+
+        if audio_input is not None:
+            audio_embeds = self._process_audio_input(
+                audio_input, audio_projection_mode=audio_projection_mode)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                audio_embeds,
+                placeholder_token_id=_AUDIO_PLACEHOLDER_TOKEN_ID,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> torch.Tensor:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner from
+        # `get_multimodal_embeddings` and `get_input_embeddings`, this
+        # condition is only for v0 compatibility.
+        elif inputs_embeds is None:
+            image_input = self._parse_and_validate_image_input(**kwargs)
+            audio_input = self._parse_and_validate_audio_input(**kwargs)
+
+            if image_input is None and audio_input is None:
+                inputs_embeds = None
+            else:
+                inputs_embeds = self.get_input_embeddings_v0(
+                    input_ids,
+                    image_input=image_input,
+                    audio_input=audio_input)
+                input_ids = None
+
+        hidden_states = self.language_model(
+            input_ids,
+            positions,
+            intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model.",
+            connector=[
+                "img_projection", "vision_speech_projection",
+                "speech_projection"
+            ],
+            tower_model=["image_embed", "audio_embed"],
+        )
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi4mm_audio.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi4mm_audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b0d66ae771dd085bc64778177a4d5ed71b9b1cc
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi4mm_audio.py
@@ -0,0 +1,1233 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+# Code copied from Microsoft/MoE by Jacob Platin (jacobplatin@microsoft.com)
+# but implemented by the Phi-Speech team
+#!/usr/bin/env python3
+import abc
+import math
+from typing import Literal, Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    CheckpointWrapper)
+from torch.distributed.fsdp.fully_sharded_data_parallel import (
+    FullyShardedDataParallel)
+from transformers import PretrainedConfig
+
+from vllm.model_executor.models.phi4mm_utils import (
+    AbsolutePositionalEncoding, ConvModule, FeedForward, MeanVarianceNormLayer,
+    MultiHeadedAttention, MultiSequential, NemoConvSubsampling,
+    T5RelativeAttentionLogitBias, adaptive_enc_mask, get_offset, unfold_tensor)
+
+_AUDIO_PLACEHOLDER_TOKEN_ID = 200011  # <|endoftext11|>
+
+
+class ConformerEncoderLayer(nn.Module):
+    """ConformerEncoder Layer module.
+    for more details see conformer paper:
+        https://arxiv.org/abs/2005.08100
+    This module implement the Conformer block layer.
+
+    Args:
+        d_model: int
+            attention dim.
+        ext_pw_out_channel: int
+            if > 0, ext_pw_out_channel is a dim channel size
+             for the last pointwise conv after swish activation.
+        depthwise_seperable_out_channel: int
+            if set different to 0, the number of 
+             depthwise_seperable_out_channel will be used as a
+             channel_out of the second conv1d layer. 
+             otherwise, it equal to 0, the second conv1d layer is skipped.
+        depthwise_multiplier: int
+            number of input_dim channels duplication. this value
+             will be used to compute the hidden channels of the Conv1D.
+        n_head: int
+            the number of heads for multihead attention module.
+        d_ffn: int
+            output size of the feed_forward blocks.
+        ext_pw_kernel_size: int
+            kernel size of the conv pointwise of the conformer.
+        kernel_size: int
+            kernel size.
+        dropout_rate: float
+            dropout rate.
+        causal: bool, optional
+            if set to True, convolution have no access
+             to future frames. default False.
+        batch_norm: bool, optional
+            if set to True, apply batchnorm before activation
+            in ConvModule layer of the conformer.
+            default False
+        activation: str, optional
+            activation function name,
+            one of ["relu", "swish", "sigmoid"],
+            sigmoid activation is only used with "glu_in_fnn=True",
+            default "relu".
+        chunk_se: int, optional
+            0 for offline SE.
+            1 for streaming SE, where mean is computed
+             by accumulated history until current chunk_se.
+            2 for streaming SE, where mean is computed
+             by only the current chunk.
+            default 0.
+        chunk_size: int, optional
+            chunk_size for cnn. default 18
+        conv_activation: str, optional
+            activation function used in ConvModule part
+            of the conformer, default "relu".
+        conv_glu_type: str, optional
+            activation function used for the glu inside
+            the ConvModule part of the conformer.
+            default: "sigmoid".
+        bias_in_glu: bool, optional
+            if set to True, use additive bias in the weight module
+             before GLU.
+        linear_glu_in_convm: bool, optional
+            if set to True, use GLULinear module,
+             otherwise, used GLUPointWiseConv module.
+              default to False.
+        attention_inner_dim: int, optional
+            if equal to -1, attention dim for linears k/q/v is
+            equal to d_model. otherwise attention_inner_dim is used.
+            default -1.
+        attention_glu_type: str, optional
+            activation function for glu used in the multihead attention,
+             default "swish".
+        activation_checkpointing: str, optional
+            a dictionarry of {"module","interval","offload"}, where
+                "module": str
+                    accept ["transformer", "attention"] to select
+                    which module should do activation checkpointing.
+                "interval": int, default 1,
+                    interval of applying activation checkpointing,
+                    interval = 1 means that we apply checkpointing
+                    on every layer (if activation), otherwise,
+                    we apply it every x interval.
+                "offload": bool, default False,
+                    if set to True, we offload activation to cpu and
+                    reload it during backward, otherwise,
+                    we recalculate activation in backward.
+            default "".
+        export: bool, optional
+            if set to True, it remove the padding from convolutional layers
+             and allow the onnx conversion for inference.
+              default False.
+        use_pt_scaled_dot_product_attention: bool, optional
+            if set to True, use pytorch's scaled dot product attention 
+            implementation in training.
+        attn_group_sizes: int, optional
+            the number of groups to use for attention, default 1 
+            (Multi-Head Attention),
+            1 = typical Multi-Head Attention,
+            1 < attn_group_sizes < attention_heads = Grouped-Query Attention
+            attn_group_sizes = attention_heads = Multi-Query Attention
+    """
+
+    def __init__(
+        self,
+        d_model=512,
+        ext_pw_out_channel=0,
+        depthwise_seperable_out_channel=256,
+        depthwise_multiplier=1,
+        n_head=4,
+        d_ffn=2048,
+        ext_pw_kernel_size=1,
+        kernel_size=3,
+        dropout_rate=0.1,
+        causal=False,
+        batch_norm=False,
+        activation="relu",
+        chunk_se=0,
+        chunk_size=18,
+        conv_activation="relu",
+        conv_glu_type="sigmoid",
+        bias_in_glu=True,
+        linear_glu_in_convm=False,
+        attention_inner_dim=-1,
+        attention_glu_type="swish",
+        activation_checkpointing="",
+        export=False,
+        use_pt_scaled_dot_product_attention=False,
+        attn_group_sizes: int = 1,
+    ):
+        super().__init__()
+
+        self.feed_forward_in = FeedForward(
+            d_model=d_model,
+            d_inner=d_ffn,
+            dropout_rate=dropout_rate,
+            activation=activation,
+            bias_in_glu=bias_in_glu,
+        )
+
+        self.self_attn = MultiHeadedAttention(
+            n_head,
+            d_model,
+            dropout_rate,
+            attention_inner_dim,
+            attention_glu_type,
+            bias_in_glu,
+            use_pt_scaled_dot_product_attention=
+            use_pt_scaled_dot_product_attention,
+            group_size=attn_group_sizes,
+        )
+        self.conv = ConvModule(
+            d_model,
+            ext_pw_out_channel,
+            depthwise_seperable_out_channel,
+            ext_pw_kernel_size,
+            kernel_size,
+            depthwise_multiplier,
+            dropout_rate,
+            causal,
+            batch_norm,
+            chunk_se,
+            chunk_size,
+            conv_activation,
+            conv_glu_type,
+            bias_in_glu,
+            linear_glu_in_convm,
+            export=export,
+        )
+
+        self.feed_forward_out = FeedForward(
+            d_model=d_model,
+            d_inner=d_ffn,
+            dropout_rate=dropout_rate,
+            activation=activation,
+            bias_in_glu=bias_in_glu,
+        )
+
+        self.layer_norm_att = nn.LayerNorm(d_model)
+        self.layer_norm = nn.LayerNorm(d_model)
+
+    def forward(
+        self,
+        x,
+        pos_k,
+        pos_v,
+        mask,
+        relative_attention_bias: Optional[Tensor] = None,
+    ):
+        """ConformerEncoder forward.
+
+        Args:
+            x: torch.Tensor
+                input feature of shape (batch, max_time_in, size)
+            pos_k: torch.Tensor
+                positional key embedding.
+            mask: torch.Tensor
+                mask for x (batch, max_time_in)
+            relative_attention_bias: Optional[torch.Tensor]
+                bias added to attention logits w.r.t. relative positions 
+                (1, n_head, time1, time2)
+        """
+        x = x + 0.5 * self.feed_forward_in(x)
+        norm_x = self.layer_norm_att(x)
+
+        x = x + self.self_attn(
+            norm_x,
+            norm_x,
+            norm_x,
+            pos_k,
+            pos_v,
+            mask,
+            relative_attention_bias=relative_attention_bias,
+        )
+        x = x + self.conv(x)
+        x = x + 0.5 * self.feed_forward_out(x)
+
+        out = self.layer_norm(x)
+
+        return out, pos_k, pos_v, mask
+
+
+class TransformerEncoderBase(abc.ABC, nn.Module):
+    """The Base class for Transformer based encoders
+
+    Please set causal = True in streaming model
+    Args:
+        input_size: int
+            input feature dimension.
+        chunk_size: int, list(int)
+            Number of frames for each chunk
+            This variable can take 2 forms:
+            int:  Used for inference, or single chunk size training
+            list(int) : Used only for variable chunk size training
+            Some examples for the 2 cases:
+            chunk_size = 12
+            chunk_size = [6, 8, 12, 24]
+        left_chunk: int, list(int)
+            Number of chunks used for masking in streaming mode.
+            This variable can take 2 forms:
+            int:  Used for inference, or single chunk size training
+            list(int) : Used only for variable chunk size training. When
+            chunk_size is a list, left_chunk must be a list with same length.
+            Some examples for the 2 cases:
+            left_chunk = 6
+            left_chunk = [12, 9, 6, 3]
+        attention_dim: int, optional
+            attention dimension. default 256.
+        attention_heads: int, optional
+            the number of heads. default 4
+        input_layer: str, optional
+            input layer type before Conformer,
+            one of ["linear", "conv2d", "custom", "vgg2l", "embed"],
+            default "conv2d"
+        cnn_out: int, optional
+            the number of CNN channels before Conformer.
+            default -1.
+        cnn_layer_norm: bool, optional
+            layer norm between Conformer and the first CNN.
+            default False.
+        time_reduction: int, optional
+            time reduction factor
+            default 4
+        dropout_rate: float, optional
+            dropout rate. default 0.1
+        padding_idx: int, optional
+            padding index for input_layer=embed
+            default -1
+        relative_attention_bias_args: dict, optional
+            use more efficient scalar bias-based relative multihead attention
+            (Q*K^T + B) implemented in cmb.basics.embedding.
+            [T5/ALiBi]RelativeAttentionLogitBias
+            usage: relative_attention_bias_args={"type": t5/alibi}
+            additional method-specific arguments can be provided (see 
+            transformer_base.py)
+        positional_dropout_rate: float, optional
+            dropout rate after positional encoding. default 0.0
+        nemo_conv_settings: dict, optional
+            A dictionary of settings for NeMo Subsampling.
+            default None
+        conv2d_extra_padding: str, optional
+            Add extra padding in conv2d subsampling layers. Choices are
+            (feat, feat_time, none, True).
+            if True or feat_time, the extra padding is added into non full
+            supraframe utts in batch.
+            Default: none
+        attention_group_size: int, optional
+            the number of groups to use for attention, default 1 
+            (Multi-Head Attention),
+            1 = typical Multi-Head Attention,
+            1 < attention_group_size < attention_heads = Grouped-Query 
+            Attention
+            attention_group_size = attention_heads = Multi-Query Attention
+    """
+
+    def __init__(
+        self,
+        input_size,
+        chunk_size,
+        left_chunk,
+        attention_dim=256,
+        attention_heads=4,
+        input_layer="nemo_conv",
+        cnn_out=-1,
+        cnn_layer_norm=False,
+        time_reduction=4,
+        dropout_rate=0.0,
+        padding_idx=-1,
+        relative_attention_bias_args=None,
+        positional_dropout_rate=0.0,
+        nemo_conv_settings=None,
+        conv2d_extra_padding: Literal["feat", "feat_time", "none",
+                                      True] = "none",
+        attention_group_size=1,
+        encoder_embedding_config=None,
+    ):
+        super().__init__()
+        self.input_size = input_size
+        self.input_layer = input_layer
+        self.chunk_size = chunk_size
+        self.left_chunk = left_chunk
+        self.attention_dim = attention_dim
+        self.num_heads = attention_heads
+        self.attention_group_size = attention_group_size
+        self.time_reduction = time_reduction
+        self.nemo_conv_settings = nemo_conv_settings
+        self.encoder_embedding_config = encoder_embedding_config
+
+        if self.input_layer == "nemo_conv":
+            default_nemo_conv_settings = {
+                "subsampling": "dw_striding",
+                "subsampling_factor": self.time_reduction,
+                "feat_in": input_size,
+                "feat_out": attention_dim,
+                "conv_channels": 256,
+                "subsampling_conv_chunking_factor": 1,
+                "activation": nn.ReLU(),
+                "is_causal": False,
+            }
+            # Override any of the defaults with the incoming, user settings
+            if nemo_conv_settings:
+                default_nemo_conv_settings.update(nemo_conv_settings)
+                for i in ["subsampling_factor", "feat_in", "feat_out"]:
+                    assert (
+                        i not in nemo_conv_settings
+                    ), "{i} should be specified outside of the NeMo dictionary"
+
+            self.embed = NemoConvSubsampling(**default_nemo_conv_settings, )
+        else:
+            raise ValueError("unknown input_layer: " + input_layer)
+
+        self.pos_emb = AbsolutePositionalEncoding(attention_dim,
+                                                  positional_dropout_rate)
+
+        self.relative_attention_bias_type = (
+            relative_attention_bias_args.get("type")
+            if relative_attention_bias_args else None)
+        if self.relative_attention_bias_type == "t5":
+            assert (self.num_heads % self.attention_group_size == 0
+                    ), "attention_group_size must divide n_head"
+            self.relative_attention_bias_layer = T5RelativeAttentionLogitBias(
+                self.num_heads // self.attention_group_size,
+                max_distance=relative_attention_bias_args.get(
+                    "t5_bias_max_distance", 1000),
+                symmetric=relative_attention_bias_args.get(
+                    "t5_bias_symmetric", False),
+            )
+        else:
+            raise NotImplementedError
+
+        self.encoder_embedding = MeanVarianceNormLayer(
+            self.encoder_embedding_config["input_size"])
+
+    def compute_lens_change(self, feature_lens):
+        """feature_lens: int
+        return updated feature lens.
+
+        This used to return a different lambda function for each case that 
+        computed the right thing.  That does not work within Torchscript. 
+        If you really need this to be faster, create nn.Module()-s for all
+        the cases and return one of them.  Torchscript does support that.
+        """
+        if self.input_layer == "nemo_conv":
+            # Handle the special causal case
+            subsampling_causal_cond = self.nemo_conv_settings.get(
+                "subsampling", "dw_striding") in [
+                    "dw_striding",
+                    "striding",
+                    "striding_conv1d",
+                ]
+            is_causal = self.nemo_conv_settings.get("is_causal", False)
+            if is_causal and subsampling_causal_cond:
+                lens_change = (torch.ceil(feature_lens /
+                                          self.time_reduction).long()
+                               if isinstance(feature_lens, Tensor) else
+                               math.ceil(feature_lens / self.time_reduction))
+                feature_lens_remainder = feature_lens % self.time_reduction
+                if isinstance(feature_lens, Tensor):
+                    lens_change[feature_lens_remainder != 1] += 1
+                elif feature_lens_remainder != 1:
+                    lens_change += 1
+                return lens_change
+            ceil_func = (math.ceil
+                         if isinstance(feature_lens, int) else torch.ceil)
+            return ceil_func(feature_lens / self.time_reduction)
+
+    @abc.abstractmethod
+    def forward(self):
+        """Abstract forward method implementation."""
+
+    def _chunk_size_selection(self, chunk_size=None, left_chunk=None):
+        """If chunk size is a list, we will randomly select a chunk size."""
+
+        if chunk_size is None:
+            chunk_size = self.chunk_size
+        if left_chunk is None:
+            left_chunk = self.left_chunk
+        if isinstance(chunk_size, list):
+            # Variable chunk size during training
+            chunk_size_index = int(
+                torch.randint(low=0, high=len(chunk_size), size=(1, )))
+            chunk_size_train_eff = chunk_size[chunk_size_index]
+            if not isinstance(left_chunk, list):
+                raise ValueError(
+                    "Since chunk_size is a list, left_chunk must be a list")
+            if len(left_chunk) != len(chunk_size):
+                raise ValueError(
+                    "The length of left_chunk must be the same as length of "\
+                        "chunk_size."
+                )
+            left_chunk_train_eff = left_chunk[chunk_size_index]
+        else:
+            chunk_size_train_eff = chunk_size
+            left_chunk_train_eff = left_chunk
+
+        return chunk_size_train_eff, left_chunk_train_eff
+
+    def _get_embed_class(self, embed):
+        # pylint: disable=protected-access
+        is_embed_using_act_chkpt = isinstance(embed, CheckpointWrapper)
+        is_embed_fsdp_wrapped = isinstance(embed, FullyShardedDataParallel)
+        embed_class = embed
+        if is_embed_using_act_chkpt:
+            embed_class = embed._checkpoint_wrapped_module
+        if is_embed_fsdp_wrapped:
+            embed_class = embed.module
+        return embed_class
+
+    def _forward_embeddings_core(self, input_tensor, masks):
+        embed_class = self._get_embed_class(self.embed)
+        assert isinstance(embed_class, NemoConvSubsampling)
+        input_tensor, masks = self.embed(input_tensor, masks)
+        return input_tensor, masks
+
+    def _position_embedding(self, input_tensor):
+        pos_k = None
+        pos_v = None
+        if self.relative_attention_bias_layer is None:
+            input_tensor = self.pos_emb(
+                input_tensor)  # default to add abs sinusoid embedding
+        return pos_k, pos_v
+
+    def _streaming_mask(self, seq_len, batch_size, chunk_size, left_chunk):
+        chunk_size_train_eff, left_chunk_train_eff = \
+            self._chunk_size_selection(chunk_size, left_chunk)
+
+        # Create mask matrix for streaming
+        # S stores start index. if chunksize is 18, s is [0,18,36,....]
+        chunk_start_idx = np.arange(0, seq_len, chunk_size_train_eff)
+
+        enc_streaming_mask = (adaptive_enc_mask(
+            seq_len, chunk_start_idx,
+            left_window=left_chunk_train_eff).unsqueeze(0).expand(
+                [batch_size, -1, -1]))
+        return enc_streaming_mask
+
+    def forward_embeddings(self,
+                           xs_pad,
+                           masks,
+                           chunk_size_nc=None,
+                           left_chunk_nc=None):
+        """Forwarding the inputs through the top embedding layers
+
+        Args:
+            xs_pad: torch.Tensor
+                input tensor
+            masks: torch.Tensor
+                input mask
+            chunk_size_nc: (optional, default is None) chunk size for 
+                            non-causal layers
+            left_chunk_nc: (optional, default is None) # of left chunks for
+                            non-causal layers
+        """
+        # pylint: disable=R0915
+        # get new lens.
+        seq_len = int(self.compute_lens_change(xs_pad.shape[1]))
+        if seq_len <= 0:
+            raise ValueError(
+                f"""The sequence length after time reduction is invalid: 
+                {seq_len}. Your input feature is too short. Consider 
+                filtering out the very short sentence from data 
+                loader""", )
+
+        batch_size = xs_pad.shape[0]
+
+        enc_streaming_mask = self._streaming_mask(seq_len, batch_size,
+                                                  self.chunk_size,
+                                                  self.left_chunk)
+
+        if xs_pad.is_cuda:
+            enc_streaming_mask = enc_streaming_mask.cuda()
+            xs_pad = xs_pad.cuda()
+
+        input_tensor = xs_pad
+        input_tensor, masks = self._forward_embeddings_core(
+            input_tensor, masks)
+
+        streaming_mask = enc_streaming_mask
+        if streaming_mask is not None and masks is not None:
+            hs_mask = masks & streaming_mask
+        elif masks is not None:
+            hs_mask = masks
+        else:
+            hs_mask = streaming_mask
+
+        if chunk_size_nc is not None:
+            enc_streaming_mask_nc = self._streaming_mask(
+                seq_len, batch_size, chunk_size_nc, left_chunk_nc)
+            if xs_pad.is_cuda:
+                enc_streaming_mask_nc = enc_streaming_mask_nc.cuda()
+            if masks is not None:
+                hs_mask_nc = masks & enc_streaming_mask_nc
+            else:
+                hs_mask_nc = enc_streaming_mask_nc
+        else:
+            hs_mask_nc = None
+
+        pos_k, pos_v = self._position_embedding(input_tensor)
+
+        if chunk_size_nc is None:
+            return input_tensor, pos_k, pos_v, hs_mask, masks
+        return input_tensor, pos_k, pos_v, hs_mask, masks, hs_mask_nc
+
+    def get_offset(self):
+        """Returns offset used when retaining inputs for decoding.
+
+        This is essentially, how many additional frames have to be added to
+        the front-end CNN input to ensure it can produce a single output.
+        So if the "padding" parameter is 0, typically offset will be > 0.
+        """
+        return get_offset(self.input_layer, self.time_reduction)
+
+
+class ConformerEncoder(TransformerEncoderBase):
+    """ConformerEncoder module.
+    see original paper for more details:
+        https://arxiv.org/abs/2005.08100
+
+    Please set causal = True in streaming model
+    Args:
+        input_size: int
+            input feature dimension.
+        chunk_size: int, list(int)
+            Number of frames for each chunk
+            This variable can take 2 forms:
+            int:  Used for inference, or single chunk size training
+            list(int) : Used only for variable chunk size training
+            Some examples for the 2 cases:
+            chunk_size = 12
+            chunk_size = [6, 8, 12, 24]
+        left_chunk: int, list(int)
+            Number of chunks used for masking in streaming mode.
+            This variable can take 2 forms:
+            int:  Used for inference, or single chunk size training
+            list(int) : Used only for variable chunk size training. When
+            chunk_size is a list, left_chunk must be a list with same length.
+            Some examples for the 2 cases:
+            left_chunk = 6
+            left_chunk = [12, 9, 6, 3]
+        left_chunk: int
+            number of chunks used for masking in streaming mode.
+        num_lang: int
+            This parameter is used to store the number of languages in the 
+            lang_dict, only used for multiseed/multilingual models. 
+            default None.
+        attention_dim: int, optional
+            attention dimension. default 256.
+        attention_heads: int, optional
+            the number of heads. default 4
+        linear_units:
+            the number of units of position-wise feed forward.
+            default 2048
+        num_block:
+            number of Transformer layer. default 6
+        dropout_rate: float, optional
+            dropout rate. default 0.1
+        input_layer: str, optional
+            input layer type before Conformer,
+            one of ["linear", "conv2d", "custom", "vgg2l", "embed"],
+            default "conv2d"
+        causal: bool, optional
+            if set to True, convolution have no access
+             to future frames. default False.
+        batch_norm: bool, optional
+            if set to True, apply batchnorm before activation
+            in ConvModule layer of the conformer.
+            default False
+        cnn_out: int, optional
+            the number of CNN channels before Conformer.
+            default -1.
+        cnn_layer_norm: bool, optional
+            layer norm between Conformer and the first CNN.
+            default False.
+        ext_pw_out_channel: int, optional
+            the number of channel for CNN
+            before depthwise_seperable_CNN.
+            If 0 then use linear. default 0.
+        ext_pw_kernel_size: int, optional
+            kernel size of N before depthwise_seperable_CNN.
+            only work for ext_pw_out_channel > 0.
+            default 1
+        depthwise_seperable_out_channel: int, optional
+            the number of channel for
+            depthwise_seperable_CNN.
+            default 256.
+        depthwise_multiplier: int, optional
+            the number of multiplier for
+            depthwise_seperable_CNN.
+            default 1.
+        chunk_se: int, optional
+            0 for offline SE.
+            1 for streaming SE, where mean is computed
+             by accumulated history until current chunk_se.
+            2 for streaming SE, where mean is computed
+             by only the current chunk.
+            default 0.
+        kernel_size: int, optional
+            the number of kernels for depthwise_seperable_CNN.
+            default 3.
+        activation: str, optional
+            FeedForward block activation.
+            one of ["relu", "swish", "sigmoid"]
+            default "relu".
+        conv_activation: str, optional
+            activation function used in ConvModule part
+            of the conformer, default "relu".
+        conv_glu_type: str, optional
+            activation used use glu in depthwise_seperable_CNN,
+            default "sigmoid"
+        bias_in_glu: bool, optional
+            if set to True, use additive bias in the weight module
+             before GLU. default True
+        linear_glu_in_convm: bool, optional
+            if set to True, use GLULinear module,
+             otherwise, used GLUPointWiseConv module.
+              default to False.
+        attention_glu_type: str
+            only work for glu_in_attention !=0
+            default "swish".
+        export: bool, optional
+            if set to True, it remove the padding from convolutional layers
+             and allow the onnx conversion for inference.
+              default False.
+        activation_checkpointing: str, optional
+            a dictionarry of {"module","interval","offload"}, where
+                "module": str
+                    accept ["transformer", "attention"] to select
+                    which module should do activation checkpointing.
+                "interval": int, default 1,
+                    interval of applying activation checkpointing,
+                    interval = 1 means that we apply checkpointing
+                    on every layer (if activation), otherwise,
+                    we apply it every x interval.
+                "offload": bool, default False,
+                    if set to True, we offload activation to cpu and
+                    reload it during backward, otherwise,
+                    we recalculate activation in backward.
+            default "".
+        extra_layer_output_idx: int
+            the layer index to be exposed.
+        relative_attention_bias_args: dict, optional
+            use more efficient scalar bias-based relative multihead attention 
+            (Q*K^T + B) implemented in cmb.basics.embedding.
+            [T5/ALiBi]RelativeAttentionLogitBias
+            usage: relative_attention_bias_args={"type": t5/alibi}
+            additional method-specific arguments can be provided (see 
+            transformer_base.py)
+        time_reduction: int optional
+            time reduction factor
+            default 4
+        use_pt_scaled_dot_product_attention: whether to use pytorch scaled 
+            dot product attention in training.
+            Default: False
+        nemo_conv_settings: dict, optional
+            A dictionary of settings for NeMo Subsampling.
+            default: None
+            usage: nemo_conv_settings=
+                {
+                    "subsampling":
+                    dw_striding/striding/dw_striding_conv1d/striding_conv1d,
+                    "conv_channels": int,
+                    "subsampling_conv_chunking_factor": int,
+                    "is_causal": True/False
+                }
+        conv2d_extra_padding: str, optional
+            Add extra padding in conv2d subsampling layers. Choices are
+            (feat, feat_time, none, True)
+            Default: none
+        replication_pad_for_subsample_embedding:  For batched-streaming 
+            decoding, use "replication" padding for the cache at start of
+            utterance.
+            Default: False
+        attention_group_size: int, optional
+            the number of groups to use for attention, default 1 
+            (Multi-Head Attention),
+            1 = typical Multi-Head Attention,
+            1 < attention_group_size < attention_heads = Grouped-Query
+            Attention
+            attention_group_size = attention_heads = Multi-Query Attention
+    """
+
+    extra_multi_layer_output_idxs: list[int]
+
+    def __init__(  # pylint: disable-all
+        self,
+        input_size,
+        chunk_size,
+        left_chunk,
+        num_lang=None,
+        attention_dim=256,
+        attention_heads=4,
+        linear_units=2048,
+        num_blocks=6,
+        dropout_rate=0.1,
+        input_layer="nemo_conv",
+        causal=True,
+        batch_norm=False,
+        cnn_out=-1,
+        cnn_layer_norm=False,
+        ext_pw_out_channel=0,
+        ext_pw_kernel_size=1,
+        depthwise_seperable_out_channel=256,
+        depthwise_multiplier=1,
+        chunk_se=0,
+        kernel_size=3,
+        activation="relu",
+        conv_activation="relu",
+        conv_glu_type="sigmoid",
+        bias_in_glu=True,
+        linear_glu_in_convm=False,
+        attention_glu_type="swish",
+        export=False,
+        extra_layer_output_idx=-1,
+        extra_multi_layer_output_idxs=[],  # noqa
+        activation_checkpointing="",
+        relative_attention_bias_args=None,
+        time_reduction=4,
+        use_pt_scaled_dot_product_attention=False,
+        nemo_conv_settings=None,
+        conv2d_extra_padding: Literal["feat", "feat_time", "none",
+                                      True] = "none",
+        replication_pad_for_subsample_embedding=False,
+        attention_group_size=1,
+        encoder_embedding_config=None,
+    ):
+        super().__init__(
+            input_size,
+            chunk_size,
+            left_chunk,
+            attention_dim,
+            attention_heads,
+            input_layer,
+            cnn_out,
+            cnn_layer_norm,
+            time_reduction,
+            dropout_rate=dropout_rate,
+            relative_attention_bias_args=relative_attention_bias_args,
+            positional_dropout_rate=0.0,
+            nemo_conv_settings=nemo_conv_settings,
+            conv2d_extra_padding=conv2d_extra_padding,
+            attention_group_size=attention_group_size,
+            encoder_embedding_config=encoder_embedding_config,
+        )
+        self.num_blocks = num_blocks
+        self.num_lang = num_lang
+        self.kernel_size = kernel_size
+        self.replication_pad_for_subsample_embedding: bool = (
+            replication_pad_for_subsample_embedding)
+        assert (self.num_heads % attention_group_size == 0
+                ), "attention_group_size must divide n_head"
+        self.num_heads_k = self.num_heads // attention_group_size
+
+        self.encoders = MultiSequential(*[
+            ConformerEncoderLayer(
+                d_model=attention_dim,
+                ext_pw_out_channel=ext_pw_out_channel,
+                depthwise_seperable_out_channel=depthwise_seperable_out_channel,
+                depthwise_multiplier=depthwise_multiplier,
+                n_head=attention_heads,
+                d_ffn=linear_units,
+                ext_pw_kernel_size=ext_pw_kernel_size,
+                kernel_size=kernel_size,
+                dropout_rate=dropout_rate,
+                causal=causal,
+                batch_norm=batch_norm,
+                activation=activation,
+                chunk_se=chunk_se,
+                chunk_size=chunk_size,
+                conv_activation=conv_activation,
+                conv_glu_type=conv_glu_type,
+                bias_in_glu=bias_in_glu,
+                linear_glu_in_convm=linear_glu_in_convm,
+                attention_glu_type=attention_glu_type,
+                activation_checkpointing=activation_checkpointing,
+                export=export,
+                use_pt_scaled_dot_product_attention=
+                use_pt_scaled_dot_product_attention,
+                attn_group_sizes=attention_group_size,
+            ) for _ in range(num_blocks)
+        ])
+        self.extra_layer_output_idx = extra_layer_output_idx
+        self.extra_multi_layer_output_idxs = extra_multi_layer_output_idxs
+        # Make a zeros scalar we can use in get_initial_state to determine
+        # the device and the needed dtype:
+        self.register_buffer("dev_type", torch.zeros(()), persistent=False)
+
+    def init_relative_attention_bias(self, input_tensor):
+        if self.relative_attention_bias_layer:
+            return self.relative_attention_bias_layer(input_tensor)
+
+    def calculate_hs_mask(self, xs_pad, device, mask):
+        max_audio_length = xs_pad.shape[1]
+        batch_size = xs_pad.shape[0]
+        enc_streaming_mask = self._streaming_mask(max_audio_length, batch_size,
+                                                  self.chunk_size,
+                                                  self.left_chunk)
+        enc_streaming_mask = enc_streaming_mask.to(device)
+        if mask is None:
+            return enc_streaming_mask
+
+        feature_lens = mask.sum(1)
+        padding_length = feature_lens
+        pad_mask = (torch.arange(0, max_audio_length,
+                                 device=device).expand(padding_length.size(0),
+                                                       -1)
+                    < padding_length.unsqueeze(1))
+        pad_mask = pad_mask.unsqueeze(1)
+        pad_mask = pad_mask & enc_streaming_mask
+        return pad_mask
+
+    @torch.jit.ignore
+    def forward(self, xs_pad, masks):
+        """Conformer Forward function
+
+        Args:
+            xs_pad: torch.Tensor
+                input tensor
+            masks: torch.Tensor
+                post-embedding input lengths
+        """
+        xs_pad = self.encoder_embedding(xs_pad)
+        input_tensor, pos_k, pos_v, hs_mask, masks = self.forward_embeddings(
+            xs_pad, masks)
+
+        unfolded = False
+        ori_bz, seq_len, D = input_tensor.shape
+        max_seq_len = 500  #maximum position for absolute positional encoding
+        if seq_len > max_seq_len:
+            # audio sequence is longer than max_seq_len, unfold it into chunks
+            # of max_seq_len
+            unfolded = True
+            # the unfold op will drop residual frames, pad it to the multiple
+            # of max_seq_len
+            if seq_len % max_seq_len > 0:
+                chunk_pad_size = max_seq_len - (seq_len % max_seq_len)
+            else:
+                chunk_pad_size = 0
+            if chunk_pad_size > 0:
+                input_tensor_pad = F.pad(input_tensor,
+                                         (0, 0, 0, chunk_pad_size), "constant",
+                                         0)
+                input_tensor = input_tensor_pad.to(input_tensor.device)
+            input_tensor = unfold_tensor(input_tensor, max_seq_len)
+            if masks is not None:
+                # revise hs_mask here because the previous calculated hs_mask
+                # did not consider extra pad
+                subsampled_pad_mask = masks.squeeze(
+                    1)  # [bz, subsampled_unmask_seq_len]
+                extra_padded_subsamlped_pad_mask = F.pad(
+                    subsampled_pad_mask, (0, chunk_pad_size), "constant",
+                    False)  # extra padding to the pad mask
+                extra_padded_subsamlped_pad_mask = \
+                    extra_padded_subsamlped_pad_mask.unsqueeze(-1).float()
+                masks_unfold = unfold_tensor(
+                    extra_padded_subsamlped_pad_mask, max_seq_len
+                )  # unfold the pad mask like we did to the input tensor
+                masks_unfold = masks_unfold.squeeze(
+                    -1).bool()  # unfold op does not support bool tensor
+            else:
+                masks_unfold = None
+            hs_mask = self.calculate_hs_mask(
+                input_tensor, input_tensor.device, masks_unfold
+            )  # calculate hs_mask based on the unfolded pad mask
+
+        # layer_emb = None
+
+        relative_attention_bias = self.init_relative_attention_bias(
+            input_tensor)
+
+        _simplified_path = (self.extra_layer_output_idx == -1
+                            and relative_attention_bias is None)
+
+        if _simplified_path:
+            input_tensor, *_ = self.encoders(input_tensor, pos_k, pos_v,
+                                             hs_mask)
+        else:
+            for i, layer in enumerate(self.encoders):
+                input_tensor, _, _, _ = layer(
+                    input_tensor,
+                    pos_k,
+                    pos_v,
+                    hs_mask,
+                    relative_attention_bias=relative_attention_bias,
+                )
+
+                # if i == self.extra_layer_output_idx:
+                #     layer_emb = input_tensor
+
+        if unfolded:
+            embed_dim = input_tensor.shape[-1]
+            input_tensor = input_tensor.reshape(ori_bz, -1, embed_dim)
+            # if we ever padded before unfolding, we need to remove the padding
+            if chunk_pad_size > 0:
+                input_tensor = input_tensor[:, :-chunk_pad_size, :]
+
+        return input_tensor, masks  # , layer_emb
+
+
+class WindowQformer(nn.Module):
+    """Window-level Qformer"""
+
+    def __init__(
+        self,
+        window_size: int = 8,
+        num_queries: int = 1,
+        num_blocks: int = 2,
+        attention_dim: int = 512,
+        attention_heads: int = 8,
+        linear_units: int = 2048,
+        dropout_rate: float = 0.0,
+        normalize_before: bool = True,
+    ):
+        super().__init__()
+
+        self.decoders = nn.ModuleList([
+            nn.TransformerDecoderLayer(
+                d_model=attention_dim,
+                nhead=attention_heads,
+                dim_feedforward=linear_units,
+                dropout=dropout_rate,
+                activation="relu",
+                batch_first=True,
+                norm_first=normalize_before,  # TODO need to verify
+            ) for _ in range(num_blocks)
+        ])
+
+        self.queries = nn.Parameter(torch.zeros(1, num_queries, attention_dim))
+        self.after_norm = (nn.LayerNorm(attention_dim, eps=1e-12)
+                           if normalize_before else None)
+        self.window_size = window_size
+
+    def forward(self, audio_embed, mask, embed_len=None):
+        """forward decoder"""
+        # audio_embed: N x T x D => N x D x T
+
+        audio_embed = audio_embed.transpose(1, 2)
+        # audio_embed: N x D x 1 x T => N x DK x T'
+        padding = audio_embed.shape[-1] % self.window_size
+        if padding > 0:
+            audio_embed = F.pad(audio_embed, (0, self.window_size - padding),
+                                "constant", 0)
+
+        embed_chunk = F.unfold(
+            audio_embed[..., None, :],
+            kernel_size=(1, self.window_size),
+            stride=(1, self.window_size),
+        )
+        bsz, _, slen = embed_chunk.shape
+        # N x D x K x T'
+        embed_chunk = embed_chunk.view(bsz, -1, self.window_size, slen)
+        # N x T' x K x D
+        embed_chunk = embed_chunk.transpose(1, 3).contiguous()
+        # NT' x K x D
+        embed_chunk = embed_chunk.view(bsz * slen, self.window_size, -1)
+        # NT' x 1 x D
+        q = self.queries.expand(bsz * slen, -1, -1)
+        for layer in self.decoders:
+            q = layer(tgt=q,
+                      memory=embed_chunk,
+                      tgt_mask=None,
+                      memory_mask=mask)
+
+        if self.after_norm is not None:
+            q = self.after_norm(q)
+
+        if embed_len is not None:
+            embed_len = embed_len // self.window_size
+        # N x T' x D
+        out = q.view(bsz, slen, -1)
+
+        return out, embed_len
+
+
+class AudioEmbedding(nn.Module):
+    """Image embedding."""
+
+    def __init__(self, config: PretrainedConfig, **kwargs) -> None:
+        super().__init__()
+        self.config = config
+        # n_embed or hidden_size for text LM
+        hidden_size = (config.n_embd
+                       if hasattr(config, "n_embd") else config.hidden_size)
+
+        # self.wte = nn.Embedding(config.vocab_size, hidden_size)
+
+        audio_dim_out = (
+            None  # Set this variable according to the actual audio processor
+        )
+        self.layer_idx = -2
+
+        if (isinstance(config.audio_processor, dict)
+                and config.audio_processor.get("name", None) == "cascades"):
+            encoder_config = config.audio_processor.get("config", None)
+            assert encoder_config is not None
+            self.encoder = ConformerEncoder(**encoder_config)
+
+            audio_dim_out = encoder_config["attention_dim"]
+            n_mels = encoder_config["input_size"]
+        else:
+            raise NotImplementedError("")
+
+        assert (audio_dim_out
+                is not None), "Remember to set values for audio_dim_out"
+        self.audio_dim_out = audio_dim_out
+        self.audio_dim_in = n_mels
+
+        self.freeze_audio_processor = kwargs.get("freeze_audio_processor",
+                                                 False)
+
+        self.downsample_rate = kwargs.get("downsample_rate", 1)
+
+        if kwargs.get("use_qformer", False):
+            qformer_config = kwargs.get("qformer_config", {})
+            qformer_config["attention_dim"] = audio_dim_out
+            self.qformer = WindowQformer(**qformer_config)
+        else:
+            self.qformer = None
+
+        if kwargs.get("use_conv_downsample", False):
+            assert (self.qformer is None
+                    ), "don't support use qformer and conv downsample together"
+            nemo_conv_settings = kwargs.get("nemo_conv_settings", {})
+            default_nemo_conv_settings = {
+                "subsampling": "dw_striding",
+                "subsampling_factor": self.downsample_rate,
+                "feat_in": audio_dim_out,
+                "feat_out": audio_dim_out,
+                "conv_channels": 256,
+                "subsampling_conv_chunking_factor": 1,
+                "activation": nn.ReLU(),
+                "is_causal": False,
+            }
+            # Override any of the defaults with the incoming, user settings
+            if nemo_conv_settings:
+                default_nemo_conv_settings.update(nemo_conv_settings)
+                for i in ["subsampling_factor", "feat_in", "feat_out"]:
+                    assert (
+                        i not in nemo_conv_settings
+                    ), "{i} should be specified outside of the NeMo dictionary"
+
+            self.conv_ds = NemoConvSubsampling(**default_nemo_conv_settings, )
+        else:
+            self.conv_ds = None
+
+        projection_cls = kwargs.get("projection_cls", "linear")
+        if projection_cls == "linear":
+            self.audio_projection = nn.Linear(audio_dim_out, hidden_size)
+        elif projection_cls == "mlp":
+            # follow llava-v1.5's implementation
+            # (do not use image_projection and image_proj_norm)
+            dim_projection = hidden_size
+            depth = 2
+            self.linear_downsample_rate = (1 if (self.qformer or self.conv_ds)
+                                           else self.downsample_rate)
+            layers = [
+                nn.Linear(audio_dim_out * self.linear_downsample_rate,
+                          dim_projection)
+            ]
+            for _ in range(1, depth):
+                layers.extend(
+                    [nn.GELU(),
+                     nn.Linear(dim_projection, dim_projection)])
+            self.audio_projection = nn.Sequential(*layers)
+            # NOTE vision-speech tasks use a separate projection layer
+            layers = [
+                nn.Linear(audio_dim_out * self.linear_downsample_rate,
+                          dim_projection)
+            ]
+            for _ in range(1, depth):
+                layers.extend(
+                    [nn.GELU(),
+                     nn.Linear(dim_projection, dim_projection)])
+            self.audio_projection_for_vision = nn.Sequential(*layers)
+        else:
+            raise NotImplementedError(
+                f"projection_cls = {projection_cls}, not implemented")
+
+        # TODO: audio sequence compression - Qformer
+        self.vocab_size = config.vocab_size
+        self.input_embeds = None
+        self.audio_embed_sizes = None
+
+    def set_audio_embeds(self, input_embeds: torch.FloatTensor) -> None:
+        self.input_embeds = input_embeds
+
+    def set_audio_embed_sizes(self,
+                              audio_embed_sizes: torch.LongTensor) -> None:
+        self.audio_embed_sizes = audio_embed_sizes
+
+    def get_audio_features(
+        self,
+        input_embeds: torch.FloatTensor,
+        audio_attention_mask: torch.Tensor = None,
+        audio_projection_mode: str = "speech",
+    ) -> torch.FloatTensor:
+        """
+        arguments:
+            input_embeds: audio features (B, T, D)  B: num audios in a sequence
+        """
+        if self.freeze_audio_processor:
+            with torch.no_grad():
+                audio_features, masks = self.encoder(input_embeds,
+                                                     audio_attention_mask)
+        else:
+            audio_features, masks = self.encoder(input_embeds,
+                                                 audio_attention_mask)
+
+        if self.qformer is not None:
+            audio_features, _ = self.qformer(audio_features, mask=None)
+
+        if self.conv_ds is not None:
+            if masks is not None:
+                masks = masks.squeeze(1)
+
+            audio_features, masks = self.conv_ds(audio_features, mask=masks)
+
+        if self.linear_downsample_rate != 1:
+            bs, seq_len, feat_dim = audio_features.size()
+            padding = seq_len % self.linear_downsample_rate
+            if padding > 0:
+                audio_features = F.pad(
+                    audio_features,
+                    (0, 0, 0, self.linear_downsample_rate - padding),
+                    "constant",
+                    0,
+                )
+
+            seq_len = audio_features.size(1)
+            audio_features = audio_features.view(
+                bs,
+                seq_len // self.linear_downsample_rate,
+                feat_dim * self.linear_downsample_rate,
+            )
+
+        if audio_projection_mode == 'speech':
+            audio_set_tensor = self.audio_projection(audio_features)
+        elif audio_projection_mode == 'vision':
+            audio_set_tensor = self.audio_projection_for_vision(audio_features)
+        else:
+            raise ValueError(
+                f"audio_projection_mode = {audio_projection_mode} not "\
+                    "implemented"
+            )
+
+        return audio_set_tensor
+
+    def forward(
+        self,
+        audio_features: torch.FloatTensor,
+        audio_attention_mask: torch.Tensor = None,
+        audio_projection_mode: str = "speech",
+    ) -> torch.FloatTensor:
+        """
+        arguments:
+            audio_features: audio features (T, D)
+        
+        returns:
+            audio_embeds: audio embeddings (num_audio_tokens, hidden_dim)
+        """
+        audio_embeds = self.get_audio_features(
+            audio_features.unsqueeze(0),
+            audio_attention_mask=audio_attention_mask,
+            audio_projection_mode=audio_projection_mode,
+        )
+        return audio_embeds.squeeze(0)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi4mm_utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi4mm_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4890d8427e2a2df07c72c324ec9291b2d58e520
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phi4mm_utils.py
@@ -0,0 +1,1884 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+# Code copied from Microsoft/MoE by Jacob Platin (jacobplatin@microsoft.com)
+# but implemented by the Phi-Speech team
+#!/usr/bin/env python3
+import math
+from typing import Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+
+class BlockBase(nn.Module):
+    """Block abstract module"""
+
+    def __init__(self, input_size, output_size):
+        super().__init__()
+        self.input_size = input_size
+        self.output_size = output_size
+
+
+def get_activation(name="relu"):
+    """Select an activation function by name
+
+    Args:
+        name: str
+            activation function name,
+            one of ["relu", "gelu", "swish", "sigmoid"],
+            default "relu".
+    """
+    name = name.lower()
+    if name == "relu":
+        return nn.ReLU(inplace=True)
+    if name == "gelu":
+        return nn.GELU()
+    if name == "swish":
+        return Swish()
+    if name == "sigmoid":
+        return torch.nn.Sigmoid()
+    return nn.Identity()
+
+
+def adaptive_enc_mask(x_len, chunk_start_idx, left_window=0, right_window=0):
+    """
+    The function is very important for Transformer Transducer Streaming mode
+    Args:
+        xs_len (int): sequence length
+        chunk_start_idx (list): first idx of each chunk, such as [0,18,36,48]. 
+        It also supports adaptive chunk size [0,10,15,45]
+        left_window (int): how many left chunks can be seen
+        right_window (int): how many right chunks can be seen. It is used for 
+        chunk overlap model.
+        Returns:
+            mask (torch.Tensor): a mask tensor for streaming model
+            Torch 1.0.1
+            tensor([[1., 1., 0., 0.],
+                    [0., 1., 1., 0.],
+                    [0., 0., 1., 1.]])
+            Torch 1.4.1
+            tensor([[True., True., False., False.],
+                    [False., True., True., False.],
+                    [False., False., True., True.]])
+    """
+    chunk_start_idx = torch.Tensor(chunk_start_idx).long(
+    )  # first idx of each chunk, such as [0,18,36,48].
+    start_pad = torch.nn.functional.pad(
+        chunk_start_idx,
+        (1, 0))  # append 0 to the beginning, so it becomes [0, 0, 18, 36, 48]
+    end_pad = torch.nn.functional.pad(
+        chunk_start_idx, (0, 1), value=x_len
+    )  # append x_len to the end, so it becomes [0,18,36,48, x_len]
+    seq_range = torch.arange(0,
+                             x_len).unsqueeze(-1)  # seq_range size: [x_len, 1]
+    idx = ((seq_range < end_pad) &
+           (seq_range >= start_pad)).nonzero()[:, 1]  # idx size: [x_len]
+    # boundary = end_pad[idx]  # boundary size: [x_len]
+    seq_range_expand = (torch.arange(0, x_len).unsqueeze(0).expand(x_len, -1)
+                        )  # seq_range_expand size [x_len, x_len]
+    idx_left = idx - left_window
+    idx_left[idx_left < 0] = 0
+    boundary_left = start_pad[idx_left]
+    mask_left = seq_range_expand >= boundary_left.unsqueeze(-1)
+    idx_right = idx + right_window
+    idx_right[idx_right > len(chunk_start_idx)] = len(chunk_start_idx)
+    boundary_right = end_pad[idx_right]
+    mask_right = seq_range_expand < boundary_right.unsqueeze(-1)
+    return mask_left & mask_right
+
+
+class Swish(nn.Module):
+    """Implement Swish activation module.
+    From https://arxiv.org/pdf/2005.03191.pdf
+
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.act_fn = nn.Sigmoid()
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Apply Swish function
+
+        Args:
+            x: torch.Tensor
+                Input.
+        """
+        return x * self.act_fn(x)
+
+
+class GLU(nn.Module):
+    """Implement Gated Linear Unit (GLU) module"""
+
+    def __init__(self, dim: int = -1, act_name: str = "sigmoid") -> None:
+        super().__init__()
+        self.dim = dim
+        self.act_name = act_name.lower()
+
+        if self.act_name == "relu":
+            self.act_fn = nn.ReLU(inplace=True)
+        elif self.act_name == "gelu":
+            self.act_fn = nn.GELU()
+        elif self.act_name == "swish":
+            self.act_fn = Swish()
+        elif self.act_name == "sigmoid":
+            self.act_fn = nn.Sigmoid()
+        else:
+            self.act_fn = nn.Identity()
+
+    def forward(self, x: Tensor) -> Tensor:
+        """GLU forward
+        Apply Swish function on the first half of input matrices
+        with sigmoid of the second half.
+
+        Args:
+            x: torch.Tensor
+                Input.
+
+        """
+        half_x, gate = x.chunk(2, dim=self.dim)
+        return half_x * self.act_fn(gate)
+
+
+# TODO: Abdel, this can be improved using GLU module
+class GLUPointWiseConv(nn.Module):
+    """GLUPointWiseConv module
+    used for conformer architecture,
+    for more details see:
+    https://arxiv.org/pdf/2005.08100v1.pdf
+
+    Args:
+        input_dim: int
+            input channel size.
+        output_dim: int
+            output channel size.
+        kernel_size: int
+            kernel size
+        glu_type: str, optional
+            activation function one of
+             ["sigmoid", "relu", "gelu"]
+              default "sigmoid".
+        bias_in_glu: bool, optional
+            use addtive bias in glu
+        causal: bool, optional
+            if set to True, padding is set to the half of
+             kernel size, ie, convolution can't see future frames.
+              default False.
+
+    """
+
+    def __init__(
+        self,
+        input_dim,
+        output_dim,
+        kernel_size,
+        glu_type="sigmoid",
+        bias_in_glu=True,
+        causal=False,
+    ):
+        super().__init__()
+
+        self.glu_type = glu_type
+        self.output_dim = output_dim
+        self.bias_in_glu = bias_in_glu
+        if causal:
+            self.ext_pw_conv_1d = nn.Conv1d(
+                input_dim,
+                output_dim * 2,
+                kernel_size,
+                1,
+                padding=(kernel_size - 1),
+            )
+        else:
+            self.ext_pw_conv_1d = nn.Conv1d(
+                input_dim,
+                output_dim * 2,
+                kernel_size,
+                1,
+                padding=(kernel_size - 1) // 2,
+            )
+
+        if glu_type == "sigmoid":
+            self.glu_act = nn.Sigmoid()
+        elif glu_type == "relu":
+            self.glu_act = nn.ReLU()
+        elif glu_type == "gelu":
+            self.glu_act = nn.GELU()
+        elif glu_type == "swish":
+            self.glu_act = Swish()
+        else:
+            raise ValueError(f"Unsupported activation type {self.glu_act}")
+
+        if bias_in_glu:
+            self.b1 = nn.Parameter(torch.zeros(1, output_dim, 1))
+            self.b2 = nn.Parameter(torch.zeros(1, output_dim, 1))
+
+    def forward(self, x):
+        """
+        Args:
+            x: torch.Tensor
+                input tensor
+        """
+        # to be consistent with GLULinear, we assume the input always has the
+        # #channel (#dim) in the last dimension of the tensor, so need to
+        # switch the dimension first for 1D-Conv case
+        x = x.permute([0, 2, 1])
+        x = self.ext_pw_conv_1d(x)
+        if self.glu_type == "bilinear":
+            if self.bias_in_glu:
+                x = (x[:, 0:self.output_dim, :] + self.b1) * (
+                    x[:, self.output_dim:self.output_dim * 2, :] + self.b2)
+            else:
+                x = (x[:, 0:self.output_dim, :]) * (
+                    x[:, self.output_dim:self.output_dim * 2, :])
+        else:
+            if self.bias_in_glu:
+                x = (x[:, 0:self.output_dim, :] + self.b1) * self.glu_act(
+                    x[:, self.output_dim:self.output_dim * 2, :] + self.b2)
+            else:
+                x = (x[:, 0:self.output_dim, :]) * self.glu_act(
+                    x[:, self.output_dim:self.output_dim * 2, :])
+
+        x = x.permute([0, 2, 1])
+        return x
+
+
+class DepthWiseSeperableConv1d(nn.Module):
+    """DepthWiseSeperableConv1d module used in Convnet module
+    for the conformer, for more details see:
+    https://arxiv.org/pdf/2005.08100v1.pdf
+
+    Args:
+        input_dim: int
+            input channel size.
+        depthwise_seperable_out_channel: int
+            if set different to 0, the number of 
+             depthwise_seperable_out_channel will be used as a channel_out
+             of the second conv1d layer.
+             otherwise, it equal to 0, the second conv1d layer is skipped.
+        kernel_size: int
+            kernel_size
+        depthwise_multiplier: int
+            number of input_dim channels duplication. this value
+            will be used to compute the hidden channels of the Conv1D.
+        padding: int, optional
+            padding for the conv1d,
+             default: 0.
+
+    """
+
+    def __init__(
+        self,
+        input_dim,
+        depthwise_seperable_out_channel,
+        kernel_size,
+        depthwise_multiplier,
+        padding=0,
+    ):
+        super().__init__()
+
+        self.dw_conv = nn.Conv1d(
+            input_dim,
+            input_dim * depthwise_multiplier,
+            kernel_size,
+            1,
+            padding=padding,
+            groups=input_dim,
+        )
+
+        if depthwise_seperable_out_channel != 0:
+            self.pw_conv = nn.Conv1d(
+                input_dim * depthwise_multiplier,
+                depthwise_seperable_out_channel,
+                1,
+                1,
+                0,
+            )
+        else:
+            self.pw_conv = nn.Identity()
+        self.depthwise_seperable_out_channel = depthwise_seperable_out_channel
+
+    def forward(self, x):
+        """
+
+        Args:
+            x: torch.Tensor
+                input tensor
+        """
+        x = self.dw_conv(x)
+        if self.depthwise_seperable_out_channel != 0:
+            x = self.pw_conv(x)
+        return x
+
+
+class ConvModule(nn.Module):
+    """ConvModule Module for the conformer block.
+    for more details see:
+    https://arxiv.org/pdf/2005.08100v1.pdf
+
+    Args:
+        input_dim: int
+            input channel size.
+        ext_pw_out_channel: int
+            if > 0, ext_pw_out_channel is a dim channel size
+             for the last pointwise conv after swish activation.
+        depthwise_seperable_out_channel: int
+            if set different to 0, the number of 
+             depthwise_seperable_out_channel
+             will be used as a channel_out of the second conv1d layer.
+             otherwise, it equal to 0, the second conv1d layer is skipped.
+        ext_pw_kernel_size: int
+            kernel size of the conv pointwise of the conformer.
+        kernel_size: int
+            kernel size.
+        depthwise_multiplier: int
+            number of input_dim channels duplication. this value
+             will be used to compute the hidden channels of the Conv1D.
+        dropout_rate: float
+            dropout rate.
+        causal: bool, optional
+            if set to True, convolution have no access
+             to future frames. default False.
+        batch_norm: bool, optional
+            if set to True, apply batchnorm before activation.
+            default False
+        chunk_se: int, optional
+            0 for offline SE.
+            1 for streaming SE, where mean is computed
+             by accumulated history until current chunk_se.
+            2 for streaming SE, where mean is computed
+             by only the current chunk.
+        chunk_size: int, optional
+            chunk size for cnn. default 18
+        activation: str, optional
+            activation function used in ConvModule,
+            default: "relu".
+        glu_type: str, optional
+            activation function used for the glu,
+            default: "sigmoid".
+        bias_in_glu: bool, optional
+            if set to True, use additive bias in the weight module
+             before GLU.
+        linear_glu_in_convm: bool, optional
+            if set to True, use GLULinear module,
+             otherwise, used GLUPointWiseConv module.
+              default to False.
+        export: bool, optional,
+            if set to True, padding is equal to 0.  This is for inference,
+             or onnx export.  Typically this is set by the export program or
+             the decoder program, and it isn't present in your config file.
+             default False
+    """
+
+    def __init__(
+        self,
+        input_dim,
+        ext_pw_out_channel,
+        depthwise_seperable_out_channel,
+        ext_pw_kernel_size,
+        kernel_size,
+        depthwise_multiplier,
+        dropout_rate,
+        causal=False,
+        batch_norm=False,
+        chunk_se=0,
+        chunk_size=18,
+        activation="relu",
+        glu_type="sigmoid",
+        bias_in_glu=True,
+        linear_glu_in_convm=False,
+        export=False,
+    ):
+        super().__init__()
+        self.layer_norm = nn.LayerNorm(input_dim)
+        self.input_dim = input_dim
+        self.ext_pw_out_channel = ext_pw_out_channel
+        self.ext_pw_kernel_size = ext_pw_kernel_size
+        self.depthwise_seperable_out_channel = depthwise_seperable_out_channel
+        self.glu_type = glu_type
+        self.bias_in_glu = bias_in_glu
+        self.linear_glu_in_convm = linear_glu_in_convm
+        self.causal = causal
+
+        self._add_ext_pw_layer()
+
+        self.batch_norm = batch_norm
+        self.kernel_size = kernel_size
+
+        if batch_norm:
+            self.bn_layer = nn.BatchNorm1d(input_dim)
+
+        self.act = get_activation(activation)
+        self.dropout = nn.Dropout(dropout_rate)
+        self.export = export
+
+        if causal:
+            padding = 0 if export else kernel_size - 1
+        else:
+            padding = (kernel_size - 1) // 2
+
+        self.dw_sep_conv_1d = DepthWiseSeperableConv1d(
+            input_dim,
+            depthwise_seperable_out_channel,
+            kernel_size,
+            depthwise_multiplier,
+            padding=padding,
+        )
+
+        if depthwise_seperable_out_channel != 0:
+            if input_dim != depthwise_seperable_out_channel:
+                self.ln2 = nn.Linear(depthwise_seperable_out_channel,
+                                     input_dim)
+        else:
+            if depthwise_multiplier != 1:
+                self.ln2 = nn.Linear(input_dim * depthwise_multiplier,
+                                     input_dim)
+
+    def _add_ext_pw_layer(self):
+        """
+        This function is an extension of __init__ function
+        and dedicated to the convolution module creation
+        of the conformer.
+        """
+        self.ln1 = self.glu = self.bn_layer = self.ext_pw_conv_1d = (
+            nn.Identity())  # jit hacks.
+        self.squeeze_excitation = nn.Identity()  # jit.
+        self.apply_ln1 = self.fix_len1 = False  # jit.
+
+        if self.ext_pw_out_channel != 0:
+            if self.causal:
+                self.ext_pw_conv_1d = nn.Conv1d(
+                    self.input_dim,
+                    self.ext_pw_out_channel,
+                    self.ext_pw_kernel_size,
+                    1,
+                    padding=(self.ext_pw_kernel_size - 1),
+                )
+                if self.ext_pw_kernel_size > 1:
+                    self.fix_len1 = True
+                else:
+                    self.fix_len1 = False
+            else:
+                self.ext_pw_conv_1d = nn.Conv1d(
+                    self.input_dim,
+                    self.ext_pw_out_channel,
+                    self.ext_pw_kernel_size,
+                    1,
+                    padding=(self.ext_pw_kernel_size - 1) // 2,
+                )
+                self.fix_len1 = False
+
+            if self.linear_glu_in_convm:
+                self.glu = GLULinear(
+                    self.input_dim,
+                    self.ext_pw_out_channel,
+                    self.glu_type,
+                    self.bias_in_glu,
+                )
+            else:
+                self.glu = GLUPointWiseConv(
+                    self.input_dim,
+                    self.ext_pw_out_channel,
+                    self.ext_pw_kernel_size,
+                    self.glu_type,
+                    self.bias_in_glu,
+                    self.causal,
+                )
+
+            if self.input_dim != self.ext_pw_out_channel:
+                self.apply_ln1 = True
+                self.ln1 = nn.Linear(self.ext_pw_out_channel, self.input_dim)
+            else:
+                self.apply_ln1 = False
+        else:
+            self.pw_conv_simplify_w = torch.nn.Parameter(torch.ones(3))
+            self.pw_conv_simplify_b = torch.nn.Parameter(torch.zeros(3))
+
+    def forward(self, x):
+        """ConvModule Forward.
+
+        Args:
+            x: torch.Tensor
+                input tensor.
+        """
+        x = self.layer_norm(x)
+
+        if self.ext_pw_out_channel != 0:
+            x = self.glu(x)
+            if self.causal and self.ext_pw_kernel_size > 1:
+                x = x[:, :-(self.ext_pw_kernel_size - 1), :]
+            if self.apply_ln1:
+                x = self.ln1(x)
+        else:
+            x_0 = x * self.pw_conv_simplify_w[0] + self.pw_conv_simplify_b[0]
+            x_1 = x * self.pw_conv_simplify_w[1] + self.pw_conv_simplify_b[1]
+            x = x_0 + x_1
+
+        x = x.permute([0, 2, 1])
+
+        x = self.dw_sep_conv_1d(x)
+        if self.causal and self.kernel_size > 1:
+            x = x[:, :, :-(self.kernel_size - 1)]
+        if hasattr(self, "ln2"):
+            x = x.permute([0, 2, 1])
+            x = self.ln2(x)
+            x = x.permute([0, 2, 1])
+        if self.batch_norm:
+            x = self.bn_layer(x)
+        x = self.act(x)
+
+        if self.ext_pw_out_channel != 0:
+            x = self.ext_pw_conv_1d(x)
+            if self.fix_len1:
+                x = x[:, :, :-(self.ext_pw_kernel_size - 1)]
+
+            if self.apply_ln1:
+                x = x.permute([0, 2, 1])
+                x = self.ln1(x)
+                x = x.permute([0, 2, 1])
+
+            x = x.permute([0, 2, 1])
+        else:
+            x = x.unsqueeze(1).permute([0, 1, 3, 2])
+            x = x * self.pw_conv_simplify_w[2] + self.pw_conv_simplify_b[2]
+            x = x.squeeze(1)
+
+        x = self.dropout(x)
+        return x
+
+
+class GLULinear(nn.Module):
+    """Linear + GLU module
+
+    Args:
+        input_dim: int
+            input size
+        output_dim: int
+            output size.
+        glu_type:
+            activation function name used in glu module.
+            default "sigmoid" (swish function).
+        bias_in_glu: bool, optional
+            If True, the addtive bias is added. Default False.
+    """
+
+    def __init__(
+        self,
+        input_dim,
+        output_dim,
+        glu_type="sigmoid",
+        bias_in_glu=True,
+    ):
+        super().__init__()
+        self.linear = nn.Linear(input_dim, output_dim * 2, bias_in_glu)
+        self.glu_act = GLU(-1, glu_type)
+
+    def forward(self, x):
+        """GLULinear forward
+
+        Args:
+            x: torch.Tensor
+                inpute tensor.
+        """
+        x = self.linear(x)
+        return self.glu_act(x)
+
+
+class FeedForward(nn.Module):
+    """FeedForward Module.
+    For more details see Conformer paper:
+        https://arxiv.org/pdf/2005.08100.pdf
+
+    Args:
+        d_model: int
+            input size.
+        d_inner: int
+            output size.
+        dropout_rate: float,
+            dropout rate.
+        activation: str,
+            activation function name,
+            one of ["relu", "swish", "sigmoid"],
+            sigmoid activation is only used with "glu_in_fnn=True",
+            default "sigmoid".
+        bias_in_glu: bool, optional
+    """
+
+    def __init__(
+        self,
+        d_model,
+        d_inner,
+        dropout_rate,
+        activation="sigmoid",
+        bias_in_glu=True,
+    ):
+        super().__init__()
+        self.d_model = d_model
+        self.d_inner = d_inner
+
+        self.layer_norm = nn.LayerNorm(d_model)
+        module = GLULinear(d_model, d_inner, activation, bias_in_glu)
+        self.net = nn.Sequential(
+            module,
+            nn.Dropout(dropout_rate),
+            nn.Linear(d_inner, d_model),
+            nn.Dropout(dropout_rate),
+        )
+
+    def forward(self, x):
+        """FeedForward forward function.
+
+        Args:
+            x: torch.Tensor
+                input tensor.
+        """
+        out = self.net(self.layer_norm(x))
+
+        return out
+
+
+#### positional encoding starts here
+def _pre_hook(
+    state_dict,
+    prefix,
+    local_metadata,
+    strict,
+    missing_keys,
+    unexpected_keys,
+    error_msgs,
+):
+    """Perform pre-hook in load_state_dict for backward compatibility.
+
+    Note:
+        We saved self.pe until v.0.5.2 but we have omitted it later.
+        Therefore, we remove the item "pe" from `state_dict` for backward 
+        compatibility.
+
+    """
+    k = prefix + "pe"
+    if k in state_dict:
+        state_dict.pop(k)
+
+
+class T5RelativeAttentionLogitBias(nn.Module):
+    """
+    This module implements the relative position bias described in Section 
+    2.1 of the T5 paper: https://arxiv.org/pdf/1910.10683.pdf
+
+    The Huggingface implementation is used as a reference
+    https://github.com/huggingface/transformers/blob/v4.30.0/src/
+    transformers/models/t5/modeling_t5.py#L435
+
+    Modifies attention as Q*K^T + B, where B is a learned scalar bias based
+    on relative position of the query and key. It is HxNxN, where H is the 
+    number of heads, N is the sequence length.
+
+    I've made these modifications to the original T5 bias:
+    - Skipping of the bucketing step. Original T5 bias converted rel 
+      position distances into logarithmically increasing buckets. This is 
+      supposed to help with length generalization.
+    - I just directly use rel position index as bias values, as we don't 
+      need length generalization (40s max is good enough for ASR encoder), 
+      and it keeps ONNX export simple.
+    - I've also extended it so that biases can be asymmetric, the default 
+      implementation treats L->R and R->L the same. Asymmetric was found to 
+      yield better results in my experiments.
+
+    Args:
+        num_heads: int
+            Number of attention heads
+        num_buckets: int
+            Number of buckets to use for relative attention bias. This is the
+            size of the learnable bias parameter. Bucketing is not yet 
+            supported, so this defaults to -1 which means no bucketing is
+            used (max_distance determines size of bias param).
+        max_distance: int
+            Maximum distance to use for relative attention bias. With 
+            num_buckets=-1, this directly controls the max size of the bias 
+            parameter. When num_buckets > 0 is supported, this will control 
+            the maximum distance for logarithmic bucketing after which all 
+            positions are in the same bucket.
+        symmetric: bool
+            Whether to use symmetric or asymmetric biases. symmetric=False uses
+            2x number of bias params to distinguish L->R from R->L. This was 
+            found to be better for the encoder.
+    """
+
+    def __init__(self,
+                 num_heads,
+                 num_buckets=-1,
+                 max_distance=1000,
+                 symmetric=False):
+        super().__init__()
+        self.num_heads = num_heads
+        self.num_buckets = num_buckets
+        self.max_distance = max_distance
+        self.symmetric = symmetric
+        self._skip_bucketing = self.num_buckets < 0
+        if self._skip_bucketing:
+            self.num_buckets = max_distance
+        else:
+            raise NotImplementedError(
+                "T5 attention bias with bucketed positions is not yet tested")
+        if not self.symmetric:
+            self.num_buckets *= 2
+        self.bias_values = nn.Embedding(self.num_buckets, self.num_heads)
+
+    def forward(self, x):
+        # instantiate bias compatible with shape of x
+        maxpos = x.size(1)
+        context_position = torch.arange(maxpos,
+                                        device=x.device,
+                                        dtype=torch.long)[:, None]
+        memory_position = torch.arange(maxpos,
+                                       device=x.device,
+                                       dtype=torch.long)[None, :]
+        relative_position = memory_position - context_position
+        # clipping to a maximum distance using ops that play well with ONNX
+        # export
+        relative_position = relative_position.masked_fill(
+            relative_position < -self.max_distance, -self.max_distance)
+        relative_position = relative_position.masked_fill(
+            relative_position > self.max_distance - 1, self.max_distance - 1)
+
+        # mapping from relative position to index in the bias parameter
+        if self._skip_bucketing:
+            bias_idx = relative_position
+        else:
+            bias_idx = self._bucket_relative_position(relative_position)
+        if self.symmetric:
+            bias_idx = bias_idx.abs()
+        else:
+            bias_idx += self.num_buckets // 2
+
+        t5_rel_att_bias = self.bias_values(bias_idx)  # [L, L, H]
+        t5_rel_att_bias = t5_rel_att_bias.permute(2, 0, 1).unsqueeze(
+            0)  # [1, H, L, L]
+
+        return t5_rel_att_bias
+
+    def _bucket_relative_position(self, relative_position):
+        # this is a placeholder (isn't tested, likely buggy) using HuggingFace
+        # implem as a reference this also needs to be extended to support
+        # asymmetric +/- ve positions
+        relative_buckets = 0
+        if not self.causal:
+            self.num_buckets //= 2
+            relative_buckets += (relative_position > 0).to(
+                torch.long) * self.num_buckets
+            relative_position = torch.abs(relative_position)
+        else:
+            relative_position = -torch.min(relative_position,
+                                           torch.zeros_like(relative_position))
+        # now relative_position is in the range [0, inf)
+
+        # half of the buckets are for exact increments in positions
+        max_exact = self.num_buckets // 2
+        is_small = relative_position < max_exact
+
+        # The other half of the buckets are for logarithmically bigger bins in
+        # positions up to max_distance
+        relative_position_if_large = max_exact + (
+            torch.log(relative_position.float() / max_exact) /
+            math.log(self.max_distance / max_exact) *
+            (self.num_buckets - max_exact)).to(torch.long)
+        relative_position_if_large = torch.min(
+            relative_position_if_large,
+            torch.full_like(relative_position_if_large, self.num_buckets - 1),
+        )
+
+        relative_buckets += torch.where(is_small, relative_position,
+                                        relative_position_if_large)
+        return relative_buckets
+
+
+class AbsolutePositionalEncoding(nn.Module):
+    """Absolute Positional encoding module.
+    This module implement Absolute sinusoidal positional encoding
+    from: https://arxiv.org/pdf/1706.03762.pdf
+
+    Args:
+        d_model: int
+            Input embedding size.
+        dropout_rate: float
+            dropout rate
+        max_len: int, optional
+            Maximum input length sequence, Default 5000
+
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000):
+        """Construct an PositionalEncoding object."""
+        super().__init__()
+        self.d_model = d_model
+        self.xscale = math.sqrt(self.d_model)
+        self.dropout = torch.nn.Dropout(p=dropout_rate)
+        self.pe = None
+        self.extend_pe(torch.tensor(0.0).expand(1, max_len))
+        self._register_load_state_dict_pre_hook(_pre_hook)
+
+    def extend_pe(self, x):
+        """Reset the positional encodings.
+
+        Args:
+            x: torch.Tensor
+        """
+        if self.pe is not None and self.pe.size(1) >= x.size(1):
+            if self.pe.dtype != x.dtype or self.pe.device != x.device:
+                self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+            return
+        pe = torch.zeros(x.size(1), self.d_model)
+        position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, self.d_model, 2, dtype=torch.float32) *
+            -(math.log(10000.0) / self.d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.pe = pe.to(device=x.device, dtype=x.dtype)
+
+    def forward(self, x: torch.Tensor):
+        """Add positional encoding.
+
+        Args:
+            x: torch.Tensor
+                Input tensor. shape is (batch, time, ...)
+
+        Returns:
+            torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
+
+        """
+        self.extend_pe(x)
+        x = x * self.xscale + self.pe[:, :x.size(1)]
+        return self.dropout(x)
+
+
+#### forward embedding layers starts here
+class MeanVarianceNormLayer(nn.Module):
+    """Mean/variance normalization layer.
+
+    Will subtract mean and multiply input by inverted standard deviation.
+    Typically used as a very first layer in a model.
+
+    Args:
+        input_size: int
+            layer input size.
+    """
+
+    def __init__(self, input_size):
+        super().__init__()
+        self.input_size = input_size
+        self.global_mean = nn.Parameter(torch.zeros(input_size))
+        self.global_invstd = nn.Parameter(torch.ones(input_size))
+
+    def forward(self, input_: Tensor) -> Tensor:
+        """MeanVarianceNormLayer Forward
+
+        Args:
+            input_: torch.Tensor
+                input tensor.
+        """
+        return (input_ - self.global_mean) * self.global_invstd
+
+
+class CausalConv1D(nn.Conv1d):
+    """
+    A causal version of nn.Conv1d where each step would have limited access to
+    locations on its right or left
+    All arguments are the same as nn.Conv1d except padding.
+
+    If padding is set None, then paddings are set automatically to make it a 
+    causal convolution where each location would not see any steps on its right.
+
+    If padding is set as a list (size of 2), then padding[0] would be used as 
+    left padding and padding[1] as right padding.
+    It would make it possible to control the number of steps to be accessible
+    on the right and left.
+    This mode is not supported when stride > 1. padding[0]+padding[1] should 
+    be equal to (kernel_size - 1).
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: Union[str, int] = 0,
+        dilation: int = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = "zeros",
+        device=None,
+        dtype=None,
+    ) -> None:
+        self.cache_drop_size = None
+        if padding is None:
+            self._left_padding = kernel_size - 1
+            self._right_padding = stride - 1
+        else:
+            if stride != 1 and padding != kernel_size - 1:
+                raise ValueError(
+                    "No striding allowed for non-symmetric convolutions!")
+            if isinstance(padding, int):
+                self._left_padding = padding
+                self._right_padding = padding
+            elif (isinstance(padding, list) and len(padding) == 2
+                  and padding[0] + padding[1] == kernel_size - 1):
+                self._left_padding = padding[0]
+                self._right_padding = padding[1]
+            else:
+                raise ValueError(f"Invalid padding param: {padding}!")
+
+        self._max_cache_len = self._left_padding
+
+        super().__init__(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=0,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+
+    def update_cache(self, x, cache=None):
+        if cache is None:
+            new_x = F.pad(x, pad=(self._left_padding, self._right_padding))
+            next_cache = cache
+        else:
+            new_x = F.pad(x, pad=(0, self._right_padding))
+            new_x = torch.cat([cache, new_x], dim=-1)
+            if self.cache_drop_size > 0:
+                next_cache = new_x[:, :, :-self.cache_drop_size]
+            else:
+                next_cache = new_x
+            next_cache = next_cache[:, :, -cache.size(-1):]
+        return new_x, next_cache
+
+    def forward(self, x, cache=None):
+        x, cache = self.update_cache(x, cache=cache)
+        x = super().forward(x)
+        if cache is None:
+            return x
+        else:
+            return x, cache
+
+
+class CausalConv2D(nn.Conv2d):
+    """
+    A causal version of nn.Conv2d where each location in the 2D matrix would
+    have no access to locations on its right or down
+    All arguments are the same as nn.Conv2d except padding which should be 
+    set as None
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: Union[str, int] = 0,
+        dilation: int = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = "zeros",
+        device=None,
+        dtype=None,
+    ) -> None:
+        if padding is not None:
+            raise ValueError(
+                "Argument padding should be set to None for CausalConv2D.")
+        self._left_padding = kernel_size - 1
+        self._right_padding = stride - 1
+
+        padding = 0
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            device,
+            dtype,
+        )
+
+    def forward(
+        self,
+        x,
+    ):
+        x = F.pad(
+            x,
+            pad=(self._left_padding, self._right_padding, 0, 0),
+        )
+        x = super().forward(x)
+        return x
+
+
+class NemoConvSubsampling(torch.nn.Module):
+    """Convlutional subsampling module, taken from NeMo ASR
+    (https://github.com/NVIDIA/NeMo/blob/b367413645d5c72db3c2c96e46e95a
+    34501479cf/nemo/collections/asr/parts/submodules/subsampling.py)
+
+    Striding Subsampling: "Speech-Transformer: A No-Recurrence 
+    Sequence-to-Sequence Model for Speech Recognition" by Linhao Dong 
+    et al. (https://ieeexplore.ieee.org/document/8462506)
+
+
+    Compared with the EncoderConv2D (`input_layer: custom`), this is a 
+    much simplified approach, and uses no LayerNorm and far fewer Conv2Ds.
+    Moreover, depthwise convolutions are used to reduce FLOPs, but the first
+      layer is kept as a regular convolution so as not to degrade accuracy.
+
+    `Striding` and `dw_striding` are the same except that the latter uses 
+    depthwise convolutions after the first layer, whereas the former does not.
+
+    Args:
+        subsampling_factor (int): Time reduction factor
+        feat_in (int): size of the input features
+        feat_out (int): size of the output features
+        subsampling (str): The subsampling technique, choose from
+            {"striding", "dw-striding", "striding_conv1d", 
+            "dw_striding_conv1d"}
+        conv_channels (int): Number of channels for the convolution layers, 
+                            default is 256.
+        subsampling_conv_chunking_factor (int): Input chunking factor which 
+            can be -1 (no chunking) 1 (auto) or a power of 2. Default is 1
+        activation (Module): activation function, default is nn.ReLU()
+        is_causal (bool): whether to use causal Conv1/2D, where each step will
+            have limited access to locations on its right or left
+    """
+
+    def __init__(
+            self,
+            feat_in,
+            feat_out,
+            subsampling_factor=4,
+            subsampling="dw_striding",
+            conv_channels=256,
+            subsampling_conv_chunking_factor=1,
+            activation=nn.ReLU(),  # noqa: B008
+            is_causal=False,
+    ):
+        super().__init__()
+        self._subsampling = subsampling
+        self._conv_channels = conv_channels
+        self._feat_in = feat_in
+        self._feat_out = feat_out
+
+        if subsampling_factor % 2 != 0:
+            raise ValueError("Sampling factor should be a multiply of 2!")
+        self._sampling_num = int(math.log(subsampling_factor, 2))
+        self.subsampling_factor = subsampling_factor
+        self.is_causal = is_causal
+        self.subsampling_causal_cond = subsampling in (
+            "dw_striding",
+            "striding",
+            "striding_conv1d",
+        )
+
+        if (subsampling_conv_chunking_factor != -1
+                and subsampling_conv_chunking_factor != 1
+                and subsampling_conv_chunking_factor % 2 != 0):
+            raise ValueError(
+                "subsampling_conv_chunking_factor should be -1, 1, or a "\
+                    "power of 2"
+            )
+        self.subsampling_conv_chunking_factor = \
+            subsampling_conv_chunking_factor
+
+        in_channels = 1
+        layers = []
+
+        if subsampling == "dw_striding":
+            self._stride = 2
+            self._kernel_size = 3
+            self._ceil_mode = False
+
+            if self.is_causal:
+                self._left_padding = self._kernel_size - 1
+                self._right_padding = self._stride - 1
+                self._max_cache_len = subsampling_factor + 1
+            else:
+                self._left_padding = (self._kernel_size - 1) // 2
+                self._right_padding = (self._kernel_size - 1) // 2
+                self._max_cache_len = 0
+
+            # Layer 1
+            if self.is_causal:
+                layers.append(
+                    CausalConv2D(
+                        in_channels=in_channels,
+                        out_channels=conv_channels,
+                        kernel_size=self._kernel_size,
+                        stride=self._stride,
+                        padding=None,
+                    ))
+            else:
+                layers.append(
+                    torch.nn.Conv2d(
+                        in_channels=in_channels,
+                        out_channels=conv_channels,
+                        kernel_size=self._kernel_size,
+                        stride=self._stride,
+                        padding=self._left_padding,
+                    ))
+            in_channels = conv_channels
+            layers.append(activation)
+
+            for i in range(self._sampling_num - 1):
+                if self.is_causal:
+                    layers.append(
+                        CausalConv2D(
+                            in_channels=in_channels,
+                            out_channels=in_channels,
+                            kernel_size=self._kernel_size,
+                            stride=self._stride,
+                            padding=None,
+                            groups=in_channels,
+                        ))
+                else:
+                    layers.append(
+                        torch.nn.Conv2d(
+                            in_channels=in_channels,
+                            out_channels=in_channels,
+                            kernel_size=self._kernel_size,
+                            stride=self._stride,
+                            padding=self._left_padding,
+                            groups=in_channels,
+                        ))
+
+                layers.append(
+                    torch.nn.Conv2d(
+                        in_channels=in_channels,
+                        out_channels=conv_channels,
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        groups=1,
+                    ))
+                layers.append(activation)
+                in_channels = conv_channels
+
+        elif subsampling == "striding":
+            self._stride = 2
+            self._kernel_size = 3
+            self._ceil_mode = False
+
+            if self.is_causal:
+                self._left_padding = self._kernel_size - 1
+                self._right_padding = self._stride - 1
+                self._max_cache_len = subsampling_factor + 1
+            else:
+                self._left_padding = (self._kernel_size - 1) // 2
+                self._right_padding = (self._kernel_size - 1) // 2
+                self._max_cache_len = 0
+
+            for i in range(self._sampling_num):
+                if self.is_causal:
+                    layers.append(
+                        CausalConv2D(
+                            in_channels=in_channels,
+                            out_channels=conv_channels,
+                            kernel_size=self._kernel_size,
+                            stride=self._stride,
+                            padding=None,
+                        ))
+                else:
+                    layers.append(
+                        torch.nn.Conv2d(
+                            in_channels=in_channels,
+                            out_channels=conv_channels,
+                            kernel_size=self._kernel_size,
+                            stride=self._stride,
+                            padding=self._left_padding,
+                        ))
+                layers.append(activation)
+                in_channels = conv_channels
+
+        elif subsampling == "striding_conv1d":
+            in_channels = feat_in
+
+            self._stride = 2
+            self._kernel_size = 5
+            self._ceil_mode = False
+
+            if self.is_causal:
+                self._left_padding = self._kernel_size - 1
+                self._right_padding = self._stride - 1
+                self._max_cache_len = subsampling_factor + 1
+            else:
+                self._left_padding = (self._kernel_size - 1) // 2
+                self._right_padding = (self._kernel_size - 1) // 2
+                self._max_cache_len = 0
+
+            for i in range(self._sampling_num):
+                if self.is_causal:
+                    layers.append(
+                        CausalConv1D(
+                            in_channels=in_channels,
+                            out_channels=(feat_out if self._sampling_num == i +
+                                          1 else conv_channels),
+                            kernel_size=self._kernel_size,
+                            stride=self._stride,
+                            padding=None,
+                        ))
+                else:
+                    layers.append(
+                        torch.nn.Conv1d(
+                            in_channels=in_channels,
+                            out_channels=(feat_out if self._sampling_num == i +
+                                          1 else conv_channels),
+                            kernel_size=self._kernel_size,
+                            stride=self._stride,
+                            padding=self._left_padding,
+                        ))
+                layers.append(activation)
+                in_channels = conv_channels
+
+        elif subsampling == "dw_striding_conv1d":
+            in_channels = feat_in
+
+            self._stride = 2
+            self._kernel_size = 5
+            self._ceil_mode = False
+
+            self._left_padding = (self._kernel_size - 1) // 2
+            self._right_padding = (self._kernel_size - 1) // 2
+
+            # Layer 1
+            layers.extend([
+                torch.nn.Conv1d(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    kernel_size=self._kernel_size,
+                    stride=self._stride,
+                    padding=self._left_padding,
+                    groups=in_channels,
+                ),
+                torch.nn.Conv1d(
+                    in_channels=in_channels,
+                    out_channels=(feat_out if self._sampling_num == 1 else
+                                  conv_channels),
+                    kernel_size=1,
+                    stride=1,
+                    padding=0,
+                    groups=1,
+                ),
+            ])
+            in_channels = conv_channels
+            layers.append(activation)
+
+            for i in range(self._sampling_num - 1):
+                layers.extend([
+                    torch.nn.Conv1d(
+                        in_channels=in_channels,
+                        out_channels=in_channels,
+                        kernel_size=self._kernel_size,
+                        stride=self._stride,
+                        padding=self._left_padding,
+                        groups=in_channels,
+                    ),
+                    torch.nn.Conv1d(
+                        in_channels=in_channels,
+                        out_channels=(feat_out if self._sampling_num == i +
+                                      2 else conv_channels),
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        groups=1,
+                    ),
+                ])
+                layers.append(activation)
+                in_channels = conv_channels
+
+        else:
+            raise ValueError(f"Not valid sub-sampling: {subsampling}!")
+
+        if subsampling in ["dw_striding", "striding"]:
+            in_length = torch.tensor(feat_in, dtype=torch.float)
+            out_length = calc_length(
+                lengths=in_length,
+                all_paddings=self._left_padding + self._right_padding,
+                kernel_size=self._kernel_size,
+                stride=self._stride,
+                ceil_mode=self._ceil_mode,
+                repeat_num=self._sampling_num,
+            )
+            self.out = torch.nn.Linear(conv_channels * int(out_length),
+                                       feat_out)
+            self.conv2d_subsampling = True
+        elif subsampling in ["striding_conv1d", "dw_striding_conv1d"]:
+            self.out = None
+            self.conv2d_subsampling = False
+        else:
+            raise ValueError(f"Not valid sub-sampling: {subsampling}!")
+
+        self.conv = torch.nn.Sequential(*layers)
+
+    def get_sampling_frames(self):
+        return [1, self.subsampling_factor]
+
+    def get_streaming_cache_size(self):
+        return [0, self.subsampling_factor + 1]
+
+    def forward(self, x, mask):
+        """
+        Forward method for NeMo subsampling.
+
+        Args:
+            x[Batch, Time, Filters]: torch.Tensor
+                input tensor
+            x_mask: torch.Tensor
+                input mask
+
+        Returns:
+            x: torch.Tensor
+                Resulting tensor from subsampling (B, T // 
+                time_reduction_factor, feat_out)
+            pad_mask: torch.Tensor
+                tensor of padded hidden state sequences (B, 1, T // 
+                time_reduction_factor)
+        """
+        x = x.unsqueeze(1) if self.conv2d_subsampling else x.transpose(1, 2)
+
+        # split inputs if chunking_factor is set
+        if (self.subsampling_conv_chunking_factor != -1
+                and self.conv2d_subsampling):
+            if self.subsampling_conv_chunking_factor == 1:
+                # if subsampling_conv_chunking_factor is 1, we split only
+                # if needed.
+                # avoiding a bug / feature limiting indexing of tensors
+                # to 2**31.
+                # see https://github.com/pytorch/pytorch/issues/80020
+                x_ceil = (2**31 / self._conv_channels * self._stride *
+                          self._stride)
+                need_to_split = torch.numel(x) > x_ceil
+            else:
+                # if subsampling_conv_chunking_factor > 1 we always split
+                need_to_split = True
+
+            if need_to_split:
+                x, success = self.conv_split_by_batch(x)
+                if not success:  # if unable to split by batch, try by channel
+                    if self._subsampling == "dw_striding":
+                        x = self.conv_split_by_channel(x)
+                    else:
+                        x = self.conv(x)  # try anyway
+            else:
+                x = self.conv(x)
+        else:
+            x = self.conv(x)
+
+        # Flatten Channel and Frequency Axes
+        if self.conv2d_subsampling:
+            b, c, t, f = x.size()
+            x = self.out(x.transpose(1, 2).reshape(b, t, -1))
+        # Transpose to Channel Last mode
+        else:
+            x = x.transpose(1, 2)
+
+        if mask is None:
+            return x, None
+
+        max_audio_length = x.shape[1]
+        feature_lens = mask.sum(1)
+        padding_length = torch.ceil(feature_lens / self.subsampling_factor)
+        if self.is_causal and self.subsampling_causal_cond:
+            feature_lens_remainder = feature_lens % self.subsampling_factor
+            padding_length[feature_lens_remainder != 1] += 1
+        pad_mask = torch.arange(0, max_audio_length, device=x.device).expand(
+            padding_length.size(0), -1) < padding_length.unsqueeze(1)
+        return x, pad_mask.unsqueeze(1)
+
+    def reset_parameters(self):
+        # initialize weights
+        if self._subsampling == "dw_striding":
+            with torch.no_grad():
+                # init conv
+                scale = 1.0 / self._kernel_size
+                dw_max = (self._kernel_size**2)**-0.5
+                pw_max = self._conv_channels**-0.5
+
+                torch.nn.init.uniform_(self.conv[0].weight, -scale, scale)
+                torch.nn.init.uniform_(self.conv[0].bias, -scale, scale)
+
+                for idx in range(2, len(self.conv), 3):
+                    torch.nn.init.uniform_(self.conv[idx].weight, -dw_max,
+                                           dw_max)
+                    torch.nn.init.uniform_(self.conv[idx].bias, -dw_max,
+                                           dw_max)
+                    torch.nn.init.uniform_(self.conv[idx + 1].weight, -pw_max,
+                                           pw_max)
+                    torch.nn.init.uniform_(self.conv[idx + 1].bias, -pw_max,
+                                           pw_max)
+
+                # init fc (80 * 64 = 5120 from https://github.com/kssteven418/
+                # Squeezeformer/blob/13c97d6cf92f2844d2cb3142b4c5bfa9ad1a8951/
+                # src/models/conformer_encoder.py#L487
+                fc_scale = (self._feat_out * self._feat_in /
+                            self._sampling_num)**-0.5
+                torch.nn.init.uniform_(self.out.weight, -fc_scale, fc_scale)
+                torch.nn.init.uniform_(self.out.bias, -fc_scale, fc_scale)
+
+    def conv_split_by_batch(self, x):
+        """Tries to split input by batch, run conv and concat results"""
+        b, _, _, _ = x.size()
+        if b == 1:  # can't split if batch size is 1
+            return x, False
+
+        if self.subsampling_conv_chunking_factor > 1:
+            cf = self.subsampling_conv_chunking_factor
+        else:
+            # avoiding a bug / feature limiting indexing of tensors to 2**31
+            # see https://github.com/pytorch/pytorch/issues/80020
+            x_ceil = 2**31 / self._conv_channels * self._stride * self._stride
+            p = math.ceil(math.log(torch.numel(x) / x_ceil, 2))
+            cf = 2**p
+
+        new_batch_size = b // cf
+        if new_batch_size == 0:  # input is too big
+            return x, False
+
+        return (
+            torch.cat([
+                self.conv(chunk)
+                for chunk in torch.split(x, new_batch_size, 0)
+            ]),
+            True,
+        )
+
+    def conv_split_by_channel(self, x):
+        """For dw convs, tries to split input by time, run conv and concat 
+        results"""
+        x = self.conv[0](x)  # full conv2D
+        x = self.conv[1](x)  # activation
+
+        for i in range(self._sampling_num - 1):
+            _, c, t, _ = x.size()
+
+            if self.subsampling_conv_chunking_factor > 1:
+                cf = self.subsampling_conv_chunking_factor
+            else:
+                # avoiding a bug / feature limiting indexing of tensors
+                # to 2**31
+                # see https://github.com/pytorch/pytorch/issues/80020
+                p = math.ceil(math.log(torch.numel(x) / 2**31, 2))
+                cf = 2**p
+
+            new_c = int(c // cf)
+            if new_c == 0:
+                new_c = 1
+
+            new_t = int(t // cf)
+            if new_t == 0:
+                new_t = 1
+
+            x = self.channel_chunked_conv(self.conv[i * 3 + 2], new_c,
+                                          x)  # conv2D, depthwise
+
+            # splitting pointwise convs by time
+            x = torch.cat(
+                [
+                    self.conv[i * 3 + 3](chunk)
+                    for chunk in torch.split(x, new_t, 2)
+                ],
+                2,
+            )  # conv2D, pointwise
+            x = self.conv[i * 3 + 4](x)  # activation
+        return x
+
+    def channel_chunked_conv(self, conv, chunk_size, x):
+        """Performs channel chunked convolution"""
+
+        ind = 0
+        out_chunks = []
+        for chunk in torch.split(x, chunk_size, 1):
+            step = chunk.size()[1]
+
+            if self.is_causal:
+                chunk = nn.functional.pad(
+                    chunk,
+                    pad=(
+                        self._kernel_size - 1,
+                        self._stride - 1,
+                        self._kernel_size - 1,
+                        self._stride - 1,
+                    ),
+                )
+                ch_out = nn.functional.conv2d(
+                    chunk,
+                    conv.weight[ind:ind + step, :, :, :],
+                    bias=conv.bias[ind:ind + step],
+                    stride=self._stride,
+                    padding=0,
+                    groups=step,
+                )
+            else:
+                ch_out = nn.functional.conv2d(
+                    chunk,
+                    conv.weight[ind:ind + step, :, :, :],
+                    bias=conv.bias[ind:ind + step],
+                    stride=self._stride,
+                    padding=self._left_padding,
+                    groups=step,
+                )
+            out_chunks.append(ch_out)
+            ind += step
+
+        return torch.cat(out_chunks, 1)
+
+    def change_subsampling_conv_chunking_factor(
+            self, subsampling_conv_chunking_factor: int):
+        if (subsampling_conv_chunking_factor != -1
+                and subsampling_conv_chunking_factor != 1
+                and subsampling_conv_chunking_factor % 2 != 0):
+            raise ValueError(
+                "subsampling_conv_chunking_factor should be -1, 1, or a "\
+                    "power of 2"
+            )
+        self.subsampling_conv_chunking_factor = subsampling_conv_chunking_factor
+
+
+def calc_length(lengths,
+                all_paddings,
+                kernel_size,
+                stride,
+                ceil_mode,
+                repeat_num=1):
+    """Calculates the output length of a Tensor passed through a convolution or
+      max pooling layer"""
+    add_pad: float = all_paddings - kernel_size
+    one: float = 1.0
+    for i in range(repeat_num):
+        lengths = (torch.div(lengths.to(dtype=torch.float) + add_pad, stride) +
+                   one)
+        lengths = torch.ceil(lengths) if ceil_mode else torch.floor(lengths)
+    return lengths.to(dtype=torch.int)
+
+
+####  multihead attention starts here
+class AttModule(nn.Module):
+    """Attention abstraction module"""
+
+    def __init__(self):
+        super().__init__()
+        self.export_mode = False
+
+    def set_export(self, mode=True):
+        """set the export mode"""
+        self.export_mode = mode
+
+    def forward(
+        self,
+        x: Tensor,
+        memory: Optional[Tensor] = None,
+        pos_emb: Optional[Tensor] = None,
+        att_mask: Optional[Tensor] = None,
+    ) -> tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]:
+        """AttModule forward
+
+        Args:
+            x: torch.Tensor
+                input tensor.
+            memory: torch.Tensor, optional
+                memory tensor.
+            pos_emb: torch.Tensor, optional
+                positional encoder embedding.
+            att_mask: torch.Tensor, optional
+                attention mask tensor.
+        """
+        return x, memory, pos_emb, att_mask
+
+
+class AttBlock(BlockBase, AttModule):
+    """Attention Block module to support both Attention and Block module."""
+
+    def memory_dims(self, max_len=False):
+        """memory dimensions"""
+        return (1, self.input_size)
+
+
+def masked_softmax(
+    scores,
+    mask: Optional[Tensor],
+):
+    if mask is not None:
+        mask = mask.unsqueeze(1).eq(0)  # (batch, 1, time1, time2)
+        scores = scores.masked_fill(mask, -torch.inf)
+        attn = torch.softmax(scores, dim=-1).masked_fill(
+            mask, 0.0)  # (batch, head, time1, time2)
+    else:
+        attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)
+    return attn
+
+
+class MultiHeadedAttention(nn.Module):
+    """Multi-Head Attention layer with optional relative position embedding 
+    and GLU.
+
+    Args:
+        n_head: int
+            the number of heads.
+        n_feat: int
+            input size features.
+        dropout_rate: float
+            dropout rate.
+        use_LN: bool
+            apply layer norm or not
+        dropout_at_output: bool
+            whether to apply dropout at output
+        attention_inner_dim: int, optional
+            the attention dimension used in the class,
+            it can be different from the input dimension n_feat.
+            default: -1 (equal to n_feat).
+        use_pt_scaled_dot_product_attention: bool, optional
+            if set True, use pytorch scaled dot product attention in training.
+            NOTE: this will NOT be used in ONNX decoding due to a lack of 
+            support.  In that case, we use the original attention 
+            implementation, which shows no regression.
+            default: False.
+        n_value: int, optional
+            if set to values other than -1, use a different dimension for 
+            value. With the default value (i.e. -1), it is backward compatible.
+        group_size: int, optional. must divide `n_head`
+            if group_size > 1:       GQA
+            if group_size = 1:       MHA
+            if group_size = n_head:  MQA
+    """
+
+    inv_sqrt_d_k: torch.jit.Final[float]
+    h: torch.jit.Final[int]
+    h_k: torch.jit.Final[int]
+    g: torch.jit.Final[int]
+
+    def __init__(
+        self,
+        n_head,
+        n_feat,
+        dropout_rate,
+        attention_inner_dim=-1,
+        glu_type="swish",
+        bias_in_glu=True,
+        use_pt_scaled_dot_product_attention=False,
+        n_value=-1,
+        group_size: int = 1,
+    ):
+        super().__init__()
+        if n_value == -1:
+            n_value = n_feat
+        if attention_inner_dim == -1:
+            attention_inner_dim = n_feat
+        assert attention_inner_dim % n_head == 0
+
+        # We assume d_v always equals d_k
+        self.d_k = attention_inner_dim // n_head
+        self.inv_sqrt_d_k = 1.0 / math.sqrt(self.d_k)
+        self.h = n_head
+        assert n_head % group_size == 0, "group_size must divide n_head"
+        self.g = group_size
+        self.h_k = n_head // group_size
+
+        self.linear_q = nn.Linear(n_feat, attention_inner_dim)
+        self.linear_k = nn.Linear(n_feat, attention_inner_dim // group_size)
+        self.linear_v = nn.Linear(n_value, attention_inner_dim // group_size)
+        self.linear_out = nn.Linear(attention_inner_dim // group_size, n_value)
+
+        self.attn = torch.jit.Attribute(None, Optional[Tensor])
+        self.dropout = nn.Dropout(p=dropout_rate)
+        self.dropout_rate = dropout_rate
+        self.use_pt_scaled_dot_product_attention = (
+            use_pt_scaled_dot_product_attention)
+
+        if use_pt_scaled_dot_product_attention and group_size > 1:
+            raise ValueError("Cannot use PT Scaled Attention with GQA")
+
+        # Torchscript eager quantization.  Note that these functions below are
+        # NOOPs and have very little impact on performance unless quantization
+        # is enabled.
+        self.quant_q = torch.ao.quantization.QuantStub()
+        self.quant_x = torch.ao.quantization.QuantStub()
+        self.dequant = torch.ao.quantization.DeQuantStub()
+        self.ffunc = torch.ao.nn.quantized.FloatFunctional()
+
+    def forward(
+        self,
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+        pos_k: Tensor,
+        pos_v: Tensor,
+        mask: Optional[Tensor],
+        relative_attention_bias: Optional[Tensor] = None,
+    ):
+        """Compute 'Scaled Dot Product Attention'.
+
+        Args:
+            query: torch.Tensor
+                query tensor (batch, time1, size)
+            key: torch.Tensor
+                key tensor (batch, time2, size)
+            value: torch.Tensor
+                value tensor (batch, time1, size)
+            pos_k: torch.Tensor
+                key tensor used for relative positional embedding.
+            pos_v: torch.Tensor
+                value tensor used for relative positional embedding.
+            mask: torch.Tensor
+                mask tensor (batch, time1, time2)
+            relative_attention_bias: torch.Tensor
+                bias added to attention logits w.r.t. relative positions
+                (1, n_head, time1, time2)
+        """
+        n_batch = query.size(0)
+
+        q = self.linear_q(query).view(n_batch, -1, self.h,
+                                      self.d_k)  # (b, t, d)
+        k = self.linear_k(key).view(n_batch, -1, self.h_k,
+                                    self.d_k)  # (b, t, d)
+        v = self.linear_v(value).view(n_batch, -1, self.h_k, self.d_k)
+        q = (q.transpose(1, 2) if self.use_pt_scaled_dot_product_attention
+             and not torch.jit.is_scripting() else q.transpose(1, 2) *
+             self.inv_sqrt_d_k)
+        k = k.transpose(1, 2)  # (batch, head_k, time2, d_k)
+        v = v.transpose(1, 2)  # (batch, head_k, time2, d_k)
+
+        if (self.use_pt_scaled_dot_product_attention
+                and not torch.jit.is_scripting()):
+            attn_mask = None
+            if mask is not None:
+                mask = mask.unsqueeze(1)
+                if relative_attention_bias is not None:
+                    attn_mask = mask + relative_attention_bias
+                else:
+                    attn_mask = mask
+                if mask.dtype != q.dtype:
+                    attn_mask = attn_mask.to(q.dtype)
+
+            with torch.nn.attention.sdpa_kernel([
+                    torch.nn.attention.SDPBackend.FLASH_ATTENTION,
+                    torch.nn.attention.SDPBackend.EFFICIENT_ATTENTION,
+                    torch.nn.attention.SDPBackend.MATH,
+                    torch.nn.attention.SDPBackend.CUDNN_ATTENTION,
+            ]):
+                x = torch.nn.functional.scaled_dot_product_attention(
+                    q,
+                    k,
+                    v,
+                    attn_mask=attn_mask,
+                    dropout_p=self.dropout_rate,
+                )
+        else:
+            if self.h != self.h_k:
+                q = q.reshape(n_batch, self.g, self.h_k, -1, self.d_k)
+                A = torch.einsum("b g h t d, b h s d -> b h t s", q, k)
+            else:
+                A = torch.matmul(q, k.transpose(-2, -1))
+            if pos_k is not None:
+                if self.h != self.h_k:
+                    B = torch.einsum("b g h t d, t s d -> b h t s", q, pos_k)
+                else:
+                    reshape_q = (q.contiguous().view(n_batch * self.h, -1,
+                                                     self.d_k).transpose(0, 1)
+                                 )  # (t1,nh,dk)
+                    B = torch.matmul(reshape_q,
+                                     pos_k.transpose(-2,
+                                                     -1))  # pos_k: (t1,dk,t2)
+                    B = B.transpose(0, 1).view(n_batch, self.h, pos_k.size(0),
+                                               pos_k.size(1))
+                scores = A + B
+            else:
+                scores = A
+
+            if relative_attention_bias is not None:
+                scores = scores + relative_attention_bias
+
+            attn = masked_softmax(scores, mask)  # (batch, head, time1, time2)
+
+            self.attn = attn
+
+            p_attn = self.dropout(attn)
+            x = torch.matmul(p_attn.to(v.dtype),
+                             v)  # (batch, head, time1, d_k)
+            if pos_v is not None:
+                reshape_attn = (p_attn.contiguous().view(
+                    n_batch * self.h, pos_v.size(0),
+                    pos_v.size(1)).transpose(0, 1))  # (t1, bh, t2)
+
+                attn_v = (torch.matmul(reshape_attn, pos_v).transpose(
+                    0, 1).contiguous().view(n_batch, self.h, pos_v.size(0),
+                                            self.d_k))
+                x = x + attn_v
+        x = (x.transpose(1, 2).contiguous().view(n_batch, -1,
+                                                 self.h_k * self.d_k)
+             )  # (batch, time1, d_model)
+
+        return self.linear_out(x)  # (batch, time1, d_model)
+
+
+class MultiSequential(torch.nn.Sequential):
+    """Multi-input multi-output torch.nn.Sequential"""
+
+    @torch.jit.ignore
+    def forward(self, *args):
+        """Forward method implementation."""
+        for m in self:
+            args = m(*args)
+        return args
+
+
+def get_offset(input_layer: str, time_reduction: int):
+    """Get an offset. We will use the offset for determining #frames of a 
+    subsampled feature.
+
+    Args:
+        input_layer (str): Type of an input layer
+        time_reduction (int): time reduction factor for downsampling a feature
+    Returns:
+        int: offset
+    """
+    if input_layer in ("conv2d", "nemo_conv") and time_reduction == 4:
+        return 3
+    if input_layer in ("conv2d", ) and time_reduction == 6:
+        return 1
+    if input_layer in ("conv2d", "nemo_conv") and time_reduction == 8:
+        return 7
+    return 0
+
+
+def unfold_tensor(xs_pad, max_seq_len):
+    """
+    For a given tensor with shape of (N, T, D), if sequence length T is 
+    longer than max_seq_len, this function unfold it to a 
+    (NT', max_seq_len, D) where T' is T // max_seq_len.
+    Args:
+        xs_pad: N, T, D
+    """
+    _, _, D = xs_pad.shape
+    xs_pad = xs_pad.transpose(-1, -2)  # convert to N, D, T
+    # N x D x 1 x T => N x (D x max_seq_len) x T'
+    xs_pad = F.unfold(
+        xs_pad[..., None, :],
+        kernel_size=(1, max_seq_len),
+        stride=(1, max_seq_len),
+    )
+    new_bsz, _, slen = xs_pad.shape
+    # N x D x max_seq_len x T'
+    xs_pad = xs_pad.view(new_bsz, -1, max_seq_len, slen)
+    # N x T' x max_seq_len x D
+    xs_pad = xs_pad.permute(0, 3, 2, 1).contiguous()
+    # NT' x max_seq_len x D
+    xs_pad = xs_pad.view(-1, max_seq_len, D)
+    return xs_pad
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phimoe.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phimoe.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfe0982204fa95816a11b8cea0c756de4ad6feaa
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/phimoe.py
@@ -0,0 +1,680 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only PhiMoE model."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers.configuration_utils import PretrainedConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class PhiMoEConfig(PretrainedConfig):
+
+    model_type = "phimoe"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        head_dim=None,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=1e6,
+        sliding_window=None,
+        attention_dropout=0.0,
+        num_experts_per_tok=2,
+        num_local_experts=16,
+        output_router_logits=False,
+        router_aux_loss_coef=0.001,
+        router_jitter_noise=0.0,
+        attention_bias=False,
+        lm_head_bias=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+        self.attention_bias = attention_bias
+        self.lm_head_bias = lm_head_bias
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+        if head_dim is None:
+            head_dim = hidden_size // num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.router_jitter_noise = router_jitter_noise
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+class mp(torch.autograd.Function):
+
+    @staticmethod
+    def forward(
+        ctx,
+        scores: torch.Tensor,
+        multiplier: torch.Tensor,
+        selected_experts: torch.Tensor,
+        masked_gates: torch.Tensor,
+        mask_for_one: torch.Tensor,
+    ):
+        ctx.save_for_backward(multiplier, selected_experts, masked_gates)
+        return multiplier * mask_for_one
+
+    @staticmethod
+    def backward(
+        ctx,
+        grad_at_output: torch.Tensor,
+    ):
+        multiplier, selected_experts, masked_gates = ctx.saved_tensors
+
+        grad_at_output = grad_at_output * multiplier
+
+        grad_at_scores_expanded = masked_gates * grad_at_output.mul(-1)
+        grad_at_scores_expanded.scatter_add_(
+            dim=-1,
+            index=selected_experts,
+            src=grad_at_output,
+        )
+
+        return (
+            grad_at_scores_expanded,
+            None,
+            None,
+            None,
+            None,
+        )
+
+
+def sparsemixer(scores, jitter_eps=0.01):
+    ################ first expert ################
+
+    with torch.no_grad():
+        # compute mask for sparsity
+        mask_logits_threshold, max_ind = scores.max(dim=-1, keepdim=True)
+        factor = scores.abs().clamp(min=mask_logits_threshold)
+        mask_logits_threshold = ((mask_logits_threshold - scores) /
+                                 factor) > (2 * jitter_eps)
+
+    # apply mask
+    masked_gates = scores.masked_fill(mask_logits_threshold, float("-inf"))
+    selected_experts = max_ind
+
+    # compute scores for gradients
+    masked_gates = torch.softmax(masked_gates, dim=-1)
+    multiplier_o = masked_gates.gather(dim=-1, index=selected_experts)
+
+    multiplier = multiplier_o
+
+    # masked out first expert
+    masked_scores = torch.scatter(
+        scores,
+        -1,
+        selected_experts,
+        float("-inf"),
+    )
+    with torch.no_grad():
+        # compute mask for sparsity
+        mask_logits_threshold, max_ind = masked_scores.max(dim=-1,
+                                                           keepdim=True)
+        factor = scores.abs().clamp(min=mask_logits_threshold)
+        mask_logits_threshold = ((mask_logits_threshold - scores) /
+                                 factor) > (2 * jitter_eps)
+
+    # apply mask
+    masked_gates_top2 = masked_scores.masked_fill(mask_logits_threshold,
+                                                  float("-inf"))
+    selected_experts_top2 = max_ind
+    # compute scores for gradients
+    masked_gates_top2 = torch.softmax(masked_gates_top2, dim=-1)
+    multiplier_top2 = masked_gates_top2.gather(dim=-1,
+                                               index=selected_experts_top2)
+
+    multiplier = torch.concat((multiplier, multiplier_top2), dim=-1)
+    selected_experts = torch.concat((selected_experts, selected_experts_top2),
+                                    dim=-1)
+
+    return (
+        multiplier,
+        selected_experts,
+    )
+
+
+def phimoe_routing_function(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+):
+    assert hidden_states.shape[0] == gating_output.shape[0], (
+        "Number of tokens mismatch")
+    assert topk == 2, "Only top-2 routing is supported"
+    assert renormalize is False, "Renormalization is not supported"
+
+    topk_weights, topk_ids = sparsemixer(gating_output)
+    return topk_weights, topk_ids
+
+
+class PhiMoE(nn.Module):
+    """A tensor-parallel MoE implementation for PhiMoE that shards each expert
+    across all ranks.
+
+    Each expert's weights are sharded across all ranks and a fused MoE
+    kernel is used for the forward pass, and finally we reduce the outputs
+    across ranks.
+    """
+
+    def __init__(
+        self,
+        num_experts: int,
+        top_k: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: Optional[torch.dtype] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        tp_size: Optional[int] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+
+        # Gate always runs at half / full precision for now.
+        self.gate = ReplicatedLinear(
+            hidden_size,
+            num_experts,
+            bias=False,
+            params_dtype=params_dtype,
+            quant_config=None,
+        )
+
+        self.experts = FusedMoE(
+            num_experts=num_experts,
+            top_k=top_k,
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            params_dtype=params_dtype,
+            reduce_results=True,
+            renormalize=False,
+            quant_config=quant_config,
+            tp_size=tp_size,
+            custom_routing_function=phimoe_routing_function,
+            prefix=f"{prefix}.experts")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # NOTE: hidden_states can have either 1D or 2D shape.
+        orig_shape = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+        final_hidden_states = self.experts(hidden_states, router_logits)
+        return final_hidden_states.view(orig_shape)
+
+
+class PhiMoEAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        head_dim: Optional[int] = None,
+        max_position: int = 4096 * 32,
+        rope_theta: float = 10000,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        rope_scaling: Optional[dict] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        if head_dim is None:
+            head_dim = hidden_size // num_heads
+        self.head_dim = head_dim
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+            rope_scaling=self.rope_scaling,
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class PhiMoEDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PhiMoEConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        self.self_attn = PhiMoEAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            head_dim=getattr(config, "head_dim",
+                             self.hidden_size // config.num_attention_heads),
+            rope_theta=rope_theta,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            rope_scaling=config.rope_scaling,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.block_sparse_moe = PhiMoE(
+            num_experts=config.num_local_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.block_sparse_moe",
+        )
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.rms_norm_eps,
+                                            elementwise_affine=True)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
+                                                     eps=config.rms_norm_eps,
+                                                     elementwise_affine=True)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        residual = hidden_states
+
+        # Self Attention
+        hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        hidden_states = hidden_states + residual
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.block_sparse_moe(hidden_states)
+
+        hidden_states = hidden_states + residual
+        return hidden_states, residual
+
+
+@support_torch_compile
+class PhiMoEModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        lora_vocab = ((lora_config.lora_extra_vocab_size *
+                       (lora_config.max_loras or 1)) if lora_config else 0)
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+        self.config = config
+        self.quant_config = quant_config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: PhiMoEDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers")
+        self.norm = nn.LayerNorm(config.hidden_size,
+                                 eps=config.rms_norm_eps,
+                                 elementwise_affine=True)
+
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="w1",
+            ckpt_down_proj_name="w2",
+            ckpt_up_proj_name="w3",
+            num_experts=self.config.num_local_experts,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+        for name, loaded_weight in weights:
+            if (self.quant_config is not None and
+                (scale_name := self.quant_config.get_cache_scale(name))):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
+                                 loaded_weight[0])
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                    )
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class PhiMoEForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    fall_back_to_pt_during_load = False
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.lora_config = lora_config
+        self.quant_config = vllm_config.quant_config
+
+        self.model = PhiMoEModel(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "model"))
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            self.unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=(
+                DEFAULT_VOCAB_PADDING_SIZE
+                # We need bigger padding if using lora for kernel
+                # compatibility
+                if not lora_config else lora_config.lora_vocab_padding_size),
+            quant_config=None,
+            bias=True,
+        )
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size)
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/pixtral.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/pixtral.py
new file mode 100644
index 0000000000000000000000000000000000000000..41eaf372785ebb7962c26f04a5d6394eefdc7544
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/pixtral.py
@@ -0,0 +1,1349 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from dataclasses import dataclass, fields
+from functools import cached_property
+from typing import Literal, Optional, TypedDict, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mistral_common.protocol.instruct.messages import (ImageChunk, TextChunk,
+                                                       UserMessage)
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+from mistral_common.tokens.tokenizers.multimodal import ImageEncoder
+from PIL import Image
+from transformers import PixtralVisionConfig, TensorType
+from transformers.image_utils import ImageInput
+from transformers.models.pixtral.image_processing_pixtral import (
+    _num_image_tokens as _get_pixtral_hf_num_image_tokens)
+from transformers.models.pixtral.modeling_pixtral import (
+    PixtralRotaryEmbedding, apply_rotary_pos_emb, position_ids_in_meshgrid)
+from transformers.tokenization_utils_base import TextInput
+
+from vllm.config import VllmConfig
+from vllm.distributed import divide, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_and_mul_fn
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    NestedTensors)
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+                                   MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, MultiModalHashes,
+                                        PromptReplacement, PromptUpdate,
+                                        PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
+from vllm.platforms import current_platform
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.tokenizer import (MistralTokenizer,
+                                               cached_tokenizer_from_config)
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .utils import (flatten_bn, init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
+
+try:
+    from xformers import ops as xops
+    if (current_platform.is_cuda()
+            and current_platform.has_device_capability(100)):
+        # Xformers FA is not compatible with B200
+        USE_XFORMERS_OPS = False
+    else:
+        USE_XFORMERS_OPS = True
+except ImportError:
+    USE_XFORMERS_OPS = False
+
+PATCH_MERGE = "patch_merge"
+
+
+class PixtralImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+
+    images: Union[torch.Tensor, list[torch.Tensor]]
+    """
+    Shape: `(batch_size * num_images, num_channels, image_width, image_height)`
+
+    The result of stacking `ImageEncoding.tokens` from each prompt.
+    """
+
+
+class PixtralProcessorAdapter:
+    """
+    Provide a HF-compatible interface for
+    `mistral_common.tokens.tokenizers.multimodal.ImageEncoder`.
+    """
+
+    def __init__(self, tokenizer: MistralTokenizer) -> None:
+        super().__init__()
+
+        self.tokenizer = tokenizer
+
+    @property
+    def image_processor(self) -> ImageEncoder:
+        image_encoder = self.tokenizer.instruct.mm_encoder
+        assert isinstance(image_encoder, ImageEncoder)
+        return image_encoder
+
+    @cached_property
+    def image_break_id(self) -> int:
+        return self.image_processor.special_ids.img_break
+
+    @cached_property
+    def image_token_id(self) -> int:
+        return self.image_processor.special_ids.img
+
+    @cached_property
+    def image_end_id(self) -> int:
+        return self.image_processor.special_ids.img_end
+
+    @cached_property
+    def image_size(self) -> int:
+        return self.image_processor.mm_config.max_image_size
+
+    @cached_property
+    def patch_size(self) -> int:
+        return self.image_processor.mm_config.image_patch_size
+
+    def __call__(
+        self,
+        text: Optional[Union[TextInput, list[TextInput]]] = None,
+        images: Optional[Union[ImageInput, list[ImageInput]]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **kwargs,
+    ) -> Mapping[str, NestedTensors]:
+        if text is None:
+            text = []
+        if not isinstance(text, list):
+            text = [text]
+        if images is None:
+            images = []
+        if not isinstance(images, list):
+            images = [images]
+
+        if not images:
+            input_ids = self.tokenizer(text).input_ids
+
+            return {"input_ids": torch.tensor(input_ids)}
+
+        # Allow dummy text, which is used for profiling as well as token inputs
+        if any(len(t) > 0 for t in text):
+            raise ValueError(
+                "You've passed text inputs instead of token inputs. "
+                "Make sure to process your input via `mistral_common`'s "
+                "tokenizer or pass a chat completion request. "
+                "For more info, see: "
+                "https://github.com/vllm-project/vllm/issues/8411.")
+
+        images_processed = list[torch.Tensor]()
+        images_tokens = list[torch.Tensor]()
+
+        for image in images:
+            image_inputs = self.image_processor(ImageChunk(image=image))
+            image_processed = torch.tensor(image_inputs.image)
+            image_tokens = torch.tensor(image_inputs.tokens)
+
+            images_processed.append(image_processed)
+            images_tokens.append(image_tokens)
+
+        return {
+            "input_ids": torch.cat(images_tokens)[None].expand(len(text), -1),
+            "images": images_processed,
+        }
+
+
+class PixtralProcessingInfo(BaseProcessingInfo):
+
+    def get_tokenizer(self) -> MistralTokenizer:
+        tokenizer = cached_tokenizer_from_config(self.ctx.model_config)
+        if not isinstance(tokenizer, MistralTokenizer):
+            raise ValueError("This model requires `--tokenizer-mode mistral`")
+
+        return tokenizer
+
+    def get_hf_processor(self) -> PixtralProcessorAdapter:
+        return PixtralProcessorAdapter(self.get_tokenizer())
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_vision_config(
+        self,
+        processor: Optional[PixtralProcessorAdapter] = None,
+    ):
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        return PixtralVisionConfig(
+            image_size=processor.image_size,
+            patch_size=processor.patch_size,
+        )
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[PixtralProcessorAdapter] = None,
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        ncols, nrows = processor.image_processor._image_to_num_tokens(
+            Image.new("RGB", (image_width, image_height)))
+
+        return ncols * nrows
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        image_processor = self.get_hf_processor().image_processor
+        max_image_size = image_processor.mm_config.max_image_size
+
+        return ImageSize(width=max_image_size, height=max_image_size)
+
+
+class PixtralDummyInputsBuilder(BaseDummyInputsBuilder[PixtralProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        return ""
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+    def get_dummy_processor_inputs(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> ProcessorInputs:
+        tokenizer = self.info.get_tokenizer()
+
+        dummy_text = self.get_dummy_text(mm_counts)
+        dummy_mm_data = self.get_dummy_mm_data(seq_len, mm_counts)
+        dummy_images = dummy_mm_data.get("image", [])
+        tokenization_kwargs = {"truncation": False}
+
+        request = ChatCompletionRequest(messages=[
+            UserMessage(content=[
+                TextChunk(text=dummy_text),
+                *(ImageChunk(image=image) for image in dummy_images),
+            ]),
+        ])
+        res = tokenizer.mistral.encode_chat_completion(request)
+        dummy_tokens = res.tokens
+
+        return ProcessorInputs(prompt=dummy_tokens,
+                               mm_data=dummy_mm_data,
+                               tokenization_kwargs=tokenization_kwargs)
+
+
+class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo]
+                                 ):
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: Mapping[str, NestedTensors],
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(images=MultiModalFieldConfig.batched("image"))
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        image_break_id = processor.image_break_id
+        image_token_id = processor.image_token_id
+        image_end_id = processor.image_end_id
+
+        def get_replacement(item_idx: int):
+            images = mm_items.get_items("image", ImageProcessorItems)
+            image_size = images.get_image_size(item_idx)
+
+            ncols, nrows = processor.image_processor._image_to_num_tokens(
+                Image.new("RGB", (image_size.width, image_size.height)))
+
+            tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
+            tokens[-1] = image_end_id
+
+            return PromptUpdateDetails.select_token_id(tokens, image_token_id)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target="",  # Never match the prompt (see below note)
+                replacement=get_replacement,
+            ),
+        ]
+
+    def _cached_apply_hf_processor(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        tokenization_kwargs: Mapping[str, object],
+        *,
+        return_mm_hashes: bool,
+    ) -> tuple[list[int], MultiModalKwargs, Optional[MultiModalHashes], bool]:
+        (
+            prompt_ids,
+            mm_kwargs,
+            mm_hashes,
+            _,
+        ) = super()._cached_apply_hf_processor(
+            prompt=prompt,
+            mm_data_items=mm_data_items,
+            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+            tokenization_kwargs=tokenization_kwargs,
+            return_mm_hashes=return_mm_hashes,
+        )
+
+        # NOTE: The tokens are already inserted by the chat template
+        return prompt_ids, mm_kwargs, mm_hashes, True
+
+
+@MULTIMODAL_REGISTRY.register_processor(PixtralMultiModalProcessor,
+                                        info=PixtralProcessingInfo,
+                                        dummy_inputs=PixtralDummyInputsBuilder)
+class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                      SupportsPP):
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return None
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        dataclass_fields = {field.name for field in fields(VisionEncoderArgs)}
+        vision_args = {
+            key: value
+            for key, value in self.config.vision_config.to_dict().items()
+            if key in dataclass_fields
+        }
+
+        self.vision_args = VisionEncoderArgs(**vision_args)
+
+        # init MistralForCausalLM
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        self.vision_encoder = VisionTransformer(self.vision_args)
+
+        if self.vision_args.add_pre_mm_projector_layer_norm:
+            self.pre_mm_projector_norm = RMSNorm(self.vision_args.hidden_size,
+                                                 eps=1e-5)
+
+        if self.vision_args.mm_projector_id == PATCH_MERGE:
+            self.patch_merger = PatchMerger(
+                vision_encoder_dim=self.vision_args.hidden_size,
+                spatial_merge_size=self.vision_args.spatial_merge_size,
+                use_mlp_bias=False,
+            )
+
+        self.vision_language_adapter = VisionLanguageAdapter(
+            self.vision_args, dim=config.text_config.hidden_size)
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[PixtralImagePixelInputs]:
+        images = kwargs.pop("images", None)
+        if images is None:
+            return None
+
+        if not isinstance(images, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of images. "
+                             f"Got type: {type(images)}")
+
+        return PixtralImagePixelInputs(
+            type="pixel_values",
+            images=flatten_bn(images),
+        )
+
+    def _process_image_input(
+        self,
+        image_input: PixtralImagePixelInputs,
+    ) -> tuple[torch.Tensor, ...]:
+        images = image_input["images"]
+        image_features = self.vision_encoder(images)
+        feature_sizes = [
+            image_feature.shape[0] for image_feature in image_features
+        ]
+        image_features = torch.cat(image_features)
+        if self.vision_args.add_pre_mm_projector_layer_norm:
+            image_features = self.pre_mm_projector_norm(image_features)
+        if self.vision_args.mm_projector_id == PATCH_MERGE:
+            patch_size = self.vision_args.patch_size
+            spatial_merge_size_square = self.vision_args.spatial_merge_size**2
+            img_patch_dims = [(img.shape[1] // patch_size,
+                               img.shape[2] // patch_size) for img in images]
+            feature_sizes = [
+                feature_size // spatial_merge_size_square
+                for feature_size in feature_sizes
+            ]
+            image_features = self.patch_merger(image_features,
+                                               image_sizes=img_patch_dims)
+        image_embeds = self.vision_language_adapter(image_features)
+        image_embeds = torch.split(image_embeds, feature_sizes)
+        return image_embeds
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+
+        return self._process_image_input(image_input)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                self.vision_args.image_token_id,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        """Run forward pass for pixtral."""
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model.model(input_ids,
+                                                  positions,
+                                                  intermediate_tensors,
+                                                  inputs_embeds=inputs_embeds)
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+
+        def is_vision_encoder_weights(weight: tuple[str, torch.Tensor]):
+            return weight[0].startswith("vision_encoder")
+
+        def is_vision_lang_adapter_weights(weight: tuple[str, torch.Tensor]):
+            return weight[0].startswith("vision_language_adapter")
+
+        def is_patch_merger(weight: tuple[str, torch.Tensor]):
+            return weight[0].startswith("patch_merger")
+
+        def is_pre_mm_projector_norm(weight: tuple[str, torch.Tensor]):
+            return weight[0].startswith("pre_mm_projector_norm")
+
+        # Get references to parameters for direct loading
+        vision_encoder_dict = dict(self.vision_encoder.named_parameters())
+        patch_merger_dict = dict(self.patch_merger.named_parameters(
+        )) if self.vision_args.mm_projector_id == PATCH_MERGE else dict()
+        pre_mm_projector_norm_dict = dict(
+            self.pre_mm_projector_norm.named_parameters(
+            )) if self.vision_args.add_pre_mm_projector_layer_norm else dict()
+        vision_lang_adapter_dict = dict(
+            self.vision_language_adapter.named_parameters())
+
+        def llm_weights_generator():
+            # Single pass over weights
+            for name, w in weights:
+                if is_vision_encoder_weights((name, w)):
+                    # Load vision encoder weights directly
+                    trimmed_name = '.'.join(name.split(".")[1:])
+                    param = vision_encoder_dict[trimmed_name]
+                    with torch.no_grad():
+                        default_weight_loader(param, w)
+                elif is_patch_merger((name, w)):
+                    # Load vision patch merger weights directly
+                    trimmed_name = '.'.join(name.split(".")[1:])
+                    param = patch_merger_dict[trimmed_name]
+                    with torch.no_grad():
+                        default_weight_loader(param, w)
+                elif is_pre_mm_projector_norm((name, w)):
+                    # Load vision pre_mm_projector_norm weights directly
+                    trimmed_name = '.'.join(name.split(".")[1:])
+                    param = pre_mm_projector_norm_dict[trimmed_name]
+                    with torch.no_grad():
+                        default_weight_loader(param, w)
+                elif is_vision_lang_adapter_weights((name, w)):
+                    # Load vision-language adapter weights directly
+                    trimmed_name = '.'.join(name.split(".")[1:])
+                    param = vision_lang_adapter_dict[trimmed_name]
+                    with torch.no_grad():
+                        default_weight_loader(param, w)
+                else:
+                    # LLM weights: yield them to be loaded
+                    # by language_model.load_weights
+                    yield (name, w)
+
+        # Now we call the language model load with the generator
+        self.language_model.load_weights(llm_weights_generator())
+
+
+# Vision encoder
+@dataclass
+class VisionEncoderArgs:
+    hidden_size: int
+    num_channels: int
+    image_size: int
+    patch_size: int
+    intermediate_size: int
+    num_hidden_layers: int
+    num_attention_heads: int
+    rope_theta: float  # for rope-2D
+    image_token_id: int
+    adapter_bias: bool = True
+    spatial_merge_size: int = 1
+    add_pre_mm_projector_layer_norm: bool = False
+    mm_projector_id: str = ""
+
+
+def _reshape_for_broadcast(freqs_cis: torch.Tensor,
+                           x: torch.Tensor) -> torch.Tensor:
+    """
+    freqs_cis: complex - (seq_len, head_dim / 2)
+    x: complex - (bsz, seq_len, head_dim / 2)
+    """
+    ndim = x.ndim
+    assert ndim > 1
+    assert freqs_cis.shape == (x.shape[1], x.shape[-1]), (
+        freqs_cis.shape,
+        (x.shape[1], x.shape[-1]),
+    )
+    shape = [
+        d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)
+    ]
+    return freqs_cis.view(*shape)
+
+
+def precompute_freqs_cis_2d(
+    dim: int,
+    height: int,
+    width: int,
+    theta: float,
+) -> torch.Tensor:
+    """
+    freqs_cis: 2D complex tensor of shape (height, width, dim // 2)
+        to be indexed by (height, width) position tuples
+    """
+    # (dim / 2) frequency bases
+    freqs = 1.0 / (theta**(torch.arange(0, dim, 2).float() / dim))
+
+    h = torch.arange(height, device=freqs.device)
+    w = torch.arange(width, device=freqs.device)
+
+    freqs_h = torch.outer(h, freqs[::2]).float()
+    freqs_w = torch.outer(w, freqs[1::2]).float()
+    freqs_2d = torch.cat(
+        [
+            freqs_h[:, None, :].repeat(1, width, 1),
+            freqs_w[None, :, :].repeat(height, 1, 1),
+        ],
+        dim=-1,
+    )
+    return torch.polar(torch.ones_like(freqs_2d), freqs_2d)
+
+
+def apply_rotary_emb_vit(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    assert freqs_cis.dtype == torch.complex64
+    freqs_cis = _reshape_for_broadcast(freqs_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+class FeedForward(nn.Module):
+
+    def __init__(self, args: VisionEncoderArgs):
+        super().__init__()
+        assert args.intermediate_size is not None
+        self.w1 = nn.Linear(args.hidden_size,
+                            args.intermediate_size,
+                            bias=False)
+        self.w2 = nn.Linear(args.intermediate_size,
+                            args.hidden_size,
+                            bias=False)
+        self.w3 = nn.Linear(args.hidden_size,
+                            args.intermediate_size,
+                            bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class Attention(nn.Module):
+
+    def __init__(self, args: VisionEncoderArgs):
+        super().__init__()
+        self.args = args
+        assert not args.hidden_size % args.num_attention_heads
+        self.n_heads = args.num_attention_heads
+        self.head_dim = args.hidden_size // args.num_attention_heads
+
+        self.wq = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
+        self.wk = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
+        self.wv = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
+        self.wo = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> torch.Tensor:
+        batch, patches, _ = x.shape
+
+        q, k, v = self.wq(x), self.wk(x), self.wv(x)
+        q = q.reshape(batch, patches, self.n_heads, self.head_dim)
+        k = k.reshape(batch, patches, self.n_heads, self.head_dim)
+        v = v.reshape(batch, patches, self.n_heads, self.head_dim)
+
+        q, k = apply_rotary_emb_vit(q, k, freqs_cis=freqs_cis)
+
+        if USE_XFORMERS_OPS:
+            out = xops.memory_efficient_attention(q, k, v, attn_bias=mask)
+        else:
+            q = q.transpose(1, 2)
+            k = k.transpose(1, 2)
+            v = v.transpose(1, 2)
+            out = nn.functional.scaled_dot_product_attention(q,
+                                                             k,
+                                                             v,
+                                                             attn_mask=mask)
+            out = out.transpose(1, 2)
+
+        out = out.reshape(batch, patches, self.n_heads * self.head_dim)
+        return self.wo(out)
+
+
+class TransformerBlock(nn.Module):
+
+    def __init__(self, args: VisionEncoderArgs):
+        super().__init__()
+        self.attention = Attention(args)
+        self.feed_forward = FeedForward(args)
+        self.attention_norm = RMSNorm(args.hidden_size, eps=1e-5)
+        self.ffn_norm = RMSNorm(args.hidden_size, eps=1e-5)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> torch.Tensor:
+        r = self.attention.forward(self.attention_norm(x),
+                                   mask=mask,
+                                   freqs_cis=freqs_cis)
+        h = x + r
+        r = self.feed_forward.forward(self.ffn_norm(h))
+        out = h + r
+        return out
+
+
+class Transformer(nn.Module):
+
+    def __init__(self, args: VisionEncoderArgs):
+        super().__init__()
+        self.layers = torch.nn.ModuleList()
+        for _ in range(args.num_hidden_layers):
+            self.layers.append(TransformerBlock(args))
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: torch.Tensor,
+        freqs_cis: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        for layer in self.layers:
+            x = layer(x, mask=mask, freqs_cis=freqs_cis)
+        return x
+
+
+def position_meshgrid(patch_embeds_list: list[torch.Tensor], ) -> torch.Tensor:
+    positions = torch.cat([
+        torch.stack(
+            torch.meshgrid(
+                torch.arange(p.shape[-2]),
+                torch.arange(p.shape[-1]),
+                indexing="ij",
+            ),
+            dim=-1,
+        ).reshape(-1, 2) for p in patch_embeds_list
+    ])
+    return positions
+
+
+class VisionTransformer(nn.Module):
+
+    def __init__(self, args: VisionEncoderArgs):
+        super().__init__()
+        self.args = args
+        self.patch_conv = nn.Conv2d(
+            in_channels=args.num_channels,
+            out_channels=args.hidden_size,
+            kernel_size=args.patch_size,
+            stride=args.patch_size,
+            bias=False,
+        )
+        self.ln_pre = RMSNorm(args.hidden_size, eps=1e-5)
+        self.transformer = Transformer(args)
+
+        head_dim = self.args.hidden_size // self.args.num_attention_heads
+        assert head_dim % 2 == 0, "ROPE requires even head_dim"
+        self._freqs_cis: Optional[torch.Tensor] = None
+
+    @property
+    def max_patches_per_side(self) -> int:
+        return self.args.image_size // self.args.patch_size
+
+    @property
+    def device(self) -> torch.types.Device:
+        return next(self.parameters()).device
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return next(self.parameters()).dtype
+
+    @property
+    def freqs_cis(self) -> torch.Tensor:
+        if self._freqs_cis is None:
+            self._freqs_cis = precompute_freqs_cis_2d(
+                dim=self.args.hidden_size // self.args.num_attention_heads,
+                height=self.max_patches_per_side,
+                width=self.max_patches_per_side,
+                theta=self.args.rope_theta,
+            )
+
+        if self._freqs_cis.device != self.device:
+            self._freqs_cis = self._freqs_cis.to(device=self.device)
+
+        return self._freqs_cis
+
+    def forward(
+        self,
+        images: list[torch.Tensor],
+    ) -> torch.Tensor:
+        """
+        Args:
+            images: list of N_img images of variable sizes,
+                each of shape (C, H, W)
+        Returns:
+            image_features: tensor of token features for
+                all tokens of all images of shape (N_toks, D)
+        """
+        # pass images through initial convolution independently
+        patch_embeds_list = [
+            self.patch_conv(img.unsqueeze(0).to(self.dtype)) for img in images
+        ]
+
+        patch_embeds = [
+            p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list
+        ]
+        embed_sizes = [p.shape[1] for p in patch_embeds]
+
+        # flatten to a single sequence
+        patch_embeds = torch.cat(patch_embeds, dim=1)
+        patch_embeds = self.ln_pre(patch_embeds)
+
+        # positional embeddings
+        positions = position_meshgrid(patch_embeds_list).to(self.device)
+        freqs_cis = self.freqs_cis[positions[:, 0], positions[:, 1]]
+
+        # pass through Transformer with a block diagonal mask delimiting images
+        if USE_XFORMERS_OPS:
+            mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
+                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], )
+        else:
+            from transformers.models.pixtral.modeling_pixtral import (
+                generate_block_attention_mask)
+            mask = generate_block_attention_mask(
+                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
+                patch_embeds)
+        out = self.transformer(patch_embeds, mask=mask, freqs_cis=freqs_cis)
+
+        # squeeze dim 0 and split into separate tensors for each image
+        return torch.split(out.squeeze(0), embed_sizes)
+
+
+class VisionLanguageAdapter(nn.Module):
+
+    def __init__(self, args: VisionEncoderArgs, dim: int):
+        super().__init__()
+        assert isinstance(args, VisionEncoderArgs)
+        self.w_in = nn.Linear(
+            args.hidden_size,
+            dim,
+            bias=args.adapter_bias,
+        )
+        self.gelu = nn.GELU()
+        self.w_out = nn.Linear(dim, dim, bias=args.adapter_bias)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.w_out(self.gelu(self.w_in(x)))
+
+
+class PatchMerger(nn.Module):
+    """
+    Learned merging of spatial_merge_size ** 2 patches
+    """
+
+    def __init__(
+        self,
+        vision_encoder_dim: int,
+        spatial_merge_size: int,
+        use_mlp_bias: bool = False,
+    ) -> None:
+        super().__init__()
+
+        mlp_input_dim = vision_encoder_dim * (spatial_merge_size**2)
+
+        self.spatial_merge_size = spatial_merge_size
+        self.mlp_input_dim = mlp_input_dim
+
+        self.merging_layer = nn.Linear(
+            mlp_input_dim,
+            vision_encoder_dim,
+            bias=use_mlp_bias,
+        )
+
+    def forward(self, x: torch.Tensor,
+                image_sizes: list[tuple[int, int]]) -> torch.Tensor:
+        # image_sizes specified in tokens
+        assert sum([h * w for h, w in image_sizes]) == len(x)
+
+        # x is (N, vision_encoder_dim)
+        x = self.permute(x, image_sizes)
+
+        # x is (N / spatial_merge_size ** 2,
+        #       vision_encoder_dim * spatial_merge_size ** 2)
+        x = self.merging_layer(x)
+
+        # x is (N / spatial_merge_size ** 2, vision_encoder_dim)
+        return x
+
+    def permute(
+        self,
+        x: torch.Tensor,
+        image_sizes: list[tuple[int, int]],
+    ) -> torch.Tensor:
+        """
+        Args:
+            x: (N, D) where N is flattened and concatenated patch tokens
+                for all images
+            image_sizes: list of tuple of (height, width) in tokens for
+                each image
+        Returns:
+            image_features: reorders patch tokens so each grid of
+                (spatial_merge_size, spatial_merge_size) is contiguous.
+                now (N / spatial_merge_size ** 2, D * spatial_merge_size ** 2)
+        """
+
+        sub_grids = get_sub_grids(
+            x=x,
+            image_sizes=image_sizes,
+            spatial_merge_size=self.spatial_merge_size
+        )  # list of [d x sub_grid_size x sub_grid_size x n_patches]
+        permuted_tensor: list[torch.Tensor] = []
+        for grid in sub_grids:
+            n_patches = grid.shape[-1]
+            permuted_tensor.append(grid.view(-1, n_patches).t(
+            ))  # n_patches x d * sub_grid_size * sub_grid_size
+        return torch.cat(
+            permuted_tensor, dim=0
+        )  # (N / spatial_merge_size ** 2, d * spatial_merge_size ** 2)
+
+
+def get_sub_grids(
+    x: torch.Tensor,
+    image_sizes: list[tuple[int, int]],
+    spatial_merge_size: int,
+) -> list[torch.Tensor]:
+    # image_sizes specified in tokens
+    tokens_per_image = [h * w for h, w in image_sizes]
+    d = x.shape[-1]
+    all_img_sub_grids: list[torch.Tensor] = []
+    sub_grid_size = spatial_merge_size
+
+    for image_index, image_tokens in enumerate(x.split(tokens_per_image)):
+        # Reshape image_tokens into a 2D grid
+        h, w = image_sizes[image_index]
+        image_grid = image_tokens.view(h, w, d).permute(
+            2, 0, 1)[None, :, :, :]  # 1 x d x h x w
+        sub_grids = torch.nn.functional.unfold(image_grid,
+                                               kernel_size=sub_grid_size,
+                                               stride=sub_grid_size)
+        sub_grids = sub_grids.view(
+            1, d, sub_grid_size, sub_grid_size,
+            -1)  # 1 x d x sub_grid_size x sub_grid_size x n_patches
+
+        all_img_sub_grids.append(sub_grids[0])
+
+    return all_img_sub_grids
+
+
+#### HF Transformers version of Pixtral ####
+# Based off https://github.com/huggingface/transformers/blob/d7950bff82b18c823193d17d72188c5e46d06c83/src/transformers/models/pixtral/modeling_pixtral.py
+# This model follows the Llava family, meaning image embeddings are placed
+# instead of the `[IMG]` token placeholders.
+# The model uses [`PixtralVisionModel`] for its vision encoder,
+# and [`MistralForCausalLM`] for its language decoder.
+
+
+class PixtralHFEncoderInfo(VisionEncoderInfo[PixtralVisionConfig]):
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        ncols, nrows = self.get_patch_grid_size(
+            image_width=image_width,
+            image_height=image_height,
+        )
+        return ncols * nrows
+
+    def get_image_size(self) -> int:
+        return self.vision_config.image_size
+
+    def get_patch_size(self) -> int:
+        # spatial_merge_size is needed for Mistral3
+        spatial_merge_size = getattr(self.hf_config, "spatial_merge_size", 1)
+        return self.vision_config.patch_size * spatial_merge_size
+
+    def get_patch_grid_length(self) -> int:
+        image_size, patch_size = self.get_image_size(), self.get_patch_size()
+
+        # Since interpolation is applied, the image size need not be divisible
+        # assert image_size % patch_size == 0
+        return image_size // patch_size
+
+    # Adapted from: https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/models/pixtral/image_processing_pixtral.py#L99
+    def get_patch_grid_size(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> tuple[int, int]:
+        max_width = max_height = self.get_image_size()
+        patch_width = patch_height = self.get_patch_size()
+
+        ratio = max(image_width / max_width, image_height / max_height)
+
+        if ratio > 1:
+            image_width = int(math.floor(image_width / ratio))
+            image_height = int(math.floor(image_height / ratio))
+
+        nrows, ncols = _get_pixtral_hf_num_image_tokens(
+            (image_height, image_width),
+            (patch_height, patch_width),
+        )  # type: ignore
+
+        return ncols, nrows
+
+
+class PixtralHFMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: PixtralVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        assert config.intermediate_size is not None
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=config.hidden_size,
+            output_sizes=[config.intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(input_size=config.intermediate_size,
+                                           output_size=config.hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config,
+                                           prefix=f"{prefix}.down_proj")
+        self.act_and_mul = get_act_and_mul_fn(config.hidden_act)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_and_mul(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class PixtralHFAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: PixtralVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        assert not config.hidden_size % config.num_attention_heads
+        self.total_num_heads = config.num_attention_heads
+        tp_size = get_tensor_model_parallel_world_size()
+        self.n_heads = divide(config.num_attention_heads, tp_size)
+        self.head_dim = config.hidden_size // config.num_attention_heads
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=config.hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        assert self.total_num_heads * self.head_dim == config.hidden_size
+        self.o_proj = RowParallelLinear(
+            input_size=config.hidden_size,
+            output_size=config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        position_embeddings: torch.Tensor,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        batch, patches, _ = hidden_states.size()
+
+        qkv_states, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv_states.chunk(3, dim=-1)
+
+        # Transpose q and k to apply HF's Rotary Position Embedding
+        q = q.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2)
+        k = k.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2)
+        v = v.view(batch, patches, self.n_heads, self.head_dim)
+        cos, sin = position_embeddings
+        q, k = apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=0)
+
+        if USE_XFORMERS_OPS:
+            # Transpose q and k back for attention
+            q = q.transpose(1, 2).contiguous()
+            k = k.transpose(1, 2).contiguous()
+            out = xops.memory_efficient_attention(q,
+                                                  k,
+                                                  v,
+                                                  attn_bias=attention_mask)
+        else:
+            v = v.transpose(1, 2)
+            out = nn.functional.scaled_dot_product_attention(
+                q, k, v, attn_mask=attention_mask)
+            out = out.transpose(1, 2)
+
+        out = out.view(batch, patches, self.n_heads * self.head_dim)
+        attn_output, _ = self.o_proj(out)
+
+        return attn_output, None
+
+
+class PixtralHFTransformerBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: PixtralVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.attention_norm = RMSNorm(config.hidden_size, eps=1e-5)
+        self.attention = PixtralHFAttention(config,
+                                            quant_config=quant_config,
+                                            prefix=f"{prefix}.attention")
+        self.feed_forward = PixtralHFMLP(config,
+                                         quant_config=quant_config,
+                                         prefix=f"{prefix}.feed_forward")
+        self.ffn_norm = RMSNorm(config.hidden_size, eps=1e-5)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        position_embeddings: torch.Tensor,
+    ) -> torch.Tensor:
+        r, _ = self.attention.forward(self.attention_norm(hidden_states),
+                                      attention_mask=attention_mask,
+                                      position_embeddings=position_embeddings)
+        h = hidden_states + r
+        r = self.feed_forward.forward(self.ffn_norm(h))
+        out = h + r
+        return out
+
+
+class PixtralHFTransformer(nn.Module):
+
+    def __init__(
+        self,
+        config: PixtralVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        if num_hidden_layers_override is None:
+            num_hidden_layers = config.num_hidden_layers
+        else:
+            num_hidden_layers = num_hidden_layers_override
+
+        self.layers = nn.ModuleList([
+            PixtralHFTransformerBlock(config=config,
+                                      quant_config=quant_config,
+                                      prefix=f"{prefix}.layers.{layer_idx}")
+            for layer_idx in range(num_hidden_layers)
+        ])
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor,
+        position_embeddings: torch.Tensor,
+        return_all_hidden_states: bool,
+    ) -> torch.Tensor:
+        hidden_states_pool = [x]
+
+        for layer in self.layers:
+            x = layer(x, attention_mask, position_embeddings)
+            if return_all_hidden_states:
+                hidden_states_pool.append(x)
+        # If we have multiple feature sample layers, we return all hidden
+        # states in order and grab the ones we need by index.
+        if return_all_hidden_states:
+            return hidden_states_pool
+        return x
+
+
+class PixtralHFVisionModel(nn.Module):
+
+    def __init__(
+        self,
+        config: PixtralVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        require_post_norm: Optional[bool] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+
+        self.patch_conv = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=config.hidden_size,
+            kernel_size=config.patch_size,
+            stride=config.patch_size,
+            bias=False,
+        )
+        self.ln_pre = RMSNorm(config.hidden_size, eps=1e-5)
+        self.transformer = PixtralHFTransformer(
+            config,
+            quant_config,
+            num_hidden_layers_override=num_hidden_layers_override,
+            prefix=f"{prefix}.transformer",
+        )
+
+        num_hidden_layers = config.num_hidden_layers
+        if len(self.transformer.layers) > config.num_hidden_layers:
+            raise ValueError(
+                f"The original encoder only has {num_hidden_layers} "
+                f"layers, but you requested {len(self.transformer.layers)} "
+                "layers.")
+
+        if require_post_norm is True:
+            msg = "PixtralHFVisionModel does not have post-layernorm"
+            raise ValueError(msg)
+
+        self.dtype = next(self.parameters()).dtype
+        self.device = next(self.parameters()).device
+        self.patch_positional_embedding = PixtralRotaryEmbedding(
+            config, self.device)
+
+    def forward(
+        self,
+        pixel_values: list[torch.Tensor],
+        feature_sample_layers: Optional[list[int]] = None,
+    ) -> tuple[torch.Tensor, ...]:
+        """
+        Args:
+            pixel_values: Each image to be processed will be a separate tensor
+                in pixel_values. This means it will be a list of tensors
+                because multiple requests batched can have multiple images,
+                each with their own shape potentially
+            feature_sample_layers: Layer indices whose features should be
+                concatenated and used as the visual encoder output. If none
+                are provided, the last layer is used.
+
+        Returns:
+            image_features: tensor of token features for
+                all tokens of all images of shape (N_toks, D)
+        """
+        # pass images through initial convolution independently
+        patch_embeds_list = [
+            self.patch_conv(img.unsqueeze(0).to(self.dtype))
+            for img in pixel_values
+        ]
+
+        patch_embeds = [
+            p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list
+        ]
+        embed_sizes = [p.shape[1] for p in patch_embeds]
+
+        # flatten to a single sequence
+        patch_embeds = torch.cat(patch_embeds, dim=1)
+        patch_embeds = self.ln_pre(patch_embeds)
+
+        # positional embeddings
+        position_ids = position_ids_in_meshgrid(
+            patch_embeds_list,
+            max_width=self.config.image_size // self.config.patch_size).to(
+                self.device)
+        position_embedding = self.patch_positional_embedding(
+            patch_embeds, position_ids)
+
+        if USE_XFORMERS_OPS:
+            attention_mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
+                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], )
+        else:
+            from transformers.models.pixtral.modeling_pixtral import (
+                generate_block_attention_mask)
+            attention_mask = generate_block_attention_mask(
+                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
+                patch_embeds)
+
+        return_all_hidden_states = feature_sample_layers is not None
+        out = self.transformer(
+            patch_embeds,
+            attention_mask,
+            position_embedding,
+            return_all_hidden_states=return_all_hidden_states)
+
+        out = resolve_visual_encoder_outputs(out, feature_sample_layers, None,
+                                             self.config.num_hidden_layers)
+
+        # squeeze dim 0 and split into separate tensors for each image
+        return torch.split(out.squeeze(0), embed_sizes)
+
+    # (TODO) Add prefix argument for filtering out weights to be loaded
+    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        layer_count = len(self.transformer.layers)
+
+        for name, loaded_weight in weights:
+            # omit layers when num_hidden_layers_override is set
+            if name.startswith("transformer.layers"):
+                layer_idx = int(name.split(".")[2])
+                if layer_idx >= layer_count:
+                    continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/plamo2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/plamo2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5034b536266a5605d891a8d14721dedc007030c
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/plamo2.py
@@ -0,0 +1,901 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only PLaMo2 model."""
+from collections.abc import Iterable
+from typing import Optional
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig, PreTrainedModel
+
+from vllm.attention.backends.abstract import AttentionMetadata
+from vllm.attention.layer import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import VllmConfig
+from vllm.distributed import divide, get_tensor_model_parallel_world_size
+from vllm.distributed.parallel_state import get_pp_group
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mamba.mamba2_metadata import (
+    Mamba2Metadata, prepare_mamba2_metadata)
+from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
+    causal_conv1d_fn, causal_conv1d_update)
+from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
+    selective_state_update)
+from vllm.model_executor.layers.mamba.ops.ssd_combined import (
+    mamba_chunk_scan_combined)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    composed_weight_loader, default_weight_loader, sharded_weight_loader)
+from vllm.model_executor.models.interfaces import (HasInnerState, IsHybrid,
+                                                   SupportsPP, SupportsV0Only)
+from vllm.model_executor.models.mamba_cache import (MambaCacheManager,
+                                                    MambaCacheParams)
+from vllm.model_executor.models.utils import (
+    is_pp_missing_parameter, make_empty_intermediate_tensors_factory,
+    make_layers, maybe_prefix)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.sequence import IntermediateTensors
+from vllm.utils import LayerBlockType
+
+
+# Only used for type hinting.
+class Plamo2Config(PretrainedConfig):  # type: ignore
+    model_type: str = "plamo2"
+
+    hidden_size: int
+    num_hidden_layers: int
+    rms_norm_eps: float
+    # Attention
+    num_attention_heads: int
+    hidden_size_per_head: int
+    num_key_value_heads: int
+    # Mamba
+    mamba_d_state: int
+    mamba_d_conv: int
+    mamba_num_heads: int
+    mamba_step: int
+    # MLP
+    intermediate_size: int
+    # Tokenizer
+    vocab_size: int
+
+
+class Plamo2PreTrainedModel(PreTrainedModel):  # type: ignore
+
+    def _init_weights(self, module: torch.nn.Module) -> None:
+        std = 0.02
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+def is_mamba(config: Plamo2Config, i: int) -> bool:
+    assert config.mamba_step > 1
+
+    if config.num_hidden_layers <= (config.mamba_step // 2):
+        # use attention in last layer
+        return i != config.num_hidden_layers - 1
+    return (i % config.mamba_step) != (config.mamba_step // 2)
+
+
+# Adapted from:
+# vllm.model_executor.layers.mamba.mamba_mixer2.MambaMixer2
+# transformers.models.mamba.modeling_mamba.MambaMixer
+class Plamo2MambaMixer(nn.Module):
+
+    def __init__(self,
+                 vllm_config: VllmConfig,
+                 *,
+                 prefix: str = "",
+                 **kwargs) -> None:
+        super().__init__()
+        self.config = vllm_config.model_config.hf_config
+        self.quant_config = vllm_config.quant_config
+        self.hidden_size = self.config.hidden_size
+        self.ssm_state_size = self.config.mamba_d_state
+        self.conv_kernel_size = self.config.mamba_d_conv
+        self.intermediate_size = (self.config.mamba_num_heads *
+                                  self.config.hidden_size_per_head)
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.intermediate_size_per_tp_worker = \
+            self.intermediate_size // self.tp_size
+        self.head_dim = self.config.hidden_size_per_head
+        self.num_heads = self.config.mamba_num_heads
+        self.time_step_rank = max(64, self.hidden_size // 16)
+        self.conv1d = ColumnParallelLinear(
+            input_size=self.conv_kernel_size,
+            output_size=self.intermediate_size,
+            bias=False,
+            prefix=f"{prefix}.conv1d",
+            return_bias=False,
+        )
+        # unsqueeze to fit conv1d weights shape into the linear weights shape.
+        # Can't do this in `weight_loader` since it already exists in
+        # `ColumnParallelLinear` and `set_weight_attrs`
+        # doesn't allow to override it
+        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
+
+        self.in_proj = MergedColumnParallelLinear(
+            self.hidden_size,
+            [self.intermediate_size] * 2,
+            bias=False,
+            quant_config=self.quant_config,
+            prefix=f"{prefix}.in_proj",
+            return_bias=False,
+        )
+        # selective projection used to make dt, B and C input dependent
+        self.bcdt_proj = RowParallelLinear(
+            self.intermediate_size,
+            self.time_step_rank + self.ssm_state_size * 2,
+            bias=False,
+            quant_config=self.quant_config,
+            prefix=f"{prefix}.bcdt_proj",
+            return_bias=False,
+        )
+        # time step projection (discretization) -
+        # In the forward we need to apply dt_proj without the bias,
+        # as the bias is added in the selective scan kernel.
+        self.dt_proj = ColumnParallelLinear(
+            self.time_step_rank,
+            self.num_heads,
+            bias=False,
+            quant_config=self.quant_config,
+            prefix=f"{prefix}.dt_proj",
+            return_bias=False,
+        )
+
+        self.A = nn.Parameter(
+            torch.empty(
+                divide(self.num_heads, self.tp_size),
+                dtype=torch.float32,
+            ))
+        self.D = nn.Parameter(torch.ones(divide(self.num_heads, self.tp_size)))
+        self.dt_bias = nn.Parameter(
+            torch.ones(divide(self.num_heads, self.tp_size)))
+
+        set_weight_attrs(self.D, {"weight_loader": sharded_weight_loader(0)})
+        a_weight_loader = composed_weight_loader(
+            sharded_weight_loader(0), lambda x: -torch.exp(x.float()))
+        set_weight_attrs(self.A, {"weight_loader": a_weight_loader})
+        set_weight_attrs(self.dt_bias,
+                         {"weight_loader": sharded_weight_loader(0)})
+
+        self.out_proj = RowParallelLinear(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            quant_config=self.quant_config,
+            prefix=f"{prefix}.out_proj",
+            return_bias=False,
+        )
+        # The activation function is fixed to SiLU.
+        self.activation = "silu"
+
+        self.dt_norm = RMSNorm(self.time_step_rank,
+                               eps=self.config.rms_norm_eps)
+        self.B_norm = RMSNorm(self.ssm_state_size,
+                              eps=self.config.rms_norm_eps)
+        self.C_norm = RMSNorm(self.ssm_state_size,
+                              eps=self.config.rms_norm_eps)
+
+    def _project_ssm_parameters(self, hidden_states):
+        ssm_parameters = self.bcdt_proj(hidden_states)
+        B, C, time_step = torch.split(
+            ssm_parameters,
+            [self.ssm_state_size, self.ssm_state_size, self.time_step_rank],
+            dim=-1,
+        )
+
+        # vllm._custom_ops.rms_norm requires contiguous input tensors.
+        time_step = self.dt_norm(time_step.contiguous())
+        B = self.B_norm(B.contiguous())
+        C = self.C_norm(C.contiguous())
+        dt = self.dt_proj(time_step)
+        return B, C, dt
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        mamba_cache_params: MambaCacheParams,
+        mamba2_metadata: Mamba2Metadata,
+        **kwargs,
+    ) -> torch.Tensor:
+
+        # mamba2_metadata contains metadata necessary for the mamba2 triton
+        # kernels to operate in continuous batching and in chunked prefill
+        # modes; they are computed at top-level model forward since they
+        # stay the same and reused for all mamba layers in the same iteration
+        attn_metadata: AttentionMetadata = get_forward_context().attn_metadata
+
+        num_prefills = attn_metadata.num_prefills  # request count
+        num_decodes = attn_metadata.num_decode_tokens  # token count (=request)
+        num_prefill_tokens = attn_metadata.num_prefill_tokens  # token count
+        has_prefill = num_prefills > 0
+        has_decode = num_decodes > 0
+
+        # 1. Gated MLP's linear projection
+        projected_states = self.in_proj(hidden_states)
+        gate, hidden_states = projected_states.chunk(2, dim=-1)
+
+        # 2. Convolution sequence transformation
+        conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
+                                               self.conv1d.weight.size(2))
+
+        # Separate prefill and decode by splitting varlen input
+        # Split along token dimension
+        hidden_states_p, hidden_states_d = torch.split(
+            hidden_states,
+            [num_prefill_tokens, num_decodes],
+            dim=0,
+        )
+        gate_p, gate_d = torch.split(gate, [num_prefill_tokens, num_decodes],
+                                     dim=0)
+        # Split along batch dimension
+        state_indices_tensor_p, state_indices_tensor_d = torch.split(
+            mamba_cache_params.state_indices_tensor,
+            [num_prefills, num_decodes],
+            dim=0,
+        )
+        query_start_loc_p = (attn_metadata.query_start_loc[:num_prefills + 1]
+                             if has_prefill else None)
+
+        # Preallocate output tensor to avoid memcpy cost for merging prefill
+        # and decode outputs
+        preallocated_ssm_out = torch.empty(
+            [
+                num_prefill_tokens + num_decodes,
+                (self.num_heads // self.tp_size) * self.head_dim
+            ],
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+        preallocated_ssm_out_p, preallocated_ssm_out_d = torch.split(
+            preallocated_ssm_out,
+            [num_prefill_tokens, num_decodes],
+            dim=0,
+        )
+
+        # Process prefill requests
+        if has_prefill:
+            # 2. Convolution sequence transformation
+            # - "cache_indices" updates the conv_state cache in positions
+            # pointed to by "mamba_cache_params.state_indices_tensor"
+            hidden_states_p = causal_conv1d_fn(
+                hidden_states_p.transpose(0, 1),
+                conv_weights,
+                self.conv1d.bias,
+                activation=self.activation,
+                conv_states=mamba_cache_params.conv_state,
+                has_initial_state=mamba2_metadata.has_initial_states,
+                cache_indices=state_indices_tensor_p,
+                query_start_loc=query_start_loc_p)
+            hidden_states_p = hidden_states_p.transpose(0, 1)
+            hidden_states_p = hidden_states_p[:num_prefill_tokens]
+            # In some instances, the following `bcdt_proj` op
+            # requires contiguous inputs
+            # (e.g. if the Marlin kernel is used).
+            hidden_states_p = hidden_states_p.contiguous()
+
+            B, C, dt = self._project_ssm_parameters(hidden_states_p)
+
+            # 3. State Space Model sequence transformation
+            initial_states = None
+            if (mamba2_metadata.has_initial_states is not None
+                    and mamba2_metadata.prep_initial_states):
+                # making a copy of the states
+                initial_states = torch.where(
+                    mamba2_metadata.has_initial_states[:, None, None, None],
+                    mamba_cache_params.ssm_state[state_indices_tensor_p], 0)
+            varlen_state = mamba_chunk_scan_combined(
+                hidden_states_p.view(1, num_prefill_tokens,
+                                     self.num_heads // self.tp_size,
+                                     self.head_dim),
+                dt.unsqueeze(0),
+                self.A,
+                B.view(1, num_prefill_tokens, 1, -1),
+                C.view(1, num_prefill_tokens, 1, -1),
+                chunk_size=mamba2_metadata.chunk_size,
+                D=self.D,
+                z=gate_p.view(1, num_prefill_tokens,
+                              self.num_heads // self.tp_size, self.head_dim),
+                dt_bias=self.dt_bias,
+                seq_idx=mamba2_metadata.seq_idx,
+                chunk_indices=mamba2_metadata.chunk_indices,
+                chunk_offsets=mamba2_metadata.chunk_offsets,
+                cu_seqlens=attn_metadata.query_start_loc[:num_prefills + 1],
+                initial_states=initial_states,
+                return_varlen_states=True,
+                return_final_states=False,
+                dt_softplus=True,
+                dt_limit=(0.0, float("inf")),
+                out=preallocated_ssm_out_p.view(1, num_prefill_tokens, -1,
+                                                self.head_dim),
+            )
+
+            # update ssm states
+            # - varlen state is a (batch, nheads, headdim, dstate) tensor
+            mamba_cache_params.ssm_state[state_indices_tensor_p] = varlen_state
+
+        # Process decode requests
+        if has_decode:
+            # 2. Convolution sequence transformation
+            hidden_states_d = causal_conv1d_update(
+                hidden_states_d,
+                mamba_cache_params.conv_state,
+                conv_weights,
+                self.conv1d.bias,
+                self.activation,
+                conv_state_indices=state_indices_tensor_d)
+
+            B, C, dt = self._project_ssm_parameters(hidden_states_d)
+
+            # 3. State Space Model sequence transformation
+            A = self.A[:, None, ...][:, :,
+                                     None].expand(-1, self.head_dim,
+                                                  self.config.mamba_d_state)
+            dt = dt[:, :, None].expand(-1, -1, self.head_dim)
+            dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim)
+            D = self.D[:, None, ...].expand(-1, self.head_dim)
+            B = B.unsqueeze(1)
+            C = C.unsqueeze(1)
+            hidden_states_d = hidden_states_d.view(
+                -1, self.num_heads // self.tp_size, self.head_dim)
+
+            # - the hidden is reshaped into (bs, num_heads, head_dim)
+            # - mamba_cache_params.ssm_state's slots will be selected
+            #   using state_indices_tensor_d
+            selective_state_update(
+                mamba_cache_params.ssm_state,
+                hidden_states_d,
+                dt,
+                A,
+                B,
+                C,
+                D,
+                z=gate_d.reshape(num_decodes, -1, self.head_dim),
+                dt_bias=dt_bias,
+                dt_softplus=True,
+                state_batch_indices=state_indices_tensor_d,
+                out=preallocated_ssm_out_d.view(num_decodes, -1,
+                                                self.head_dim),
+            )
+            assert self.num_heads % self.tp_size == 0
+
+        # 4. Final linear projection
+        out = self.out_proj(preallocated_ssm_out)
+        return out
+
+
+class DenseMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: Plamo2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_up_proj = MergedColumnParallelLinear(
+            self.hidden_size,
+            [self.intermediate_size] * 2,
+            bias=False,
+            prefix=f"{prefix}.gate_up_proj",
+            quant_config=quant_config,
+            return_bias=False,
+        )
+        self.act = SiluAndMul()
+        self.down_proj = RowParallelLinear(self.intermediate_size,
+                                           self.hidden_size,
+                                           bias=False,
+                                           prefix=f"{prefix}.down_proj",
+                                           quant_config=quant_config,
+                                           return_bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        h = self.gate_up_proj(hidden_states)
+        h = self.act(h)
+        return self.down_proj(h)
+
+
+@support_torch_compile
+class Plamo2AttentionMixer(nn.Module):
+
+    def __init__(self,
+                 *,
+                 vllm_config: VllmConfig,
+                 prefix: str = "",
+                 **kwargs) -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.hidden_size = config.hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = config.num_key_value_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = config.hidden_size_per_head
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            config.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
+                                        config.hidden_size,
+                                        bias=False,
+                                        quant_config=quant_config)
+
+        self.rope_theta = config.rope_theta if hasattr(config,
+                                                       "rope_theta") else 10000
+        self.rope_scaling = config.rope_scaling if hasattr(
+            config, "rope_scaling") else None
+        max_position = config.max_position_embeddings
+        if hasattr(vllm_config.model_config, "max_model_len") and isinstance(
+                vllm_config.model_config.max_model_len, int):
+            max_position = min(max_position,
+                               vllm_config.model_config.max_model_len)
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=self.rope_theta,
+            rope_scaling=self.rope_scaling,
+        )
+        self.q_norm = RMSNorm(config.hidden_size_per_head,
+                              eps=config.rms_norm_eps)
+        self.q_norm.weight = torch.nn.Parameter(
+            torch.ones((self.num_heads, config.hidden_size_per_head)))
+        set_weight_attrs(self.q_norm.weight,
+                         {"weight_loader": sharded_weight_loader(0)})
+        self.k_norm = RMSNorm(config.hidden_size_per_head,
+                              eps=config.rms_norm_eps)
+        self.k_norm.weight = torch.nn.Parameter(
+            torch.ones((self.num_kv_heads, config.hidden_size_per_head)))
+        # Tensor-parallelism shards the K norm weights to the tp ranks
+        # in a head-wise manner. This approach does not work if there is only
+        # a single KV head, as is the case for PLaMo 2-1B.
+        if self.total_num_kv_heads != 1:
+            set_weight_attrs(self.k_norm.weight,
+                             {"weight_loader": sharded_weight_loader(0)})
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        q_shape = q.shape
+        q = q.reshape(q_shape[:-1] + self.q_norm.weight.shape)
+        q = self.q_norm.forward_native(q).reshape(q_shape)
+        k_shape = k.shape
+        k = k.reshape(k_shape[:-1] + self.k_norm.weight.shape)
+        k = self.k_norm.forward_native(k).reshape(k_shape)
+
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Plamo2DecoderLayer(nn.Module):
+
+    def __init__(self,
+                 vllm_config: VllmConfig,
+                 layer_idx: int,
+                 prefix: str = "",
+                 **kwargs) -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+
+        self.is_mamba = is_mamba(config, layer_idx)
+        if self.is_mamba:
+            self.mixer = Plamo2MambaMixer(vllm_config=vllm_config,
+                                          prefix=f"{prefix}.mixer")
+        else:
+            self.mixer = Plamo2AttentionMixer(vllm_config=vllm_config,
+                                              prefix=f"{prefix}.mixer")
+
+        self.mlp = DenseMLP(config=config,
+                            quant_config=quant_config,
+                            prefix=f"{prefix}.mlp")
+        self.pre_mixer_norm = RMSNorm(config.hidden_size,
+                                      eps=config.rms_norm_eps)
+        self.post_mixer_norm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.pre_mlp_norm = RMSNorm(config.hidden_size,
+                                    eps=config.rms_norm_eps)
+        self.post_mlp_norm = RMSNorm(config.hidden_size,
+                                     eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        mamba_cache_params: MambaCacheParams,
+        mamba2_metadata: Mamba2Metadata,
+        **kwargs,
+    ):
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.pre_mixer_norm(hidden_states)
+        else:
+            hidden_states, residual = self.pre_mixer_norm(
+                hidden_states, residual)
+
+        hidden_states = self.mixer(
+            positions=positions,
+            hidden_states=hidden_states,
+            mamba_cache_params=mamba_cache_params,
+            mamba2_metadata=mamba2_metadata,
+        )
+        hidden_states = self.post_mixer_norm(hidden_states)
+        # Fully Connected
+        hidden_states, residual = self.pre_mlp_norm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.post_mlp_norm(hidden_states)
+        return hidden_states, residual
+
+
+class Plamo2Decoder(torch.nn.Module):
+
+    def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        extra_kwargs = {"is_lora_enabled": bool(vllm_config.lora_config)}
+
+        def get_layer(prefix: str):
+            layer_idx = int(prefix.rsplit(".", 1)[1])
+            return Plamo2DecoderLayer(vllm_config=vllm_config,
+                                      layer_idx=layer_idx,
+                                      prefix=prefix,
+                                      **extra_kwargs)
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+        mamba_cache_params: MambaCacheParams,
+        mamba2_metadata: Mamba2Metadata,
+    ) -> torch.Tensor:
+        mamba_cache_index = 0
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            layer_mamba_cache_params = None
+            if layer.is_mamba:
+                layer_mamba_cache_params = mamba_cache_params.at_layer_idx(
+                    mamba_cache_index)
+                mamba_cache_index += 1
+
+            hidden_states, residual = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                residual=residual,
+                mamba_cache_params=layer_mamba_cache_params,
+                mamba2_metadata=mamba2_metadata,
+            )
+        return hidden_states, residual
+
+
+class Plamo2Model(Plamo2PreTrainedModel):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config.model_config.hf_config)
+
+        config = vllm_config.model_config.hf_config
+
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.org_vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            prefix=f"{prefix}.embed_tokens",
+        )
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+        self.layers = Plamo2Decoder(vllm_config, prefix=f"{prefix}.layers")
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_init()
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        mamba_cache_params: MambaCacheParams,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        attn_metadata: AttentionMetadata = get_forward_context().attn_metadata
+        mamba2_metadata = prepare_mamba2_metadata(
+            chunk_size=self.config.mamba_chunk_size,
+            attn_metadata=attn_metadata,
+        )
+
+        hidden_states, residual = self.layers(
+            positions=positions,
+            hidden_states=hidden_states,
+            residual=residual,
+            mamba_cache_params=mamba_cache_params,
+            mamba2_metadata=mamba2_metadata,
+        )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class Plamo2ForCausalLM(Plamo2PreTrainedModel, HasInnerState, SupportsPP,
+                        IsHybrid, SupportsV0Only):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        config = vllm_config.model_config.hf_config
+        scheduler_config = vllm_config.scheduler_config
+        assert not vllm_config.cache_config.enable_prefix_caching, \
+            "PLaMo2 currently does not support prefix caching"
+
+        super().__init__(config)
+        self.config = config
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+        self.scheduler_config = scheduler_config
+
+        # ModelConfig.get_head_size assumes head_dim is set or calculated as
+        # hidden_size // num_attention_heads. However, this is not always
+        # the case for PLaMo2, as indicated by the FIXME comment.
+        self.config.head_dim = self.config.hidden_size_per_head
+
+        self.model = Plamo2Model(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "model"))
+        self.vocab_size = self.config.vocab_size
+        self.unpadded_vocab_size = self.config.vocab_size
+        num_embeddings = ((self.vocab_size + 15) // 16) * 16
+        self.lm_head = ParallelLMHead(
+            num_embeddings,
+            self.config.hidden_size,
+            org_num_embeddings=self.config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+            prefix=f"{prefix}.lm_head",
+        )
+        if self.config.tie_word_embeddings:
+            self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
+
+        # Used to track and store by the Mamba cache between steps.
+        self.mamba_cache: Optional[MambaCacheManager] = None
+
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                self.config.vocab_size)
+        self.sampler = get_sampler()
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs):
+        if self.mamba_cache is None:
+            num_mamba_layers = self.model_config.get_num_layers_by_block_type(
+                self.vllm_config.parallel_config, LayerBlockType.mamba)
+
+            self.mamba_cache = MambaCacheManager(
+                self.vllm_config,
+                num_mamba_layers,
+                *self._get_mamba_cache_shape(),
+                self.lm_head.weight.dtype,
+                self.lm_head.weight.dtype,
+            )
+
+        mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
+
+        hidden_states = self.model(input_ids, positions, mamba_cache_params,
+                                   intermediate_tensors, inputs_embeds)
+        return hidden_states
+
+    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
+        return self.mamba_cache.copy_inputs_before_cuda_graphs(
+            input_buffers, **kwargs)
+
+    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
+        return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
+
+    def _get_mamba_cache_shape(
+            self) -> tuple[tuple[int, int], tuple[int, int, int]]:
+        world_size = get_tensor_model_parallel_world_size()
+        hidden_size = (self.config.mamba_num_heads *
+                       self.config.hidden_size_per_head)
+        conv_state_shape = (
+            hidden_size // world_size,
+            self.config.mamba_d_conv - 1,
+        )
+        temporal_state_shape = (
+            divide(self.config.mamba_num_heads, world_size),
+            self.config.hidden_size_per_head,
+            self.config.mamba_d_state,
+        )
+        return conv_state_shape, temporal_state_shape
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: Optional[torch.Tensor],
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+
+            # Both tie_word_embeddings=True and lm_head.weight in the safetensor
+            # at the same time causes dict key access error.
+            if name == "lm_head.weight" and self.config.tie_word_embeddings:
+                assert "lm_head.weight" not in params_dict
+                continue
+
+            # Update the weight names to be compatible with the vllm version
+            # of the model.
+            # Do not change the order of the replacements.
+            replacements = {
+                # Rename incompatible weight names.
+                ".A_log": ".A",
+                ".B_norm_weight": ".B_norm.weight",
+                ".C_norm_weight": ".C_norm.weight",
+                ".dt_norm_weight": ".dt_norm.weight",
+                ".q_weight": ".q_norm.weight",
+                ".k_weight": ".k_norm.weight",
+            }
+            # Apply replacements based on the defined mappings
+            for old, new in replacements.items():
+                if old in name:
+                    name = name.replace(old, new)
+
+            # Reshape the in_proj weights to match the shape expected
+            # by MergedColumnParallelLinear.
+            # This works both for unquantized weights and
+            # for quantized weights.
+            # In the quantized case, the weights are already transposed.
+            # Also, in addition to the quantized weights,
+            # the zero points and scales have to be reshaped as well.
+            # Packing should not be affected by this.
+            if ".mixer.in_proj.weight" in name \
+                or "mixer.in_proj.qweight" in name \
+                or "mixer.in_proj.scales" in name \
+                or "mixer.in_proj.qzeros" in name:
+                if "mixer.in_proj.weight" in name:
+                    loaded_weight = loaded_weight.transpose(0, 1)
+                # for weight:
+                # loaded_weight.shape[0] == self.config.hidden_size
+                # for qweight:
+                # loaded_weight.shape[0] == self.config.hidden_size // param.pack_factor  # noqa
+                # for scales and qzeros:
+                # loaded_weight.shape[0] == self.config.hidden_size // self.vllm_config.quant_config.group_size  # noqa
+                loaded_weight = loaded_weight.reshape(
+                    loaded_weight.shape[0], self.config.mamba_num_heads, -1)
+                gate_weight, hidden_states_weight = loaded_weight.chunk(2,
+                                                                        dim=-1)
+                gate_weight = gate_weight.reshape(loaded_weight.shape[0], -1)
+                hidden_states_weight = hidden_states_weight.reshape(
+                    loaded_weight.shape[0], -1)
+                loaded_weight = torch.cat([gate_weight, hidden_states_weight],
+                                          dim=-1)
+                if "mixer.in_proj.weight" in name:
+                    loaded_weight = loaded_weight.transpose(0, 1)
+
+            # Offset parameter with vllm's RMSNorm haven't been supported yet.
+            if ".pre_mixer_norm" in name:
+                loaded_weight += 1.0
+            elif ".post_mixer_norm" in name:
+                loaded_weight += 1.0 / 5
+            elif ".pre_mlp_norm" in name:
+                loaded_weight += 1.0
+            elif ".post_mlp_norm" in name:
+                loaded_weight += 1.0 / (5**1.5)
+            elif "model.norm.weight" in name:
+                loaded_weight += 1.0
+
+            # Skip layers on other devices.
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/prithvi_geospatial_mae.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/prithvi_geospatial_mae.py
new file mode 100644
index 0000000000000000000000000000000000000000..68488829071faa0113aadb27aa7efbd5f36896ea
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/prithvi_geospatial_mae.py
@@ -0,0 +1,284 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2025 The vLLM team.
+# Copyright 2025 IBM.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only IBM/NASA Prithvi Geospatial model."""
+
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+from transformers import BatchFeature
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.interfaces import (
+    IsAttentionFree, MultiModalEmbeddings, SupportsMultiModalWithRawInput,
+    default_pooling_type)
+from vllm.model_executor.models.utils import AutoWeightsLoader
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalFieldElem, MultiModalInputs,
+                                    MultiModalKwargs, MultiModalKwargsItem,
+                                    MultiModalSharedField, PlaceholderRange)
+from vllm.multimodal.parse import MultiModalDataItems
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+
+
+class PrithviGeoSpatialMAEProcessingInfo(BaseProcessingInfo):
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+
+class PrithviGeoSpatialMAEInputBuilder(
+        BaseDummyInputsBuilder[PrithviGeoSpatialMAEProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        return ""
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        # This model input is fixed and is in the form of a torch Tensor.
+        # The size of pixel_values might change in the cases where we resize
+        # the input but never exceeds the dimensions below.
+        return {
+            "pixel_values": torch.full((6, 512, 512), 1.0,
+                                       dtype=torch.float16),
+            "location_coords": torch.full((1, 2), 1.0, dtype=torch.float16),
+        }
+
+
+class PrithviGeoSpatialMAEMultiModalProcessor(BaseMultiModalProcessor):
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            pixel_values=MultiModalFieldConfig.shared(batch_size=1,
+                                                      modality="image"),
+            location_coords=MultiModalFieldConfig.shared(batch_size=1,
+                                                         modality="image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        return []
+
+    def apply(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data: MultiModalDataDict,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        tokenization_kwargs: Optional[Mapping[str, object]] = None,
+        return_mm_hashes: bool = False,
+    ) -> MultiModalInputs:
+        mm_kwargs = {}
+
+        for k, v in mm_data.items():
+            if isinstance(v, dict) and k == "image":
+                mm_kwargs.update(v)
+            else:
+                mm_kwargs[k] = v
+        mm_placeholders = {"image": [PlaceholderRange(offset=0, length=0)]}
+
+        # This model receives in input a multi-dimensional tensor representing
+        # a single image patch and therefore it is not to be split
+        # into multiple elements, but rather to be considered a single one.
+        # Hence, the decision of using a MultiModalSharedField.
+        # The expected shape is (num_channels, width, height).
+
+        # This model however allows the user to also submit multiple image
+        # patches as a batch, adding a further dimension to the above shape.
+        # At this stage we only support submitting one patch per request and
+        # batching is achieved via vLLM batching.
+        # TODO (christian-pinto): enable support for multi patch requests
+        # in tandem with vLLM batching.
+        multimodal_kwargs_items = [
+            MultiModalKwargsItem.from_elems([
+                MultiModalFieldElem(
+                    modality="image",
+                    key=key,
+                    data=data,
+                    field=MultiModalSharedField(1),
+                ) for key, data in mm_kwargs.items()
+            ])
+        ]
+
+        return MultiModalInputs(
+            type="multimodal",
+            prompt=prompt,
+            prompt_token_ids=[1],
+            mm_kwargs=MultiModalKwargs(multimodal_kwargs_items),
+            mm_hashes=None,
+            mm_placeholders=mm_placeholders,
+        )
+
+
+@default_pooling_type("All")
+@MULTIMODAL_REGISTRY.register_processor(
+    PrithviGeoSpatialMAEMultiModalProcessor,
+    info=PrithviGeoSpatialMAEProcessingInfo,
+    dummy_inputs=PrithviGeoSpatialMAEInputBuilder,
+)
+class PrithviGeoSpatialMAE(nn.Module, IsAttentionFree,
+                           SupportsMultiModalWithRawInput):
+    """Prithvi Masked Autoencoder"""
+
+    is_pooling_model = True
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return None
+
+        raise ValueError("Only image modality is supported")
+
+    def _instantiate_model(self, config: dict) -> Optional[nn.Module]:
+        # We might be able/need to support different tasks with this same model
+        if config["task_args"]["task"] == "SemanticSegmentationTask":
+            from terratorch.cli_tools import SemanticSegmentationTask
+
+            task = SemanticSegmentationTask(
+                config["model_args"],
+                config["task_args"]["model_factory"],
+                loss=config["task_args"]["loss"],
+                lr=config["task_args"]["lr"],
+                ignore_index=config["task_args"]["ignore_index"],
+                optimizer=config["task_args"]["optimizer"],
+                optimizer_hparams=config["optimizer_params"],
+                scheduler=config["task_args"]["scheduler"],
+                scheduler_hparams=config["scheduler_params"],
+                plot_on_val=config["task_args"]["plot_on_val"],
+                freeze_decoder=config["task_args"]["freeze_decoder"],
+                freeze_backbone=config["task_args"]["freeze_backbone"],
+            )
+
+            return task.model
+        else:
+            return None
+
+    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        # the actual model is dynamically instantiated using terratorch
+        # allowing us to perform changes to the model architecture
+        # at startup time (e.g., change the model decoder class.)
+        self.model = self._instantiate_model(
+            vllm_config.model_config.hf_config.to_dict()["pretrained_cfg"])
+        if self.model is None:
+            raise ValueError(
+                "Unsupported task. "
+                "Only SemanticSegmentationTask is supported for now "
+                "by PrithviGeospatialMAE.")
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.pooler = DispatchPooler(
+            {"encode": Pooler.for_encode(pooler_config)}, )
+
+    def _parse_and_validate_multimodal_data(
+            self, **kwargs) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        if not isinstance(pixel_values, torch.Tensor):
+            raise ValueError(f"Incorrect type of pixel_values. "
+                             f"Got type: {type(pixel_values)}")
+
+        location_coords = kwargs.pop("location_coords", None)
+        if not isinstance(location_coords, torch.Tensor):
+            raise ValueError(f"Incorrect type of location_coords. "
+                             f"Got type: {type(location_coords)}")
+        location_coords = torch.unbind(location_coords, dim=0)[0]
+        if location_coords.shape == torch.Size([0]):
+            location_coords = None
+
+        return pixel_values, location_coords
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        # We do not really use any input tokens and therefore no embeddings
+        # to be calculated. However, due to the mandatory token ids in
+        # the input prompt we pass one token and the size of the dummy
+        # embedding tensors must reflect that.
+        return torch.empty((input_ids.shape[0], 0))
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ):
+        pixel_values, location_coords = (
+            self._parse_and_validate_multimodal_data(**kwargs))
+        model_output = self.model(pixel_values,
+                                  location_coords=location_coords)
+
+        return model_output.output
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        params_list = []
+        model_buffers = dict(self.named_buffers())
+        loaded_buffers = []
+        for key, value in weights:
+            if key == "state_dict":
+                weights_to_parse = value
+                for name, weight in weights_to_parse.items():
+                    if "pos_embed" in name:
+                        continue
+
+                    if "_timm_module." in name:
+                        name = name.replace("_timm_module.", "")
+
+                    # this model requires a couple of buffers to be loaded
+                    # that are not loadable with the AutoWeightsLoader
+                    if name in model_buffers:
+                        if "_timm_module." in name:
+                            name = name.replace("_timm_module.", "")
+                        buffer = model_buffers[name]
+                        weight_loader = getattr(buffer, "weight_loader",
+                                                default_weight_loader)
+                        weight_loader(buffer, weight)
+                        loaded_buffers.append(name)
+                    else:
+                        params_list.append((name, weight))
+                break
+
+        # Load the remaining model parameters
+        loader = AutoWeightsLoader(self)
+        autoloaded_weights = loader.load_weights(params_list)
+
+        return autoloaded_weights.union(set(loaded_buffers))
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_5_omni_thinker.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_5_omni_thinker.py
new file mode 100644
index 0000000000000000000000000000000000000000..e95295c31885a05eddca61dd4107df86bdaa5acf
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_5_omni_thinker.py
@@ -0,0 +1,913 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Qwen2.5-Omni model (thinker part)."""
+
+from collections.abc import Iterable, Mapping, Sequence
+from copy import copy
+from functools import partial
+from typing import Any, Optional, Union
+
+import torch
+import torch.nn as nn
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.models.qwen2_5_omni.configuration_qwen2_5_omni import (
+    Qwen2_5OmniConfig, Qwen2_5OmniThinkerConfig)
+from transformers.models.qwen2_5_omni.modeling_qwen2_5_omni import (
+    Qwen2_5OmniAudioEncoder)
+from transformers.models.qwen2_5_omni.processing_qwen2_5_omni import (
+    Qwen2_5OmniProcessor)
+from transformers.models.whisper import WhisperFeatureExtractor
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
+from vllm.model_executor.models.qwen2_5_vl import (
+    Qwen2_5_VisionTransformer, Qwen2_5_VLImageEmbeddingInputs,
+    Qwen2_5_VLImageInputs, Qwen2_5_VLImagePixelInputs,
+    Qwen2_5_VLProcessingInfo, Qwen2_5_VLVideoEmbeddingInputs,
+    Qwen2_5_VLVideoInputs, Qwen2_5_VLVideoPixelInputs)
+from vllm.model_executor.models.qwen2_audio import (
+    Qwen2AudioInputs, Qwen2AudioProcessingInfo,
+    _get_feat_extract_output_lengths)
+from vllm.model_executor.models.qwen2_vl import Qwen2VLMultiModalDataParser
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (ImageItem, ModalityData,
+                                    MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs, NestedTensors)
+from vllm.multimodal.parse import (AudioProcessorItems, DictEmbeddingItems,
+                                   ModalityDataItems, MultiModalDataItems,
+                                   MultiModalDataParser)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        PlaceholderFeaturesInfo,
+                                        PromptReplacement, PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.tokenizer import decode_tokens, encode_tokens
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .utils import (AutoWeightsLoader, WeightsMapper,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+try:
+    import flash_attn
+except (ImportError, ModuleNotFoundError):
+    flash_attn = None
+
+logger = init_logger(__name__)
+
+
+def _qwen2_5_omni_thinker_field_config(hf_inputs: Mapping[str, torch.Tensor]):
+    audio_feature_lengths = hf_inputs.get("audio_feature_lengths",
+                                          torch.empty((0, )))
+
+    image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
+    image_grid_sizes = image_grid_thw.prod(-1)
+
+    video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
+    video_grid_sizes = video_grid_thw.prod(-1)
+
+    return dict(
+        input_audio_features=MultiModalFieldConfig.flat_from_sizes(
+            "audio", audio_feature_lengths, dim=1),
+        feature_attention_mask=MultiModalFieldConfig.batched("audio"),
+        audio_feature_lengths=MultiModalFieldConfig.batched("audio"),
+        pixel_values=MultiModalFieldConfig.flat_from_sizes(
+            "image", image_grid_sizes),
+        image_embeds=MultiModalFieldConfig.flat_from_sizes(
+            "image", image_grid_sizes),
+        image_grid_thw=MultiModalFieldConfig.batched("image"),
+        pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
+            "video", video_grid_sizes),
+        video_embeds=MultiModalFieldConfig.flat_from_sizes(
+            "video", video_grid_sizes),
+        video_grid_thw=MultiModalFieldConfig.batched("video"),
+        second_per_grid_ts=MultiModalFieldConfig.batched("video"),
+    )
+
+
+class Qwen2_5OmniThinkerMultiModalDataParser(Qwen2VLMultiModalDataParser):
+
+    def _parse_audio_data(
+        self,
+        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
+    ) -> ModalityDataItems[Any, Any]:
+        if isinstance(data, dict):
+            return DictEmbeddingItems(
+                data,
+                modality="audio",
+                required_fields={
+                    "input_audio_features", "audio_feature_lengths"
+                },
+                fields_factory=_qwen2_5_omni_thinker_field_config,
+            )
+
+        return super()._parse_audio_data(data)
+
+
+class Qwen2_5OmniThinkerProcessingInfo(Qwen2AudioProcessingInfo,
+                                       Qwen2_5_VLProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Qwen2_5OmniConfig).thinker_config
+
+    def get_hf_processor(self, **kwargs: object) -> Qwen2_5OmniProcessor:
+        return self.ctx.get_hf_processor(
+            Qwen2_5OmniProcessor,
+            use_fast=kwargs.pop("use_fast", True),
+            **kwargs,
+        )
+
+    def get_feature_extractor(self, **kwargs: object):
+        hf_processor = self.get_hf_processor(**kwargs)
+        feature_extractor = hf_processor.feature_extractor  # type: ignore
+        assert isinstance(feature_extractor, WhisperFeatureExtractor)
+        return feature_extractor
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"audio": None, "image": None, "video": None}
+
+
+class Qwen2_5OmniThinkerDummyInputsBuilder(
+        BaseDummyInputsBuilder[Qwen2_5OmniThinkerProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_audios = mm_counts.get("audio", 0)
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        hf_processor = self.info.get_hf_processor()
+
+        audio_token: str = hf_processor.audio_token
+        image_token: str = hf_processor.image_token
+        video_token: str = hf_processor.video_token
+
+        return (audio_token * num_audios + image_token * num_images +
+                video_token * num_videos)
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_audios = mm_counts.get("audio", 0)
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        feature_extractor = self.info.get_feature_extractor()
+
+        target_audio_length = min(
+            feature_extractor.chunk_length,
+            30,
+        ) * feature_extractor.sampling_rate
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+        target_num_frames = \
+            self.info.get_num_frames_with_most_features(seq_len, mm_counts)
+
+        mm_data = {
+            "audio":
+            self._get_dummy_audios(length=target_audio_length,
+                                   num_audios=num_audios),
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images),
+            "video":
+            self._get_dummy_videos(width=target_width,
+                                   height=target_height,
+                                   num_frames=target_num_frames,
+                                   num_videos=num_videos),
+        }
+
+        return mm_data
+
+
+class Qwen2_5OmniThinkerMultiModalProcessor(
+        BaseMultiModalProcessor[Qwen2_5OmniThinkerProcessingInfo]):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        feature_extractor = self.info.get_feature_extractor()
+        return Qwen2_5OmniThinkerMultiModalDataParser(
+            target_sr=feature_extractor.sampling_rate)
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        mm_data = dict(mm_data)
+        audios = mm_data.pop("audios", [])
+
+        # NOTE: WhisperFeatureExtractor cannot handle empty list of audios
+        if audios:
+            # NOTE: Qwen2.5-Omni processor accept "audio"
+            mm_data["audio"] = audios
+            mm_kwargs = dict(**mm_kwargs, )
+
+        hf_inputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+
+        input_features = hf_inputs.pop('input_features', None)
+        feature_attention_mask = hf_inputs.get('feature_attention_mask', None)
+        if ('input_audio_features' not in hf_inputs
+                and input_features is not None):
+            if feature_attention_mask is not None:
+                input_features = input_features.permute(
+                    0, 2, 1)[feature_attention_mask.bool()].permute(1, 0)
+            hf_inputs['input_audio_features'] = input_features
+        if ('audio_feature_lengths' not in hf_inputs
+                and feature_attention_mask is not None):
+            hf_inputs['audio_feature_lengths'] = feature_attention_mask.sum(-1)
+        return hf_inputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return _qwen2_5_omni_thinker_field_config(hf_inputs)
+
+    def _maybe_apply_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        prompt_ids: list[int],
+        mm_kwargs: MultiModalKwargs,
+        is_update_applied: bool,
+    ) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
+        """
+        Qwen2.5-Omni reimplements this function to handle `use_audio_in_video`.
+        """
+        unbound_prompt_updates = self._get_prompt_updates(
+            mm_items,
+            hf_processor_mm_kwargs,
+            mm_kwargs,
+        )
+        mm_prompt_updates = self._bind_and_group_updates(
+            unbound_prompt_updates)
+
+        mm_item_counts = mm_items.get_all_counts()
+        self._validate_mm_kwargs(mm_kwargs, mm_item_counts)
+
+        use_audio_in_video = hf_processor_mm_kwargs.get(
+            "use_audio_in_video", False)
+
+        if is_update_applied:
+            mm_placeholders = self._find_mm_placeholders(
+                mm_prompt_updates,
+                prompt_ids,
+                mm_item_counts,
+            )
+            self._validate_mm_placeholders(
+                mm_placeholders,
+                mm_item_counts,
+                use_audio_in_video=use_audio_in_video)
+
+            tokenizer = self.info.get_tokenizer()
+            prompt = decode_tokens(tokenizer, prompt_ids)
+        else:
+            (
+                prompt_ids,
+                prompt,
+                mm_placeholders,
+            ) = self._apply_prompt_updates(
+                prompt_ids,
+                mm_prompt_updates,
+                mm_item_counts,
+            )
+            self._validate_mm_placeholders(
+                mm_placeholders,
+                mm_item_counts,
+                use_audio_in_video=use_audio_in_video)
+
+        tokenizer = self.info.get_tokenizer()
+        prompt = decode_tokens(tokenizer, prompt_ids)
+
+        if use_audio_in_video:
+            mm_kwargs["use_audio_in_video"] = True
+
+        return prompt_ids, prompt, mm_placeholders
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        tokenizer = self.info.get_tokenizer()
+        image_processor = self.info.get_image_processor(
+            **hf_processor_mm_kwargs)
+        vocab = tokenizer.get_vocab()
+
+        audio_token = processor.audio_token
+        image_token = processor.image_token
+        video_token = processor.video_token
+        audio_token_id = vocab[audio_token]
+        image_token_id = vocab[image_token]
+        video_token_id = vocab[video_token]
+
+        audio_feature_lengths = out_mm_kwargs.get("audio_feature_lengths")
+        feature_attention_mask = out_mm_kwargs.get("feature_attention_mask")
+        if audio_feature_lengths is None and feature_attention_mask is None:
+            audio_output_lengths = []
+        elif audio_feature_lengths is not None:
+            _, audio_output_lens = _get_feat_extract_output_lengths(
+                audio_feature_lengths)
+            audio_output_lengths = audio_output_lens.tolist()
+        elif feature_attention_mask is not None:
+            assert isinstance(feature_attention_mask, torch.Tensor)
+            _, audio_output_lens = _get_feat_extract_output_lengths(
+                feature_attention_mask.sum(-1))
+            audio_output_lengths = audio_output_lens.tolist()
+
+        # number of audios read from video.
+        audio_in_video_item_idx = 0
+
+        def get_replacement_qwen2_audio(item_idx: int):
+            item_idx += audio_in_video_item_idx
+
+            num_features = audio_output_lengths[item_idx]
+            if num_features == 0:
+                audios = mm_items.get_items("audio", AudioProcessorItems)
+                audio = audios.get(item_idx)
+                raise ValueError(
+                    f"The audio {audio} (len={len(audio)}) is too short "
+                    "to be represented inside the model")
+
+            return [audio_token_id] * num_features
+
+        def get_replacement_qwen2_vision(item_idx: int, modality: str):
+            grid_thw = out_mm_kwargs[f"{modality}_grid_thw"][item_idx]
+            assert isinstance(grid_thw, torch.Tensor)
+            merge_length = image_processor.merge_size**2
+
+            token_id = image_token_id if modality == "image" else video_token_id
+            return [token_id] * (int(grid_thw.prod()) // merge_length)
+
+        use_audio_in_video = hf_processor_mm_kwargs.get(
+            "use_audio_in_video", False)
+        thinker_config = self.info.get_hf_config()
+
+        def get_replacement_qwen2_use_audio_in_video(item_idx: int):
+            nonlocal audio_in_video_item_idx
+
+            audio_num_features = audio_output_lengths[audio_in_video_item_idx +
+                                                      item_idx]
+            video_grid_thw = out_mm_kwargs["video_grid_thw"][item_idx]
+
+            audio_in_video_item_idx += 1
+
+            second_per_grid_ts = hf_processor_mm_kwargs.get(
+                "second_per_grid_ts", None)
+            if second_per_grid_ts:
+                video_second_per_grid_t = second_per_grid_ts[item_idx]
+            else:
+                video_second_per_grid_t = 1.0
+
+            return MRotaryEmbedding.omni_get_updates_use_audio_in_video(
+                thinker_config=thinker_config,
+                audio_len=audio_num_features,
+                video_grid_thw=video_grid_thw,
+                video_second_per_grid_t=video_second_per_grid_t,
+            )
+
+        video_replacement_fn = (
+            get_replacement_qwen2_use_audio_in_video if use_audio_in_video else
+            partial(get_replacement_qwen2_vision, modality="video"))
+
+        return [
+            PromptReplacement(
+                modality="audio",
+                target=audio_token,
+                replacement=get_replacement_qwen2_audio,
+            ),
+            PromptReplacement(
+                modality="image",
+                target=image_token,
+                replacement=partial(get_replacement_qwen2_vision,
+                                    modality="image"),
+            ),
+            PromptReplacement(
+                modality="video",
+                target=video_token,
+                replacement=video_replacement_fn,
+            ),
+        ]
+
+    def _apply_hf_processor_main(
+        self,
+        prompt: Union[str, list[int]],
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        tokenization_kwargs: Mapping[str, object],
+        *,
+        enable_hf_prompt_update: bool,
+    ) -> tuple[list[int], BatchFeature, bool]:
+        """
+        Qwen2.5-Omni reimplements this function to handle text only.
+        """
+        if isinstance(prompt, str):
+            if enable_hf_prompt_update:
+                return self._apply_hf_processor_text_mm(
+                    prompt_text=prompt,
+                    mm_items=mm_items,
+                    hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+                    tokenization_kwargs=tokenization_kwargs,
+                )
+            tokenizer = self.info.get_tokenizer()
+            prompt_ids = encode_tokens(tokenizer, prompt)
+        else:
+            prompt_ids = self._apply_hf_processor_tokens_only(prompt)
+
+        mm_processed_data = self._apply_hf_processor_mm_only(
+            mm_items=mm_items,
+            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+            tokenization_kwargs=tokenization_kwargs,
+        )
+
+        return prompt_ids, mm_processed_data, False
+
+    def _apply_hf_processor_mm_only(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        tokenization_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        """
+        Qwen2.5-Omni reimplements this function to handle `use_audio_in_video`.
+        """
+        mm_counts = mm_items.get_all_counts()
+
+        use_audio_in_video = hf_processor_mm_kwargs.get(
+            "use_audio_in_video", False)
+        if use_audio_in_video and "video" in mm_counts:
+            assert "audio" in mm_counts
+            mm_counts["audio"] -= mm_counts["video"]
+
+        _, mm_processed_data, _ = self._apply_hf_processor_text_mm(
+            prompt_text=self.dummy_inputs.get_dummy_text(mm_counts),
+            mm_items=mm_items,
+            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+            tokenization_kwargs=tokenization_kwargs,
+        )
+
+        return mm_processed_data
+
+    def _validate_mm_placeholders(
+        self,
+        mm_placeholders: Mapping[str, list[PlaceholderFeaturesInfo]],
+        mm_item_counts: Mapping[str, int],
+        use_audio_in_video: bool = False,
+    ) -> None:
+        if use_audio_in_video:
+            mm_item_counts = copy(mm_item_counts)
+            if "video" in mm_item_counts:
+                assert "audio" in mm_item_counts
+                mm_item_counts["audio"] -= mm_item_counts["video"]
+        super()._validate_mm_placeholders(mm_placeholders, mm_item_counts)
+
+
+class Qwen2_5OmniConditionalGenerationMixin:
+
+    def _validate_and_reshape_mm_tensor(self,
+                                        mm_input: object,
+                                        name: str,
+                                        dim: int = 0) -> torch.Tensor:
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of {name}. "
+                             f"Got type: {type(mm_input)}")
+        if isinstance(mm_input, torch.Tensor):
+            return torch.concat(list(mm_input), dim=dim)
+        else:
+            return torch.concat(mm_input, dim=dim)
+
+    def _parse_and_validate_audio_input(
+            self, **kwargs: object) -> Optional[Qwen2AudioInputs]:
+        input_audio_features = kwargs.pop('input_audio_features', None)
+        audio_feature_lengths = kwargs.pop('audio_feature_lengths', None)
+        feature_attention_mask = kwargs.pop('feature_attention_mask', None)
+        if input_audio_features is None:
+            return None
+        input_audio_features = self._validate_and_reshape_mm_tensor(
+            input_audio_features, 'input_audio_features', dim=1)
+        if feature_attention_mask is not None:
+            feature_attention_mask = self._validate_and_reshape_mm_tensor(
+                feature_attention_mask, 'feature_attention_mask')
+        if not isinstance(input_audio_features, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of audio input features. "
+                             f"Got type: {type(input_audio_features)}")
+        return Qwen2AudioInputs(input_features=input_audio_features,
+                                audio_feature_lengths=audio_feature_lengths,
+                                feature_attention_mask=feature_attention_mask)
+
+    def _parse_and_validate_image_input(
+        self,
+        **kwargs: dict[str, Any],
+    ) -> Optional[Qwen2_5_VLImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            pixel_values = self._validate_and_reshape_mm_tensor(
+                pixel_values, "image pixel values")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            if not isinstance(pixel_values, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image pixel values. "
+                                 f"Got type: {type(pixel_values)}")
+
+            return Qwen2_5_VLImagePixelInputs(type="pixel_values",
+                                              pixel_values=pixel_values,
+                                              image_grid_thw=image_grid_thw)
+
+        if image_embeds is not None:
+            image_embeds = self._validate_and_reshape_mm_tensor(
+                image_embeds, "image embeds")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            if not isinstance(image_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+            return Qwen2_5_VLImageEmbeddingInputs(
+                type="image_embeds",
+                image_embeds=image_embeds,
+                image_grid_thw=image_grid_thw)
+
+    def _parse_and_validate_video_input(
+        self,
+        **kwargs: dict[str, Any],
+    ) -> Optional[Qwen2_5_VLVideoInputs]:
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
+        video_embeds = kwargs.pop("video_embeds", None)
+        video_grid_thw = kwargs.pop("video_grid_thw", None)
+
+        if pixel_values_videos is None and video_embeds is None:
+            return None
+
+        if pixel_values_videos is not None:
+            pixel_values_videos = self._validate_and_reshape_mm_tensor(
+                pixel_values_videos, "video pixel values")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            return Qwen2_5_VLVideoPixelInputs(
+                type="pixel_values_videos",
+                pixel_values_videos=pixel_values_videos,
+                video_grid_thw=video_grid_thw,
+            )
+
+        if video_embeds is not None:
+            video_embeds = self._validate_and_reshape_mm_tensor(
+                video_embeds, "video embeds")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            if not isinstance(video_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of video embeddings. "
+                                 f"Got type: {type(video_embeds)}")
+            return Qwen2_5_VLVideoEmbeddingInputs(
+                type="video_embeds",
+                video_embeds=video_embeds,
+                video_grid_thw=video_grid_thw)
+
+    def _process_audio_input(
+        self,
+        audio_input: Qwen2AudioInputs,
+        audio_hashes: list[str] = None,
+        cached_audio_features: torch.Tensor = None,
+    ) -> torch.Tensor:
+
+        input_features = audio_input["input_features"]
+        audio_feature_lengths = audio_input["audio_feature_lengths"]
+        if input_features.ndim == 3:
+            assert input_features.shape[0] == 1
+            input_features = input_features.squeeze(0)
+        if audio_feature_lengths.ndim == 2:
+            assert audio_feature_lengths.shape[
+                0] == 1 or audio_feature_lengths.shape[1] == 1
+            if audio_feature_lengths.shape[0] == 1:
+                audio_feature_lengths = audio_feature_lengths.squeeze(0)
+            else:
+                audio_feature_lengths = audio_feature_lengths.squeeze(1)
+
+        audio_feat_lengths, audio_output_lengths = (
+            self.audio_tower._get_feat_extract_output_lengths(
+                audio_feature_lengths))
+
+        audio_outputs = self.audio_tower(
+            input_features.to(self.audio_tower.dtype),
+            feature_lens=audio_feature_lengths,
+            aftercnn_lens=audio_feat_lengths,
+        )
+        audio_features = audio_outputs.last_hidden_state
+        return audio_features.split(audio_output_lengths.tolist())
+
+    def _process_image_input(
+            self,
+            image_input: Qwen2_5_VLImageInputs) -> tuple[torch.Tensor, ...]:
+        if image_input["type"] == "image_embeds":
+            return image_input["image_embeds"].type(self.visual.dtype)
+
+        grid_thw = image_input["image_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        pixel_values = image_input["pixel_values"].type(self.visual.dtype)
+        image_embeds = self.visual(pixel_values, grid_thw=grid_thw)
+        # Split concatenated embeddings for each image item.
+        merge_size = self.visual.spatial_merge_size
+        sizes = grid_thw.prod(-1) // merge_size // merge_size
+
+        return image_embeds.split(sizes.tolist())
+
+    def _process_video_input(
+            self,
+            video_input: Qwen2_5_VLVideoInputs,
+            video_hashes: list[str] = None,
+            cached_video_embeds: torch.Tensor = None) -> torch.Tensor:
+        if video_input["type"] == "video_embeds":
+            return video_input["video_embeds"].type(self.visual.dtype)
+
+        grid_thw = video_input["video_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        pixel_values_videos = video_input["pixel_values_videos"].type(
+            self.visual.dtype)
+        video_embeds = self.visual(pixel_values_videos, grid_thw=grid_thw)
+        # Split concatenated embeddings for each video item.
+        merge_size = self.visual.spatial_merge_size
+        sizes = grid_thw.prod(-1) // merge_size // merge_size
+
+        return video_embeds.split(sizes.tolist())
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Qwen2_5OmniThinkerMultiModalProcessor,
+    info=Qwen2_5OmniThinkerProcessingInfo,
+    dummy_inputs=Qwen2_5OmniThinkerDummyInputsBuilder,
+)
+class Qwen2_5OmniThinkerForConditionalGeneration(
+        nn.Module, SupportsMultiModal, SupportsPP,
+        Qwen2_5OmniConditionalGenerationMixin):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "thinker.lm_head.": "language_model.lm_head.",
+            "thinker.model.": "language_model.model.",
+            "thinker.": "",
+        })
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<|vision_start|><|IMAGE|><|vision_end|>"
+        if modality.startswith("video"):
+            return "<|vision_start|><|VIDEO|><|vision_end|>"
+        if modality.startswith("audio"):
+            return f"Audio {i}: <|audio_bos|><|AUDIO|><|audio_eos|>"
+
+        raise ValueError("Only image, video or audio modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        thinker_config: Qwen2_5OmniThinkerConfig = (
+            vllm_config.model_config.hf_config.thinker_config)
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = thinker_config
+        self.multimodal_config = multimodal_config
+
+        # force "use_flash_attention_2=True" to audio tower to align
+        # the results.
+        if flash_attn is not None:
+            audio_config = thinker_config.audio_config
+            audio_config._attn_implementation_autoset = True
+            audio_config._attn_implementation = "flash_attention_2"
+        else:
+            logger.warning(
+                "flash_attn is not available, the model may not yield the "
+                "exactly same result as the transformers implementation "
+                "in the audio tower part.")
+
+        if multimodal_config.get_limit_per_prompt("audio"):
+            self.audio_tower = Qwen2_5OmniAudioEncoder(
+                thinker_config.audio_config)
+        else:
+            self.audio_tower = None
+
+        if multimodal_config.get_limit_per_prompt(
+                "image") or multimodal_config.get_limit_per_prompt("video"):
+            self.visual = Qwen2_5_VisionTransformer(
+                vision_config=thinker_config.vision_config,
+                norm_eps=getattr(thinker_config.text_config, "rms_norm_eps",
+                                 1e-6),
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "visual"),
+            )
+        else:
+            self.visual = None
+
+        self.quant_config = quant_config
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            hf_config=thinker_config.text_config,
+            architectures=["Qwen2ForCausalLM"],
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        mm_input_by_modality = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if input_key in ("pixel_values", "image_embeds"
+                             ) and "image" not in mm_input_by_modality:
+                mm_input_by_modality[
+                    "image"] = self._parse_and_validate_image_input(**kwargs)
+            if input_key in ("pixel_values_videos", "video_embeds"
+                             ) and "video" not in mm_input_by_modality:
+                mm_input_by_modality[
+                    "video"] = self._parse_and_validate_video_input(**kwargs)
+            if input_key in ("input_audio_features"
+                             ) and "audio" not in mm_input_by_modality:
+                mm_input_by_modality[
+                    "audio"] = self._parse_and_validate_audio_input(**kwargs)
+        return mm_input_by_modality
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+
+        mm_input_by_modality = self._parse_and_validate_multimodal_inputs(
+            **kwargs)
+        if not mm_input_by_modality:
+            return []
+
+        # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor correspoending to a multimodal data item (image or video).
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in mm_input_by_modality:
+            multimodal_input = mm_input_by_modality[modality]
+            if modality == "image":
+                vision_embeddings = self._process_image_input(multimodal_input)
+                multimodal_embeddings += vision_embeddings
+            if modality == "video":
+                video_embeddings = self._process_video_input(multimodal_input)
+                multimodal_embeddings += video_embeddings
+            if modality == "audio":
+                audio_embeddings = self._process_audio_input(multimodal_input)
+                multimodal_embeddings += audio_embeddings
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+
+            # TODO (ywang96): support overlapping modalitiy embeddings so that
+            # `use_audio_in_video` will work on V1.
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings, [
+                    self.config.image_token_index,
+                    self.config.video_token_index,
+                    self.config.audio_token_index
+                ])
+        return inputs_embeds
+
+    def get_multimodal_embeddings_v0(
+            self, **kwargs: object) -> Optional[NestedTensors]:
+        audio_input = self._parse_and_validate_audio_input(**kwargs)
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        video_input = self._parse_and_validate_video_input(**kwargs)
+
+        if audio_input is None and image_input is None and video_input is None:
+            return None
+
+        multimodal_embeddings: list[tuple[NestedTensors, str]] = []
+
+        if audio_input is not None:
+            audio_embeds = self._process_audio_input(audio_input)
+            multimodal_embeddings.append((audio_embeds, "audio"))
+        if image_input is not None:
+            image_embeds = self._process_image_input(image_input)
+            multimodal_embeddings.append((image_embeds, "image"))
+        if video_input is not None:
+            video_embeds = self._process_video_input(video_input)
+            multimodal_embeddings.append((video_embeds, "video"))
+        return multimodal_embeddings
+
+    def get_input_embeddings_v0(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
+            return inputs_embeds
+
+        for embeddings, modality in multimodal_embeddings:
+            if modality == "audio":
+                placeholder_token_id = self.config.audio_token_index
+            if modality == "image":
+                placeholder_token_id = self.config.image_token_index
+            if modality == "video":
+                placeholder_token_id = self.config.video_token_index
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, embeddings, placeholder_token_id)
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            multimodal_embeddings = self.get_multimodal_embeddings_v0(**kwargs)
+            inputs_embeds = self.get_input_embeddings_v0(
+                input_ids, multimodal_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model.model(input_ids,
+                                                  positions,
+                                                  intermediate_tensors,
+                                                  inputs_embeds=inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        skip_prefixes = ["talker.", "token2wav."]
+        if self.audio_tower is None:
+            skip_prefixes.extend(["audio_tower."])
+        if self.visual is None:
+            skip_prefixes.extend(["visual."])
+
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=skip_prefixes,
+        )
+        loaded_weights = loader.load_weights(weights,
+                                             mapper=self.hf_to_vllm_mapper)
+
+        return loaded_weights
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_5_vl.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_5_vl.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bcbcc4f0e37b4ad44e3cd1063e013244651a13c
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_5_vl.py
@@ -0,0 +1,1178 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py
+# Copyright 2025 The vLLM team.
+# Copyright 2025 The Qwen Team.
+# Copyright 2025 The HuggingFace Inc. team.
+# All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Qwen2.5-VL model compatible with HuggingFace weights."""
+from collections.abc import Iterable, Mapping
+from functools import lru_cache, partial
+from typing import Callable, Literal, Optional, TypedDict, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from transformers import BatchFeature
+from transformers.models.qwen2_5_vl import Qwen2_5_VLProcessor
+from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import (
+    Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig)
+
+from vllm.config import VllmConfig
+from vllm.distributed import parallel_state
+from vllm.distributed import utils as dist_utils
+from vllm.logger import init_logger
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.activation import get_act_and_mul_fn
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.gptq import GPTQConfig
+from vllm.model_executor.layers.quantization.gptq_marlin import (
+    GPTQMarlinConfig)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalFieldConfig
+from vllm.platforms import _Backend
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.config import uses_mrope
+
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP, SupportsQuant)
+from .qwen2_vl import Qwen2VLDummyInputsBuilder as Qwen2_5_VLDummyInputsBuilder
+from .qwen2_vl import (Qwen2VLMultiModalProcessor, Qwen2VLProcessingInfo,
+                       apply_rotary_pos_emb_vision)
+from .utils import (AutoWeightsLoader, WeightsMapper, cast_overflow_tensors,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+from .vision import get_vit_attn_backend
+
+logger = init_logger(__name__)
+
+# === Vision Inputs === #
+
+
+class Qwen2_5_VLImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values: torch.Tensor
+    """Shape:
+    `(num_patches, num_channels * patch_size * patch_size)`
+    """
+
+    image_grid_thw: torch.Tensor
+    """Shape: `(num_images, 3)`
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+class Qwen2_5_VLImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    image_embeds: torch.Tensor
+    """Supported types:
+    - list[`torch.Tensor`]: A list of tensors holding all images' features.
+        Each tensor holds an image's features.
+    - `torch.Tensor`: A tensor holding all images' features
+        (concatenation of all images' feature tensors).
+
+    Tensor shape: `(num_image_features, hidden_size)`
+    - `num_image_features` varies based on
+        the number and resolution of the images.
+    - `hidden_size` must match the hidden size of language model backbone.
+    """
+
+    image_grid_thw: torch.Tensor
+    """Shape: `(num_images, 3)`
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+Qwen2_5_VLImageInputs = Union[Qwen2_5_VLImagePixelInputs,
+                              Qwen2_5_VLImageEmbeddingInputs]
+
+
+class Qwen2_5_VLVideoPixelInputs(TypedDict):
+    type: Literal["pixel_values_videos"]
+    pixel_values_videos: torch.Tensor
+    """Shape:
+    `(num_patches,
+      num_channels * temporal_patch_size * patch_size * patch_size)`
+    """
+
+    video_grid_thw: torch.Tensor
+    """Shape: `(num_videos, 3)`
+
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+    second_per_grid_ts: torch.Tensor
+    """
+    The video time interval (in seconds) for each grid along the temporal 
+    dimension in the 3D position IDs. Returned when `videos` is not `None`.
+    """
+
+
+class Qwen2_5_VLVideoEmbeddingInputs(TypedDict):
+    type: Literal["video_embeds"]
+    video_embeds: torch.Tensor
+    """Supported types:
+    - list[`torch.Tensor`]: A list of tensors holding all videos' features.
+        Each tensor holds an video's features.
+    - `torch.Tensor`: A tensor holding all videos' features
+      (concatenation of all videos' feature tensors).
+
+    Tensor shape: `(num_image_features, hidden_size)`
+    - `num_image_features` varies based on
+        the number and resolution of the videos.
+    - `hidden_size` must match the hidden size of language model backbone.
+    """
+
+    video_grid_thw: torch.Tensor
+    """Shape: `(num_videos, 3)`
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+Qwen2_5_VLVideoInputs = Union[Qwen2_5_VLVideoPixelInputs,
+                              Qwen2_5_VLVideoEmbeddingInputs]
+
+# === Vision Encoder === #
+
+
+class Qwen2_5_VisionMLP(nn.Module):
+
+    def __init__(self,
+                 in_features: int,
+                 hidden_features: int,
+                 bias: bool = False,
+                 act_fn: Callable[[torch.Tensor], torch.Tensor] = F.silu,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=in_features,
+            output_sizes=[hidden_features] * 2,  # [gate_proj, up_proj]
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(hidden_features,
+                                           in_features,
+                                           bias=bias,
+                                           quant_config=quant_config,
+                                           prefix=f"{prefix}.down_proj")
+        self.act_fn = act_fn
+
+    def forward(self, x: torch.Tensor):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x_down, _ = self.down_proj(x)
+        return x_down
+
+
+def all_gather_interleave(local_tensor, hidden_size: int, tp_size: int):
+    """All-gather the input tensor interleavely across model parallel group."""
+    import torch.distributed as dist
+    gathered_tensors = [torch.zeros_like(local_tensor) for _ in range(tp_size)]
+    dist.all_gather(gathered_tensors,
+                    local_tensor,
+                    group=parallel_state.get_tp_group().device_group)
+
+    gathered_tensors_split = [
+        torch.split(tensor, hidden_size // tp_size, -1)
+        for tensor in gathered_tensors
+    ]
+    ordered_tensors = [
+        tensor for pair in zip(*gathered_tensors_split) for tensor in pair
+    ]
+    result_tensor = torch.cat(ordered_tensors, dim=-1)
+    return result_tensor
+
+
+class Qwen2_5_VisionAttention(nn.Module):
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        projection_size: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        # Per attention head and per partition values.
+        self.tp_size = parallel_state.get_tensor_model_parallel_world_size()
+        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            projection_size, num_heads)
+        self.num_attention_heads_per_partition = dist_utils.divide(
+            num_heads, self.tp_size)
+
+        self.qkv = QKVParallelLinear(
+            hidden_size=embed_dim,
+            head_size=self.hidden_size_per_attention_head,
+            total_num_heads=num_heads,
+            total_num_kv_heads=num_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv")
+        self.proj = RowParallelLinear(input_size=projection_size,
+                                      output_size=embed_dim,
+                                      quant_config=quant_config,
+                                      prefix=f"{prefix}.proj")
+
+        # Detect attention implementation.
+        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
+        if self.attn_backend not in {
+                _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS,
+                _Backend.ROCM_AITER_FA
+        }:
+            raise RuntimeError(
+                f"Qwen2.5-VL does not support {self.attn_backend} backend now."
+            )
+        self.is_flash_attn_backend = self.attn_backend in {
+            _Backend.FLASH_ATTN, _Backend.ROCM_AITER_FA
+        }
+
+    def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
+        # [s, b, 3 * head * head_dim]
+        seq_len, bs, _ = qkv.shape
+        if self.tp_size > 1:
+            qkv = all_gather_interleave(qkv, self.qkv.hidden_size,
+                                        self.tp_size)
+
+        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head * head_dim]
+        q, k, v = qkv.chunk(3, dim=2)
+
+        # 3 * [s, b, head * head_dim]
+        if self.tp_size > 1:
+            splitter = partial(dist_utils.split_tensor_along_last_dim,
+                               num_partitions=self.tp_size)
+            q = splitter(q)[self.tp_rank]
+            k = splitter(k)[self.tp_rank]
+            v = splitter(v)[self.tp_rank]
+
+        # 3 * [s, b, head * head_dim] -> 3 * [s, b, head, head_dim]
+        new_shape = (seq_len, bs, self.num_attention_heads_per_partition,
+                     self.hidden_size_per_attention_head)
+        q, k, v = (x.view(*new_shape) for x in (q, k, v))
+        return q, k, v
+
+    def forward(
+            self,
+            x: torch.Tensor,
+            cu_seqlens: torch.Tensor,
+            rotary_pos_emb: torch.Tensor,
+            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
+            seqlens: Optional[list[int]] = None,  # Only used for xFormers
+    ) -> torch.Tensor:
+        # [s, b, c] --> [s, b, head * 3 * head_dim]
+        x, _ = self.qkv(x)
+
+        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head, head_dim]
+        q, k, v = self.split_qkv(x)
+        batch_size = q.shape[1]
+
+        q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous()
+                   for x in (q, k, v))
+        if rotary_pos_emb is not None:
+            q = apply_rotary_pos_emb_vision(q, rotary_pos_emb)
+            k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)
+
+        if self.is_flash_attn_backend:
+            # from vllm_flash_attn.flash_attn_interface import (
+            #   flash_attn_varlen_func)
+            if self.attn_backend == _Backend.ROCM_AITER_FA:
+                from aiter import flash_attn_varlen_func
+            else:
+                from flash_attn import flash_attn_varlen_func
+
+            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
+
+            output = flash_attn_varlen_func(q,
+                                            k,
+                                            v,
+                                            cu_seqlens_q=cu_seqlens,
+                                            cu_seqlens_k=cu_seqlens,
+                                            max_seqlen_q=max_seqlen,
+                                            max_seqlen_k=max_seqlen,
+                                            dropout_p=0.0,
+                                            causal=False)
+
+            context_layer = rearrange(output,
+                                      "(b s) ... -> b s ...",
+                                      b=batch_size)
+        elif self.attn_backend == _Backend.TORCH_SDPA:
+            # Execute attention entry by entry for speed & less VRAM.
+            outputs = []
+            for i in range(1, len(cu_seqlens)):
+                start_idx = cu_seqlens[i - 1]
+                end_idx = cu_seqlens[i]
+                q_i = q[:, start_idx:end_idx]
+                k_i = k[:, start_idx:end_idx]
+                v_i = v[:, start_idx:end_idx]
+                q_i, k_i, v_i = (rearrange(x, "b s h d -> b h s d")
+                                 for x in [q_i, k_i, v_i])
+                output_i = F.scaled_dot_product_attention(q_i,
+                                                          k_i,
+                                                          v_i,
+                                                          dropout_p=0.0)
+                output_i = rearrange(output_i, "b h s d -> b s h d ")
+                outputs.append(output_i)
+            context_layer = torch.cat(outputs, dim=1)
+        elif self.attn_backend == _Backend.XFORMERS:
+            from xformers import ops as xops
+            from xformers.ops.fmha.attn_bias import BlockDiagonalMask
+
+            attn_bias = BlockDiagonalMask.from_seqlens(q_seqlen=seqlens,
+                                                       kv_seqlen=None,
+                                                       device=q.device)
+
+            context_layer = xops.memory_efficient_attention_forward(
+                q, k, v, attn_bias=attn_bias, p=0, scale=None)
+        context_layer = rearrange(context_layer,
+                                  "b s h d -> s b (h d)").contiguous()
+
+        output, _ = self.proj(context_layer)
+        return output
+
+
+class Qwen2_5_VisionBlock(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_hidden_dim: int,
+        act_fn: Callable[[torch.Tensor], torch.Tensor] = F.silu,
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.norm1 = norm_layer(dim)
+        self.norm2 = norm_layer(dim)
+        self.attn = Qwen2_5_VisionAttention(embed_dim=dim,
+                                            num_heads=num_heads,
+                                            projection_size=dim,
+                                            quant_config=quant_config,
+                                            prefix=f"{prefix}.attn")
+        self.mlp = Qwen2_5_VisionMLP(dim,
+                                     mlp_hidden_dim,
+                                     act_fn=act_fn,
+                                     bias=True,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.mlp")
+
+    def forward(
+            self,
+            x: torch.Tensor,
+            cu_seqlens: torch.Tensor,
+            rotary_pos_emb: torch.Tensor,
+            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
+            seqlens: Optional[list[int]] = None,  # Only used for xFormers
+    ) -> torch.Tensor:
+        x_attn = self.attn(self.norm1(x),
+                           cu_seqlens=cu_seqlens,
+                           rotary_pos_emb=rotary_pos_emb,
+                           max_seqlen=max_seqlen,
+                           seqlens=seqlens)
+        x_fused_norm, residual = self.norm2(x, residual=x_attn)
+        x = residual + self.mlp(x_fused_norm)
+        return x
+
+
+class Qwen2_5_VisionPatchEmbed(nn.Module):
+
+    def __init__(
+        self,
+        patch_size: int = 14,
+        temporal_patch_size: int = 2,
+        in_channels: int = 3,
+        hidden_size: int = 1152,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.hidden_size = hidden_size
+
+        kernel_size = (temporal_patch_size, patch_size, patch_size)
+        self.proj = nn.Conv3d(in_channels,
+                              hidden_size,
+                              kernel_size=kernel_size,
+                              stride=kernel_size,
+                              bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        L, C = x.shape
+        x = x.view(L, -1, self.temporal_patch_size, self.patch_size,
+                   self.patch_size)
+        x = self.proj(x).view(L, self.hidden_size)
+        return x
+
+
+class Qwen2_5_VisionPatchMerger(nn.Module):
+
+    def __init__(
+        self,
+        d_model: int,
+        context_dim: int,
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        spatial_merge_size: int = 2,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = context_dim * (spatial_merge_size**2)
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.ln_q = norm_layer(context_dim)
+        self.mlp = nn.ModuleList([
+            ColumnParallelLinear(self.hidden_size,
+                                 self.hidden_size,
+                                 bias=True,
+                                 quant_config=quant_config,
+                                 prefix=f"{prefix}.mlp.0"),
+            nn.GELU(),
+            RowParallelLinear(self.hidden_size,
+                              d_model,
+                              bias=True,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.mlp.2"),
+        ])
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.ln_q(x)
+        x = x.view(-1, self.hidden_size)
+
+        mlp_fc1, mlp_act, mlp_fc2 = self.mlp
+        x_parallel, _ = mlp_fc1(x)
+        x_parallel = mlp_act(x_parallel)
+        out, _ = mlp_fc2(x_parallel)
+        return out
+
+
+class Qwen2_5_VisionRotaryEmbedding(nn.Module):
+
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        inv_freq = 1.0 / (theta**(
+            torch.arange(0, dim, 2, dtype=torch.float, device='cpu') / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._freqs_cached = None
+
+    def update_freqs_cache(self, seqlen: int) -> None:
+        if seqlen > self._seq_len_cached:
+            seqlen *= 2
+            self._seq_len_cached = seqlen
+            self.inv_freq = 1.0 / (self.theta**(torch.arange(
+                0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device)
+                                                / self.dim))
+            seq = torch.arange(seqlen,
+                               device=self.inv_freq.device,
+                               dtype=self.inv_freq.dtype)
+            freqs = torch.outer(seq, self.inv_freq)
+            self._freqs_cached = freqs
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        self.update_freqs_cache(seqlen)
+        return self._freqs_cached[:seqlen]
+
+
+class Qwen2_5_VisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        vision_config: Qwen2_5_VLVisionConfig,
+        norm_eps: float = 1e-6,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        patch_size = vision_config.patch_size
+        temporal_patch_size = vision_config.temporal_patch_size
+        in_channels = vision_config.in_channels
+        depth = vision_config.depth
+        self.hidden_size = vision_config.hidden_size
+        self.num_heads = vision_config.num_heads
+
+        # args for get_window_index_thw
+        self.window_size = vision_config.window_size
+        self.patch_size = vision_config.patch_size
+        self.spatial_merge_size = vision_config.spatial_merge_size
+        self.fullatt_block_indexes = vision_config.fullatt_block_indexes
+        self.spatial_merge_unit = self.spatial_merge_size**2
+
+        self.patch_embed = Qwen2_5_VisionPatchEmbed(
+            patch_size=patch_size,
+            temporal_patch_size=temporal_patch_size,
+            in_channels=in_channels,
+            hidden_size=self.hidden_size,
+        )
+
+        norm_layer = partial(RMSNorm, eps=norm_eps)
+        head_dim = self.hidden_size // self.num_heads
+        self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList([
+            Qwen2_5_VisionBlock(dim=self.hidden_size,
+                                num_heads=self.num_heads,
+                                mlp_hidden_dim=vision_config.intermediate_size,
+                                act_fn=get_act_and_mul_fn(
+                                    vision_config.hidden_act),
+                                norm_layer=norm_layer,
+                                quant_config=quant_config,
+                                prefix=f"{prefix}.blocks.{layer_idx}")
+            for layer_idx in range(depth)
+        ])
+        self.merger = Qwen2_5_VisionPatchMerger(
+            d_model=vision_config.out_hidden_size,
+            context_dim=self.hidden_size,
+            norm_layer=norm_layer,
+            spatial_merge_size=self.spatial_merge_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.merger",
+        )
+        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.patch_embed.proj.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.patch_embed.proj.weight.device
+
+    def rotary_pos_emb_thw(self, t, h, w):
+        hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+        wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+        hpos_ids = hpos_ids.reshape(
+            h // self.spatial_merge_size,
+            self.spatial_merge_size,
+            w // self.spatial_merge_size,
+            self.spatial_merge_size,
+        ).permute(0, 2, 1, 3).flatten()
+        wpos_ids = wpos_ids.reshape(
+            h // self.spatial_merge_size,
+            self.spatial_merge_size,
+            w // self.spatial_merge_size,
+            self.spatial_merge_size,
+        ).permute(0, 2, 1, 3).flatten()
+        pos_ids = torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)
+        max_size = max(h, w)
+        rotary_pos_emb_full = self.rotary_pos_emb(max_size)
+        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+        rotary_pos_emb = rotary_pos_emb.reshape(
+            rotary_pos_emb.shape[0] // self.spatial_merge_unit,
+            self.spatial_merge_unit, -1)
+
+        return rotary_pos_emb
+
+    def get_window_index_thw(self, grid_t, grid_h, grid_w):
+        vit_merger_window_size = (self.window_size //
+                                  self.spatial_merge_size // self.patch_size)
+
+        llm_grid_h = grid_h // self.spatial_merge_size
+        llm_grid_w = grid_w // self.spatial_merge_size
+        index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(
+            grid_t, llm_grid_h, llm_grid_w)
+        pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
+        pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
+        num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
+        num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
+        index_padded = F.pad(index, (0, pad_w, 0, pad_h), 'constant', -100)
+        index_padded = index_padded.reshape(grid_t, num_windows_h,
+                                            vit_merger_window_size,
+                                            num_windows_w,
+                                            vit_merger_window_size)
+        index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
+            grid_t, num_windows_h * num_windows_w, vit_merger_window_size,
+            vit_merger_window_size)
+        seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
+        index_padded = index_padded.reshape(-1)
+        index_new = index_padded[index_padded != -100]
+        cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit
+        cu_seqlens_tmp = cu_seqlens_tmp.to(dtype=torch.int32)
+        cu_seqlens_tmp = torch.unique_consecutive(cu_seqlens_tmp)
+
+        return index_new, cu_seqlens_tmp
+
+    @lru_cache(maxsize=1024)  # noqa: B019
+    def get_rope_by_thw(self, t, h, w):
+        window_index_thw, cu_seqlens_window_thw = self.get_window_index_thw(
+            t, h, w)
+        rotary_pos_emb_thw = self.rotary_pos_emb_thw(t, h, w)
+        rotary_pos_emb_thw = rotary_pos_emb_thw[window_index_thw, :, :]
+        rotary_pos_emb_thw = rotary_pos_emb_thw.flatten(start_dim=0, end_dim=1)
+        cu_seqlens_thw = torch.repeat_interleave(
+            torch.tensor([h * w], dtype=torch.int32), t)
+        return (rotary_pos_emb_thw, window_index_thw, cu_seqlens_window_thw,
+                cu_seqlens_thw)
+
+    def compute_attn_mask_seqlen(
+        self,
+        cu_seqlens: torch.Tensor,
+    ) -> tuple[Optional[int], Optional[list[int]]]:
+        max_seqlen, seqlens = None, None
+        if (self.attn_backend == _Backend.FLASH_ATTN
+                or self.attn_backend == _Backend.ROCM_AITER_FA):
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
+        elif self.attn_backend == _Backend.XFORMERS:
+            seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+        return max_seqlen, seqlens
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        grid_thw: list[list[int]],
+    ) -> torch.Tensor:
+        # patchify
+        seq_len, _ = x.size()
+        rotary_pos_emb = []
+        window_index: list = []
+        cu_window_seqlens: list = [torch.tensor([0], dtype=torch.int32)]
+        cu_seqlens: list = []
+
+        hidden_states = x.to(device=self.device, dtype=self.dtype)
+        hidden_states = self.patch_embed(hidden_states)
+
+        window_index_id = 0
+        cu_window_seqlens_last = 0
+        for t, h, w in grid_thw:
+            t, h, w = int(t), int(h), int(w)
+            llm_h = h // self.spatial_merge_size
+            llm_w = w // self.spatial_merge_size
+
+            (
+                rotary_pos_emb_thw,
+                window_index_thw,
+                cu_seqlens_window_thw,
+                cu_seqlens_thw,
+            ) = self.get_rope_by_thw(t, h, w)
+
+            window_index.append(window_index_thw + window_index_id)
+            window_index_id += (t * llm_h * llm_w)
+
+            cu_seqlens_window_thw = (cu_seqlens_window_thw +
+                                     cu_window_seqlens_last)
+            cu_window_seqlens_last = cu_seqlens_window_thw[-1]
+            cu_window_seqlens.append(cu_seqlens_window_thw)
+
+            rotary_pos_emb.append(rotary_pos_emb_thw)
+
+            cu_seqlens.append(cu_seqlens_thw)
+
+        rotary_pos_emb = torch.cat(rotary_pos_emb)
+        window_index = torch.cat(window_index)
+        cu_window_seqlens = torch.cat(cu_window_seqlens)
+        cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
+        cu_seqlens = torch.cat(cu_seqlens)
+        cu_seqlens = torch.cumsum(cu_seqlens, dim=0, dtype=torch.int32)
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
+
+        # transformers
+        # pre-compute seqlens for window/full attn to reduce cuMemcpy operations
+        max_seqlen_full, seqlens_full = self.compute_attn_mask_seqlen(
+            cu_seqlens)
+        max_seqlen_window, seqlens_window = self.compute_attn_mask_seqlen(
+            cu_window_seqlens)
+
+        cu_seqlens = cu_seqlens.to(device=self.device, non_blocking=True)
+        cu_window_seqlens = cu_window_seqlens.to(device=self.device,
+                                                 non_blocking=True)
+        rotary_pos_emb = rotary_pos_emb.to(device=self.device,
+                                           non_blocking=True)
+        window_index = window_index.to(device=hidden_states.device,
+                                       non_blocking=True)
+
+        hidden_states = hidden_states.reshape(
+            seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+        hidden_states = hidden_states[window_index, :, :]
+        hidden_states = hidden_states.reshape(seq_len, -1)
+
+        hidden_states = hidden_states.unsqueeze(1)
+
+        for layer_num, blk in enumerate(self.blocks):
+            if layer_num in self.fullatt_block_indexes:
+                cu_seqlens_now = cu_seqlens
+                max_seqlen_now = max_seqlen_full
+                seqlens_now = seqlens_full
+            else:
+                cu_seqlens_now = cu_window_seqlens
+                max_seqlen_now = max_seqlen_window
+                seqlens_now = seqlens_window
+
+            hidden_states = blk(
+                hidden_states,
+                cu_seqlens=cu_seqlens_now,
+                rotary_pos_emb=rotary_pos_emb,
+                max_seqlen=max_seqlen_now,
+                seqlens=seqlens_now,
+            )
+
+        # For Qwen2.5-VL-3B, float16 will overflow at last block
+        # for long visual tokens sequences.
+        if hidden_states.dtype == torch.float16:
+            hidden_states = cast_overflow_tensors(hidden_states)
+
+        # adapter
+        hidden_states = self.merger(hidden_states)
+        reverse_indices = torch.argsort(window_index)
+        hidden_states = hidden_states[reverse_indices, :]
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("attn.qkv.", "attn.q.", "q"),
+            ("attn.qkv.", "attn.k.", "k"),
+            ("attn.qkv.", "attn.v.", "v"),
+            ("mlp.gate_up_proj.", "mlp.gate_proj.", 0),
+            ("mlp.gate_up_proj.", "mlp.up_proj.", 1),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class Qwen2_5_VLProcessingInfo(Qwen2VLProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Qwen2_5_VLConfig)
+
+    def get_hf_processor(self, **kwargs: object) -> Qwen2_5_VLProcessor:
+        return self.ctx.get_hf_processor(
+            Qwen2_5_VLProcessor,
+            use_fast=kwargs.pop("use_fast", True),
+            **kwargs,
+        )
+
+
+class Qwen2_5_VLMultiModalProcessor(Qwen2VLMultiModalProcessor):
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            **super()._get_mm_fields_config(hf_inputs, hf_processor_mm_kwargs),
+            second_per_grid_ts=MultiModalFieldConfig.batched("video"),
+        )
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Qwen2_5_VLMultiModalProcessor,
+    info=Qwen2_5_VLProcessingInfo,
+    dummy_inputs=Qwen2_5_VLDummyInputsBuilder)
+class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                         SupportsLoRA, SupportsPP,
+                                         SupportsQuant):
+
+    # To ensure correct weight loading and mapping.
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            # mapping for new names in checkpoint saved after transformers v4.52
+            "model.language_model.": "language_model.model.",
+            "model.visual.": "visual.",
+            # mapping for original checkpoint
+            "lm_head.": "language_model.lm_head.",
+            "model.": "language_model.model.",
+        })
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<|vision_start|><|image_pad|><|vision_end|>"
+        if modality.startswith("video"):
+            return "<|vision_start|><|video_pad|><|vision_end|>"
+
+        raise ValueError("Only image or video modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config: Qwen2_5_VLConfig = vllm_config.model_config.hf_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        if multimodal_config.get_limit_per_prompt("image") or \
+            multimodal_config.get_limit_per_prompt("video"):
+            self.visual = Qwen2_5_VisionTransformer(
+                config.vision_config,
+                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+                quant_config=self._maybe_ignore_quant_config(
+                    self.quant_config),
+                prefix=maybe_prefix(prefix, "visual"),
+            )
+        else:
+            self.visual = None
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=["Qwen2ForCausalLM"],
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _maybe_ignore_quant_config(self, config: Optional[QuantizationConfig]):
+        # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
+        # seems to avoid vision encoder sections for some models.
+        if isinstance(config, (GPTQConfig, GPTQMarlinConfig)):
+            return None
+        return config
+
+    def _validate_and_reshape_mm_tensor(self, mm_input: object,
+                                        name: str) -> torch.Tensor:
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of {name}. "
+                             f"Got type: {type(mm_input)}")
+        if isinstance(mm_input, torch.Tensor):
+            if mm_input.ndim == 2:
+                return mm_input
+            if mm_input.ndim != 3:
+                raise ValueError(f"{name} should be 2D or batched 3D tensor. "
+                                 f"Got ndim: {mm_input.ndim} "
+                                 f"(shape={mm_input.shape})")
+            return torch.concat(list(mm_input))
+        else:
+            return torch.concat(mm_input)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Qwen2_5_VLImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            pixel_values = self._validate_and_reshape_mm_tensor(
+                pixel_values, "image pixel values")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            if not isinstance(pixel_values, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image pixel values. "
+                                 f"Got type: {type(pixel_values)}")
+
+            return Qwen2_5_VLImagePixelInputs(type="pixel_values",
+                                              pixel_values=pixel_values,
+                                              image_grid_thw=image_grid_thw)
+
+        if image_embeds is not None:
+            image_embeds = self._validate_and_reshape_mm_tensor(
+                image_embeds, "image embeds")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            if not isinstance(image_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+            return Qwen2_5_VLImageEmbeddingInputs(
+                type="image_embeds",
+                image_embeds=image_embeds,
+                image_grid_thw=image_grid_thw)
+
+    def _parse_and_validate_video_input(
+            self, **kwargs: object) -> Optional[Qwen2_5_VLVideoInputs]:
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
+        video_embeds = kwargs.pop("video_embeds", None)
+        video_grid_thw = kwargs.pop("video_grid_thw", None)
+        second_per_grid_ts = kwargs.pop("second_per_grid_ts", None)
+
+        if pixel_values_videos is None and video_embeds is None:
+            return None
+
+        if pixel_values_videos is not None:
+            pixel_values_videos = self._validate_and_reshape_mm_tensor(
+                pixel_values_videos, "video pixel values")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            return Qwen2_5_VLVideoPixelInputs(
+                type="pixel_values_videos",
+                pixel_values_videos=pixel_values_videos,
+                video_grid_thw=video_grid_thw,
+                second_per_grid_ts=second_per_grid_ts,
+            )
+
+        if video_embeds is not None:
+            video_embeds = self._validate_and_reshape_mm_tensor(
+                video_embeds, "video embeds")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            if not isinstance(video_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of video embeddings. "
+                                 f"Got type: {type(video_embeds)}")
+            return Qwen2_5_VLVideoEmbeddingInputs(
+                type="video_embeds",
+                video_embeds=video_embeds,
+                video_grid_thw=video_grid_thw)
+
+    def _process_image_input(
+            self,
+            image_input: Qwen2_5_VLImageInputs) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = image_input["image_grid_thw"]
+        assert grid_thw.ndim == 2
+        grid_thw_list = grid_thw.tolist()
+
+        if image_input["type"] == "image_embeds":
+            image_embeds = image_input["image_embeds"].type(self.visual.dtype)
+        else:
+            pixel_values = image_input["pixel_values"]
+            image_embeds = self.visual(pixel_values, grid_thw=grid_thw_list)
+
+        # Split concatenated embeddings for each image item.
+        # Using prod on grid_thw_list instead of grid_thw.prod avoids CUDA sync
+        merge_size = self.visual.spatial_merge_size
+        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).prod(-1) //
+                 (merge_size * merge_size)).tolist()
+
+        return image_embeds.split(sizes)
+
+    def _process_video_input(
+            self,
+            video_input: Qwen2_5_VLVideoInputs) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = video_input["video_grid_thw"]
+        assert grid_thw.ndim == 2
+        grid_thw_list = grid_thw.tolist()
+
+        if video_input["type"] == "video_embeds":
+            video_embeds = video_input["video_embeds"].type(self.visual.dtype)
+        else:
+            pixel_values_videos = video_input["pixel_values_videos"]
+            video_embeds = self.visual(pixel_values_videos,
+                                       grid_thw=grid_thw_list)
+
+        # Split concatenated embeddings for each video item.
+        merge_size = self.visual.spatial_merge_size
+        # Using prod on grid_thw_list instead of grid_thw.prod avoids CUDA sync
+        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).prod(-1) //
+                 (merge_size * merge_size)).tolist()
+
+        return video_embeds.split(sizes)
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        mm_input_by_modality = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if input_key in ("pixel_values", "image_embeds"
+                             ) and "image" not in mm_input_by_modality:
+                mm_input_by_modality[
+                    "image"] = self._parse_and_validate_image_input(**kwargs)
+            if input_key in ("pixel_values_videos", "video_embeds"
+                             ) and "video" not in mm_input_by_modality:
+                mm_input_by_modality[
+                    "video"] = self._parse_and_validate_video_input(**kwargs)
+        return mm_input_by_modality
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+
+        mm_input_by_modality = self._parse_and_validate_multimodal_inputs(
+            **kwargs)
+        if not mm_input_by_modality:
+            return []
+
+        # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor correspoending to a multimodal data item (image or video).
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in mm_input_by_modality:
+            multimodal_input = mm_input_by_modality[modality]
+            if modality == "image":
+                vision_embeddings = self._process_image_input(multimodal_input)
+                multimodal_embeddings += vision_embeddings
+            if modality == "video":
+                video_embeddings = self._process_video_input(multimodal_input)
+                multimodal_embeddings += video_embeddings
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                [self.config.image_token_id, self.config.video_token_id])
+        return inputs_embeds
+
+    def get_input_embeddings_v0(
+        self,
+        input_ids: torch.Tensor,
+        image_input: Optional[Qwen2_5_VLImageInputs] = None,
+        video_input: Optional[Qwen2_5_VLVideoInputs] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.get_input_embeddings(input_ids)
+        if image_input is not None:
+            image_embeds = self._process_image_input(image_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                image_embeds,
+                placeholder_token_id=self.config.image_token_id,
+            )
+
+        if video_input is not None:
+            video_embeds = self._process_video_input(video_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                video_embeds,
+                placeholder_token_id=self.config.video_token_id,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        """Run forward pass for Qwen2.5-VL.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            positions: Flattened (concatenated) position ids corresponding to a
+                batch.
+                **NOTE**: If mrope is enabled (default setting for Qwen2.5-VL
+                opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+            pixel_values: Pixel values to be fed to a model.
+                `None` if no images are passed.
+            image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
+                `None` if no images are passed.
+            pixel_values_videos: Pixel values of videos to be fed to a model.
+                `None` if no videos are passed.
+            video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
+                `None` if no videos are passed.
+            second_per_grid_ts: Tensor `(num_videos)` of video time interval (
+                in seconds) for each grid along the temporal dimension in the
+                3D position IDs. `None` if no videos are passed.
+        """
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner from
+        # `get_multimodal_embeddings` and `get_input_embeddings`, this
+        # condition is only for v0 compatibility.
+        elif inputs_embeds is None:
+            image_input = self._parse_and_validate_image_input(**kwargs)
+            video_input = self._parse_and_validate_video_input(**kwargs)
+
+            if image_input is None and video_input is None:
+                inputs_embeds = None
+            else:
+                if uses_mrope(self.config):
+                    assert positions.ndim == 2 and positions.size(0) == 3, (
+                        "multimodal section rotary embedding requires "
+                        f"(3, seq_len) positions, but got {positions.size()}")
+                inputs_embeds = self.get_input_embeddings_v0(
+                    input_ids,
+                    image_input=image_input,
+                    video_input=video_input)
+                input_ids = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+
+        skip_prefixes = []
+        if self.visual is None:
+            skip_prefixes.extend(["visual."])
+        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="visual.merger.",
+            tower_model="visual.",
+        )
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_audio.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ef55cd704cf06b19f039934d9ba64a79a68d33c
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_audio.py
@@ -0,0 +1,410 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Qwen2-Audio model compatible with HuggingFace weights."""
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Any, Optional, TypedDict, Union
+
+import torch
+import torch.nn as nn
+from transformers import BatchFeature
+from transformers.models.qwen2_audio import (Qwen2AudioConfig,
+                                             Qwen2AudioEncoder,
+                                             Qwen2AudioProcessor)
+from transformers.models.whisper import WhisperFeatureExtractor
+
+from vllm.config import VllmConfig
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import (AudioProcessorItems, MultiModalDataItems,
+                                   MultiModalDataParser)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .utils import (AutoWeightsLoader, init_vllm_registered_model,
+                    maybe_prefix, merge_multimodal_embeddings)
+
+
+# # === Audio Inputs === #
+class Qwen2AudioInputs(TypedDict):
+    input_features: torch.Tensor
+    """Shape: `(num_audios, num_mel_bins, 3000)`"""
+
+    feature_attention_mask: torch.Tensor
+    """Shape: `(num_audios, 3000)`"""
+
+
+# === Audio Encoder === #
+
+
+class Qwen2AudioMultiModalProjector(nn.Module):
+
+    def __init__(self, audio_hidden_size: int, text_hidden_size: int):
+        super().__init__()
+        self.linear = nn.Linear(audio_hidden_size, text_hidden_size, bias=True)
+
+    def forward(self, audio_features):
+        hidden_states = self.linear(audio_features)
+        return hidden_states
+
+
+# From Qwen2AudioEncoder._get_feat_extract_output_lengths
+def _get_feat_extract_output_lengths(input_lengths: torch.Tensor):
+    feat_lengths = (input_lengths - 1) // 2 + 1
+    output_lengths = (feat_lengths - 2) // 2 + 1
+    return feat_lengths, output_lengths
+
+
+class Qwen2AudioProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Qwen2AudioConfig)
+
+    def get_hf_processor(self, **kwargs: object) -> Qwen2AudioProcessor:
+        return self.ctx.get_hf_processor(Qwen2AudioProcessor, **kwargs)
+
+    def get_feature_extractor(self,
+                              **kwargs: object) -> WhisperFeatureExtractor:
+        hf_processor = self.get_hf_processor(**kwargs)
+        feature_extractor = hf_processor.feature_extractor  # type: ignore
+        assert isinstance(feature_extractor, WhisperFeatureExtractor)
+        return feature_extractor
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"audio": None}
+
+
+class Qwen2AudioDummyInputsBuilder(
+        BaseDummyInputsBuilder[Qwen2AudioProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_audios = mm_counts.get("audio", 0)
+
+        hf_processor = self.info.get_hf_processor()
+        audio_token = hf_processor.audio_token
+
+        return audio_token * num_audios
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        feature_extractor = self.info.get_feature_extractor()
+
+        sampling_rate = feature_extractor.sampling_rate
+        audio_len = feature_extractor.chunk_length * sampling_rate
+        num_audios = mm_counts.get("audio", 0)
+
+        return {
+            "audio":
+            self._get_dummy_audios(length=audio_len, num_audios=num_audios)
+        }
+
+
+class Qwen2AudioMultiModalProcessor(
+        BaseMultiModalProcessor[Qwen2AudioProcessingInfo]):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        feature_extractor = self.info.get_feature_extractor()
+        return MultiModalDataParser(target_sr=feature_extractor.sampling_rate)
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, Any],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        # NOTE - we rename audios -> audio in mm data because transformers has
+        # deprecated audios for the qwen2audio processor and will remove
+        # support for it in transformers 4.54.
+        audios = mm_data.pop("audios", [])
+        if audios:
+            mm_data["audio"] = audios
+
+        # Text-only input not supported in composite processor
+        if not mm_data.get("audio", []):
+            prompt_ids = self.info.get_tokenizer().encode(prompt)
+            prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
+            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
+
+        feature_extractor = self.info.get_feature_extractor(**mm_kwargs)
+        mm_kwargs = dict(
+            **mm_kwargs,
+            sampling_rate=feature_extractor.sampling_rate,
+        )
+
+        return super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            input_features=MultiModalFieldConfig.batched("audio"),
+            feature_attention_mask=MultiModalFieldConfig.batched("audio"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+
+        # Use getattr with default to be compatible with transformers<4.48
+        audio_token = getattr(processor, "audio_token", "<|AUDIO|>")
+        audio_bos_token = getattr(processor, "audio_bos_token",
+                                  "<|audio_bos|>")
+        audio_eos_token = getattr(processor, "audio_eos_token",
+                                  "<|audio_eos|>")
+
+        audio_token_id = vocab[audio_token]
+        audio_bos_id = vocab[audio_bos_token]
+        audio_eos_id = vocab[audio_eos_token]
+
+        feature_attention_mask = out_mm_kwargs.get("feature_attention_mask")
+        if feature_attention_mask is None:
+            audio_output_lengths = []
+        else:
+            assert isinstance(feature_attention_mask, torch.Tensor)
+            _, audio_output_lens = _get_feat_extract_output_lengths(
+                feature_attention_mask.sum(-1))
+
+            audio_output_lengths = audio_output_lens.tolist()
+
+        def get_replacement_qwen2_audio(item_idx: int):
+            num_features = audio_output_lengths[item_idx]
+            if num_features == 0:
+                audios = mm_items.get_items("audio", AudioProcessorItems)
+                audio_len = audios.get_audio_length(item_idx)
+
+                raise ValueError(f"The audio (len={audio_len}) is too short "
+                                 "to be represented inside the model")
+
+            audio_tokens = [audio_token_id] * num_features
+
+            return PromptUpdateDetails.select_token_id(
+                [audio_bos_id] + audio_tokens + [audio_eos_id],
+                embed_token_id=audio_token_id,
+            )
+
+        return [
+            PromptReplacement(
+                modality="audio",
+                target=audio_token,
+                replacement=get_replacement_qwen2_audio,
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Qwen2AudioMultiModalProcessor,
+    info=Qwen2AudioProcessingInfo,
+    dummy_inputs=Qwen2AudioDummyInputsBuilder)
+class Qwen2AudioForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                         SupportsPP):
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("audio"):
+            return f"Audio {i}: <|audio_bos|><|AUDIO|><|audio_eos|>"
+
+        raise ValueError("Only audio modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.audio_tower = Qwen2AudioEncoder(config.audio_config)
+        self.multi_modal_projector = Qwen2AudioMultiModalProjector(
+            config.audio_config.d_model, config.text_config.hidden_size)
+
+        self.quant_config = quant_config
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=["Qwen2ForCausalLM"],
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _validate_and_reshape_mm_tensor(self, mm_input: object,
+                                        name: str) -> torch.Tensor:
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of {name}. "
+                             f"Got type: {type(mm_input)}")
+        if isinstance(mm_input, torch.Tensor):
+            return torch.concat(list(mm_input))
+        else:
+            return torch.concat(mm_input)
+
+    def _parse_and_validate_audio_input(
+            self, **kwargs: object) -> Optional[Qwen2AudioInputs]:
+        input_features = kwargs.pop('input_features', None)
+        feature_attention_mask = kwargs.pop('feature_attention_mask', None)
+        if input_features is None:
+            return None
+        input_features = self._validate_and_reshape_mm_tensor(
+            input_features, 'input_features')
+        feature_attention_mask = self._validate_and_reshape_mm_tensor(
+            feature_attention_mask, 'feature_attention_mask')
+        if not isinstance(input_features, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of audio input features. "
+                             f"Got type: {type(input_features)}")
+        return Qwen2AudioInputs(input_features=input_features,
+                                feature_attention_mask=feature_attention_mask)
+
+    def _process_audio_input(self,
+                             audio_input: Qwen2AudioInputs) -> torch.Tensor:
+
+        input_features = audio_input["input_features"]
+        feature_attention_mask = audio_input["feature_attention_mask"]
+
+        audio_feat_lengths, audio_output_lengths = (
+            self.audio_tower._get_feat_extract_output_lengths(
+                feature_attention_mask.sum(-1)))
+
+        batch_size, _, max_mel_seq_len = input_features.shape
+        max_seq_len = (max_mel_seq_len - 2) // 2 + 1
+        # Create a sequence tensor of shape (batch_size, max_seq_len)
+        seq_range = (torch.arange(
+            0,
+            max_seq_len,
+            dtype=audio_feat_lengths.dtype,
+            device=audio_feat_lengths.device).unsqueeze(0).expand(
+                batch_size, max_seq_len))
+        lengths_expand = audio_feat_lengths.unsqueeze(-1).expand(
+            batch_size, max_seq_len)
+        # Create mask
+        padding_mask = seq_range >= lengths_expand
+
+        audio_attention_mask_ = padding_mask.view(
+            batch_size, 1, 1, max_seq_len).expand(batch_size, 1, max_seq_len,
+                                                  max_seq_len)
+        audio_attention_mask = audio_attention_mask_.to(
+            dtype=self.audio_tower.conv1.weight.dtype,
+            device=self.audio_tower.conv1.weight.device)
+        audio_attention_mask[audio_attention_mask_] = float("-inf")
+
+        audio_outputs = self.audio_tower(input_features,
+                                         attention_mask=audio_attention_mask)
+        selected_audio_feature = audio_outputs.last_hidden_state
+        audio_features = self.multi_modal_projector(selected_audio_feature)
+        num_audios, max_audio_tokens, embed_dim = audio_features.shape
+        audio_output_lengths = audio_output_lengths.unsqueeze(1)
+        audio_features_mask = torch.arange(max_audio_tokens).expand(
+            num_audios, max_audio_tokens).to(
+                audio_output_lengths.device) < audio_output_lengths
+        masked_audio_features = audio_features[audio_features_mask].view(
+            -1, embed_dim)
+
+        # Split to tuple of embeddings for individual audio input.
+        return torch.split(masked_audio_features,
+                           audio_output_lengths.flatten().tolist())
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        audio_input = self._parse_and_validate_audio_input(**kwargs)
+        if audio_input is None:
+            return []
+        masked_audio_features = self._process_audio_input(audio_input)
+        return masked_audio_features
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.config.audio_token_index)
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            multimodal_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      multimodal_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model.model(input_ids,
+                                                  positions,
+                                                  intermediate_tensors,
+                                                  inputs_embeds=inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_moe.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_moe.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c4ad34246d663916fc4c40b3dc19842577bc0e1
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_moe.py
@@ -0,0 +1,557 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Qwen2MoE model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import Qwen2MoeConfig
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, extract_layer_index,
+                    is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+logger = init_logger(__name__)
+
+
+class Qwen2MoeMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config,
+                                           reduce_results=reduce_results)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Qwen2MoeSparseMoeBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: Qwen2MoeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+        if self.tp_size > config.num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.num_experts}.")
+
+        self.experts = FusedMoE(num_experts=config.num_experts,
+                                top_k=config.num_experts_per_tok,
+                                hidden_size=config.hidden_size,
+                                intermediate_size=config.moe_intermediate_size,
+                                reduce_results=False,
+                                renormalize=config.norm_topk_prob,
+                                quant_config=quant_config,
+                                prefix=f"{prefix}.experts")
+
+        self.gate = ReplicatedLinear(config.hidden_size,
+                                     config.num_experts,
+                                     bias=False,
+                                     quant_config=None)
+        if config.shared_expert_intermediate_size > 0:
+            self.shared_expert = Qwen2MoeMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.shared_expert_intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=self.experts.must_reduce_shared_expert_outputs(
+                ),
+            )
+        else:
+            self.shared_expert = None
+        self.shared_expert_gate = torch.nn.Linear(config.hidden_size,
+                                                  1,
+                                                  bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # NOTE: hidden_states can have either 1D or 2D shape.
+        orig_shape = hidden_states.shape
+        hidden_dim = hidden_states.shape[-1]
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        shared_output = None
+        if self.shared_expert is not None:
+            shared_output = self.shared_expert(hidden_states)
+            if self.shared_expert_gate is not None:
+                shared_output = F.sigmoid(
+                    self.shared_expert_gate(hidden_states)) * shared_output
+
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+        final_hidden_states = self.experts(hidden_states=hidden_states,
+                                           router_logits=router_logits)
+        if shared_output is not None:
+            final_hidden_states = final_hidden_states + shared_output
+        if self.tp_size > 1:
+            final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(  # noqa E501
+                final_hidden_states)
+
+        return final_hidden_states.view(orig_shape)
+
+
+class Qwen2MoeAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        dual_chunk_attention_config: Optional[dict[str, Any]] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+        self.dual_chunk_attention_config = dual_chunk_attention_config
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=True,
+            quant_config=quant_config,
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+            dual_chunk_attention_config=dual_chunk_attention_config,
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+            **{
+                "layer_idx": extract_layer_index(prefix),
+                "dual_chunk_attention_config": dual_chunk_attention_config,
+            } if dual_chunk_attention_config else {})
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Qwen2MoeDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: Qwen2MoeConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        dual_chunk_attention_config = getattr(config,
+                                              "dual_chunk_attention_config",
+                                              None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = Qwen2MoeAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+            dual_chunk_attention_config=dual_chunk_attention_config,
+        )
+
+        # Note: Qwen/Qwen2-57B-A14B-Instruct does not have
+        # `mlp_only_layers` in the config.
+        layer_idx = extract_layer_index(prefix)
+        mlp_only_layers = ([] if not hasattr(config, "mlp_only_layers") else
+                           config.mlp_only_layers)
+        if (layer_idx not in mlp_only_layers) and (
+                config.num_experts > 0 and
+            (layer_idx + 1) % config.decoder_sparse_step == 0):
+            self.mlp = Qwen2MoeSparseMoeBlock(config=config,
+                                              quant_config=quant_config,
+                                              prefix=f"{prefix}.mlp")
+        else:
+            self.mlp = Qwen2MoeMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+            )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Qwen2MoeModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.vocab_size = config.vocab_size
+        self.config = config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Qwen2MoeDecoderLayer(config=config,
+                                                cache_config=cache_config,
+                                                quant_config=quant_config,
+                                                prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(positions, hidden_states, residual)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if "mlp.experts" in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if ((name.endswith(".bias") or name.endswith("_bias"))
+                        and name not in params_dict):
+                    continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
+                if name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    # Skip loading extra bias for GPTQ models.
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param,
+                                  loaded_weight,
+                                  name,
+                                  shard_id=shard_id,
+                                  expert_id=expert_id)
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    # Remapping the name of FP8 kv-scale.
+                    if name.endswith("kv_scale"):
+                        remapped_kv_scale_name = name.replace(
+                            ".kv_scale", ".attn.kv_scale")
+                        if remapped_kv_scale_name not in params_dict:
+                            logger.warning_once(
+                                "Found kv_scale in the checkpoint (e.g. %s), but not found the expected name in the model (e.g. %s). kv_scale is not loaded.",  #  noqa: E501
+                                name,
+                                remapped_kv_scale_name,
+                            )
+                            continue
+                        else:
+                            name = remapped_kv_scale_name
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class Qwen2MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
+
+    fall_back_to_pt_during_load = False
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Qwen2MoeModel(vllm_config=vllm_config,
+                                   prefix=maybe_prefix(prefix, "model"))
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config)
+        if self.config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_rm.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_rm.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0a30e04c602a86db205886295ee8d1e39483f36
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_rm.py
@@ -0,0 +1,117 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://huggingface.co/Qwen/Qwen2.5-Math-RM-72B/blob/main/modeling_qwen2_rm.py
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+"""Inference-only Qwen2-RM model compatible with HuggingFace weights."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP, default_pooling_type
+from .qwen2 import Qwen2Model
+from .utils import AutoWeightsLoader, maybe_prefix
+
+
+class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP):
+
+    is_pooling_model = True
+    pooler: Pooler
+
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.lora_config = lora_config
+
+        self.quant_config = quant_config
+        self.model = Qwen2Model(vllm_config=vllm_config,
+                                prefix=maybe_prefix(prefix, "model"))
+
+        self.score = nn.Sequential(
+            ColumnParallelLinear(config.hidden_size,
+                                 config.hidden_size,
+                                 quant_config=quant_config,
+                                 return_bias=False),
+            nn.ReLU(),
+            RowParallelLinear(config.hidden_size,
+                              config.num_labels,
+                              quant_config=quant_config,
+                              return_bias=False),
+        )
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        logits = self.score(hidden_states)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self,
+                                   ignore_unexpected_prefixes=["lm_head."])
+        return loader.load_weights(weights)
+
+
+@default_pooling_type("ALL")
+class Qwen2ForRewardModel(Qwen2RewardBaseModel):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        vllm_config.model_config.hf_config.num_labels = 1
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.pooler = DispatchPooler(
+            {"encode": Pooler.for_encode(pooler_config)}, )
+
+
+@default_pooling_type("STEP")
+class Qwen2ForProcessRewardModel(Qwen2RewardBaseModel):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        vllm_config.model_config.hf_config.num_labels = 2
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.pooler = DispatchPooler(
+            {"encode": Pooler.for_encode(pooler_config)})
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_vl.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_vl.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e2f7ca42b4baae39ded381b9fc67770a9ae687e
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/qwen2_vl.py
@@ -0,0 +1,1458 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/19e6e80e10118f855137b90740936c0b11ac397f/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
+# Copyright 2024 The Qwen team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Qwen2-VL model compatible with HuggingFace weights."""
+from collections.abc import Iterable, Mapping, Sequence
+from functools import partial
+from typing import Any, Callable, Literal, Optional, TypedDict, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from transformers import AutoConfig, BatchFeature
+from transformers.models.qwen2_vl import (Qwen2VLImageProcessor,
+                                          Qwen2VLProcessor)
+from transformers.models.qwen2_vl.configuration_qwen2_vl import (
+    Qwen2VLConfig, Qwen2VLVisionConfig)
+from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
+from transformers.models.qwen2_vl.video_processing_qwen2_vl import (
+    Qwen2VLVideoProcessor)
+
+from vllm.config import VllmConfig
+from vllm.distributed import parallel_state, tensor_model_parallel_all_gather
+from vllm.distributed import utils as dist_utils
+from vllm.logger import init_logger
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.activation import QuickGELU
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.gptq import GPTQConfig
+from vllm.model_executor.layers.quantization.gptq_marlin import (
+    GPTQMarlinConfig)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (ImageItem, ModalityData,
+                                    MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs, VideoItem)
+from vllm.multimodal.parse import (DictEmbeddingItems, ImageSize,
+                                   ModalityDataItems, MultiModalDataItems,
+                                   MultiModalDataParser)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.platforms import _Backend, current_platform
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.config import uses_mrope
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
+from .utils import (AutoWeightsLoader, WeightsMapper,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+from .vision import get_vit_attn_backend
+
+logger = init_logger(__name__)
+
+# For profile run
+_MAX_FRAMES_PER_VIDEO = 16
+
+# === Vision Inputs === #
+
+
+class Qwen2VLImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values: torch.Tensor
+    """Shape:
+    `(num_patches, num_channels * patch_size * patch_size)`
+    """
+
+    image_grid_thw: torch.Tensor
+    """Shape: `(num_images, 3)`
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+class Qwen2VLImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    image_embeds: torch.Tensor
+    """Supported types:
+    - list[`torch.Tensor`]: A list of tensors holding all images' features.
+        Each tensor holds an image's features.
+    - `torch.Tensor`: A tensor holding all images' features
+        (concatenation of all images' feature tensors).
+    
+    Tensor shape: `(num_image_features, hidden_size)`
+    - `num_image_features` varies based on
+        the number and resolution of the images.
+    - `hidden_size` must match the hidden size of language model backbone.
+    """
+
+    image_grid_thw: torch.Tensor
+    """Shape: `(num_images, 3)`
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+Qwen2VLImageInputs = Union[Qwen2VLImagePixelInputs,
+                           Qwen2VLImageEmbeddingInputs]
+
+
+class Qwen2VLVideoPixelInputs(TypedDict):
+    type: Literal["pixel_values_videos"]
+    pixel_values_videos: torch.Tensor
+    """Shape:
+    `(num_patches,
+      num_channels * temporal_patch_size * patch_size * patch_size)`
+    """
+
+    video_grid_thw: torch.Tensor
+    """Shape: `(num_videos, 3)`
+
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+class Qwen2VLVideoEmbeddingInputs(TypedDict):
+    type: Literal["video_embeds"]
+    video_embeds: torch.Tensor
+    """Supported types:
+    - list[`torch.Tensor`]: A list of tensors holding all videos' features.
+        Each tensor holds an video's features.
+    - `torch.Tensor`: A tensor holding all videos' features
+        (concatenation of all videos' feature tensors).
+    
+    Tensor shape: `(num_image_features, hidden_size)`
+    - `num_image_features` varies based on 
+        the number and resolution of the videos.
+    - `hidden_size` must match the hidden size of language model backbone.
+    """
+
+    video_grid_thw: torch.Tensor
+    """Shape: `(num_videos, 3)`
+    This should be in `(grid_t, grid_h, grid_w)` format.
+    """
+
+
+Qwen2VLVideoInputs = Union[Qwen2VLVideoPixelInputs,
+                           Qwen2VLVideoEmbeddingInputs]
+
+# === Vision Encoder === #
+
+
+class Qwen2VisionMLP(nn.Module):
+
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int,
+        act_layer: type[nn.Module] = QuickGELU,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.fc1 = ColumnParallelLinear(in_features,
+                                        hidden_features,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.fc1")
+        self.act = act_layer()
+        self.fc2 = RowParallelLinear(hidden_features,
+                                     in_features,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.fc2")
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x_parallel, _ = self.fc1(x)
+        x_parallel = self.act(x_parallel)
+        x, _ = self.fc2(x_parallel)
+        return x
+
+
+def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor:
+    if not interleaved:
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+    else:
+        x1, x2 = x[..., ::2], x[..., 1::2]
+        return rearrange(torch.stack((-x2, x1), dim=-1),
+                         "... d two -> ... (d two)",
+                         two=2)
+
+
+def apply_rotary_emb_torch(x: torch.Tensor,
+                           cos: torch.Tensor,
+                           sin: torch.Tensor,
+                           interleaved: bool = False) -> torch.Tensor:
+    """
+    x: (batch_size, seqlen, nheads, headdim)
+    cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
+    """
+    ro_dim = cos.shape[-1] * 2
+    assert ro_dim <= x.shape[-1]
+    cos = repeat(
+        cos,
+        "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)")
+    sin = repeat(
+        sin,
+        "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)")
+    return torch.cat(
+        [
+            x[..., :ro_dim] * cos +
+            rotate_half(x[..., :ro_dim], interleaved) * sin, x[..., ro_dim:]
+        ],
+        dim=-1,
+    )
+
+
+def apply_rotary_pos_emb_vision(t: torch.Tensor,
+                                freqs: torch.Tensor) -> torch.Tensor:
+    t_ = t.float()
+    cos = freqs.cos()
+    sin = freqs.sin()
+    apply_rotary_emb = apply_rotary_emb_torch
+    if current_platform.is_cuda():
+        from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb
+    output = apply_rotary_emb(t_, cos, sin).type_as(t)
+    return output
+
+
+class Qwen2VisionAttention(nn.Module):
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        projection_size: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        # Per attention head and per partition values.
+        world_size = parallel_state.get_tensor_model_parallel_world_size()
+        self.tp_size = world_size
+        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            projection_size, num_heads)
+        self.num_attention_heads_per_partition = dist_utils.divide(
+            num_heads, world_size)
+
+        self.qkv = ColumnParallelLinear(input_size=embed_dim,
+                                        output_size=3 * projection_size,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.qkv")
+        self.proj = RowParallelLinear(input_size=projection_size,
+                                      output_size=embed_dim,
+                                      quant_config=quant_config,
+                                      prefix=f"{prefix}.proj")
+
+        # Detect attention implementation.
+        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
+        if self.attn_backend not in {
+                _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS,
+                _Backend.ROCM_AITER_FA
+        }:
+            raise RuntimeError(
+                f"Qwen2-VL does not support {self.attn_backend} backend now.")
+        self.is_flash_attn_backend = self.attn_backend in {
+            _Backend.FLASH_ATTN, _Backend.ROCM_AITER_FA
+        }
+
+    def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
+        # [s, b, 3 * head * head_dim]
+        seq_len, bs, _ = qkv.shape
+        if self.tp_size > 1:
+            qkv = tensor_model_parallel_all_gather(qkv)
+
+        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head * head_dim]
+        q, k, v = qkv.chunk(3, dim=2)
+
+        # 3 * [s, b, head * head_dim]
+        if self.tp_size > 1:
+            splitter = partial(dist_utils.split_tensor_along_last_dim,
+                               num_partitions=self.tp_size)
+            q = splitter(q)[self.tp_rank]
+            k = splitter(k)[self.tp_rank]
+            v = splitter(v)[self.tp_rank]
+
+        # 3 * [s, b, head * head_dim] -> 3 * [s, b, head, head_dim]
+        new_shape = (seq_len, bs, self.num_attention_heads_per_partition,
+                     self.hidden_size_per_attention_head)
+        q, k, v = (x.view(*new_shape) for x in (q, k, v))
+        return q, k, v
+
+    def forward(
+            self,
+            x: torch.Tensor,
+            cu_seqlens: torch.Tensor,
+            rotary_pos_emb: torch.Tensor,
+            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
+            seqlens: Optional[list[int]] = None,  # Only used for xFormers
+    ) -> torch.Tensor:
+
+        # [s, b, c] --> [s, b, 3 * head * head_dim]
+        x, _ = self.qkv(x)
+
+        # [s, b, 3 * head * head_dim] -> 3 * [s, b, head, head_dim]
+        q, k, v = self.split_qkv(x)
+        batch_size = q.shape[1]
+
+        q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous()
+                   for x in (q, k, v))
+        if rotary_pos_emb is not None:
+            q = apply_rotary_pos_emb_vision(q, rotary_pos_emb)
+            k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)
+
+        if self.is_flash_attn_backend:
+            # from vllm_flash_attn.flash_attn_interface import (
+            #   flash_attn_varlen_func)
+            if self.attn_backend == _Backend.ROCM_AITER_FA:
+                from aiter import flash_attn_varlen_func
+            else:
+                from flash_attn import flash_attn_varlen_func
+
+            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
+
+            output = flash_attn_varlen_func(q,
+                                            k,
+                                            v,
+                                            cu_seqlens_q=cu_seqlens,
+                                            cu_seqlens_k=cu_seqlens,
+                                            max_seqlen_q=max_seqlen,
+                                            max_seqlen_k=max_seqlen,
+                                            dropout_p=0.0,
+                                            causal=False)
+
+            context_layer = rearrange(output,
+                                      "(b s) ... -> b s ...",
+                                      b=batch_size)
+        elif self.attn_backend == _Backend.TORCH_SDPA:
+            # Execute attention entry by entry for speed & less VRAM.
+            outputs = []
+            for i in range(1, len(cu_seqlens)):
+                start_idx = cu_seqlens[i - 1]
+                end_idx = cu_seqlens[i]
+                q_i = q[:, start_idx:end_idx]
+                k_i = k[:, start_idx:end_idx]
+                v_i = v[:, start_idx:end_idx]
+                q_i, k_i, v_i = (rearrange(x, "b s h d -> b h s d")
+                                 for x in [q_i, k_i, v_i])
+                output_i = F.scaled_dot_product_attention(q_i,
+                                                          k_i,
+                                                          v_i,
+                                                          dropout_p=0.0)
+                output_i = rearrange(output_i, "b h s d -> b s h d ")
+                outputs.append(output_i)
+            context_layer = torch.cat(outputs, dim=1)
+        elif self.attn_backend == _Backend.XFORMERS:
+            from xformers import ops as xops
+            from xformers.ops.fmha.attn_bias import BlockDiagonalMask
+
+            attn_bias = BlockDiagonalMask.from_seqlens(q_seqlen=seqlens,
+                                                       kv_seqlen=None,
+                                                       device=q.device)
+
+            context_layer = xops.memory_efficient_attention_forward(
+                q, k, v, attn_bias=attn_bias, p=0, scale=None)
+        context_layer = rearrange(context_layer,
+                                  "b s h d -> s b (h d)").contiguous()
+
+        output, _ = self.proj(context_layer)
+        return output
+
+
+class Qwen2VisionBlock(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float,
+        act_layer: type[nn.Module] = QuickGELU,
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.norm1 = norm_layer(dim)
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+
+        self.attn = Qwen2VisionAttention(embed_dim=dim,
+                                         num_heads=num_heads,
+                                         projection_size=dim,
+                                         quant_config=quant_config,
+                                         prefix=f"{prefix}.attn")
+        self.mlp = Qwen2VisionMLP(dim,
+                                  mlp_hidden_dim,
+                                  act_layer=act_layer,
+                                  quant_config=quant_config,
+                                  prefix=f"{prefix}.mlp")
+
+    def forward(
+            self,
+            x: torch.Tensor,
+            cu_seqlens: torch.Tensor,
+            rotary_pos_emb: torch.Tensor,
+            max_seqlen: Optional[int] = None,  # Only used for Flash Attention
+            seqlens: Optional[list[int]] = None,  # Only used for xFormers
+    ) -> torch.Tensor:
+        x = x + self.attn(
+            self.norm1(x),
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb=rotary_pos_emb,
+            max_seqlen=max_seqlen,
+            seqlens=seqlens,
+        )
+
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class Qwen2VisionPatchEmbed(nn.Module):
+
+    def __init__(
+        self,
+        patch_size: int = 14,
+        temporal_patch_size: int = 2,
+        in_channels: int = 3,
+        embed_dim: int = 1152,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.embed_dim = embed_dim
+
+        kernel_size = (temporal_patch_size, patch_size, patch_size)
+        self.proj = nn.Conv3d(in_channels,
+                              embed_dim,
+                              kernel_size=kernel_size,
+                              stride=kernel_size,
+                              bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        L, C = x.shape
+        x = x.view(L, -1, self.temporal_patch_size, self.patch_size,
+                   self.patch_size)
+        x = self.proj(x).view(L, self.embed_dim)
+        return x
+
+
+class Qwen2VisionPatchMerger(nn.Module):
+
+    def __init__(
+        self,
+        d_model: int,
+        context_dim: int,
+        norm_layer: Optional[Callable[[int], nn.Module]] = None,
+        spatial_merge_size: int = 2,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = context_dim * (spatial_merge_size**2)
+        if norm_layer is None:
+            norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        self.ln_q = norm_layer(context_dim)
+        self.mlp = nn.ModuleList([
+            ColumnParallelLinear(self.hidden_size,
+                                 self.hidden_size,
+                                 bias=True,
+                                 quant_config=quant_config,
+                                 prefix=f"{prefix}.mlp.0"),
+            nn.GELU(),
+            RowParallelLinear(self.hidden_size,
+                              d_model,
+                              bias=True,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.mlp.2"),
+        ])
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.ln_q(x)
+        x = x.view(-1, self.hidden_size)
+
+        mlp_fc1, mlp_act, mlp_fc2 = self.mlp
+        x_parallel, _ = mlp_fc1(x)
+        x_parallel = mlp_act(x_parallel)
+        out, _ = mlp_fc2(x_parallel)
+        return out
+
+
+class Qwen2VisionRotaryEmbedding(nn.Module):
+
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        inv_freq = 1.0 / (theta
+                          **(torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._freqs_cached = None
+
+    def update_freqs_cache(self, seqlen: int) -> None:
+        if seqlen > self._seq_len_cached:
+            seqlen *= 2
+            self._seq_len_cached = seqlen
+            self.inv_freq = 1.0 / (self.theta**(torch.arange(
+                0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device)
+                                                / self.dim))
+            seq = torch.arange(seqlen,
+                               device=self.inv_freq.device,
+                               dtype=self.inv_freq.dtype)
+            freqs = torch.outer(seq, self.inv_freq)
+            self._freqs_cached = freqs
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        self.update_freqs_cache(seqlen)
+        return self._freqs_cached[:seqlen]
+
+
+class Qwen2VisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        vision_config: Qwen2VLVisionConfig,
+        norm_eps: float = 1e-6,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        patch_size = vision_config.patch_size
+        temporal_patch_size = vision_config.temporal_patch_size
+        spatial_merge_size = vision_config.spatial_merge_size
+        in_channels = vision_config.in_channels
+        hidden_size = vision_config.hidden_size
+        embed_dim = vision_config.embed_dim
+        depth = vision_config.depth
+        num_heads = vision_config.num_heads
+        mlp_ratio = vision_config.mlp_ratio
+
+        self.spatial_merge_size = spatial_merge_size
+        self.num_heads = num_heads
+        self.embed_dim = embed_dim
+
+        self.patch_embed = Qwen2VisionPatchEmbed(
+            patch_size=patch_size,
+            temporal_patch_size=temporal_patch_size,
+            in_channels=in_channels,
+            embed_dim=embed_dim,
+        )
+
+        norm_layer = partial(nn.LayerNorm, eps=norm_eps)
+        head_dim = embed_dim // num_heads
+        self.rotary_pos_emb = Qwen2VisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList([
+            Qwen2VisionBlock(dim=embed_dim,
+                             num_heads=num_heads,
+                             mlp_ratio=mlp_ratio,
+                             norm_layer=norm_layer,
+                             quant_config=quant_config,
+                             prefix=f"{prefix}.blocks.{layer_idx}")
+            for layer_idx in range(depth)
+        ])
+        self.merger = Qwen2VisionPatchMerger(
+            d_model=hidden_size,
+            context_dim=embed_dim,
+            norm_layer=norm_layer,
+            quant_config=quant_config,
+            prefix=f"{prefix}.merger",
+        )
+        self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.patch_embed.proj.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.patch_embed.proj.weight.device
+
+    def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
+        pos_ids = []
+        for t, h, w in grid_thw:
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            hpos_ids = hpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            ).permute(0, 2, 1, 3).flatten()
+            wpos_ids = wpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            ).permute(0, 2, 1, 3).flatten()
+            pos_ids.append(
+                torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        pos_ids = torch.cat(pos_ids, dim=0)
+        max_grid_size = grid_thw[:, 1:].max()
+        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+        return rotary_pos_emb
+
+    def compute_attn_mask_seqlen(
+            self, cu_seqlens: torch.Tensor
+    ) -> tuple[Optional[int], Optional[list[int]]]:
+        max_seqlen, seqlens = None, None
+        if (self.attn_backend == _Backend.FLASH_ATTN
+                or self.attn_backend == _Backend.ROCM_AITER_FA):
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
+        elif self.attn_backend == _Backend.XFORMERS:
+            seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+        return max_seqlen, seqlens
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        grid_thw: torch.Tensor,
+    ) -> torch.Tensor:
+        # patchify
+        x = x.to(device=self.device, dtype=self.dtype)
+        x = self.patch_embed(x)
+
+        # compute position embedding
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+
+        # compute cu_seqlens
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2],
+                                             grid_thw[:, 0]).cumsum(
+                                                 dim=0, dtype=torch.int32)
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
+
+        # transformers
+        x = x.unsqueeze(1)
+
+        # pre-compute seqlens for attn mask to reduce cuMemcpy operations
+        max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens)
+        for blk in self.blocks:
+            x = blk(
+                x,
+                cu_seqlens=cu_seqlens,
+                rotary_pos_emb=rotary_pos_emb,
+                max_seqlen=max_seqlen,
+                seqlens=seqlens,
+            )
+
+        # adapter
+        x = self.merger(x)
+
+        return x
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+def _qwen2vl_field_config(hf_inputs: Mapping[str, torch.Tensor]):
+    image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
+    image_grid_sizes = image_grid_thw.prod(-1)
+
+    video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3)))
+    video_grid_sizes = video_grid_thw.prod(-1)
+
+    return dict(
+        pixel_values=MultiModalFieldConfig.flat_from_sizes(
+            "image", image_grid_sizes),
+        image_embeds=MultiModalFieldConfig.flat_from_sizes(
+            "image", image_grid_sizes),
+        image_grid_thw=MultiModalFieldConfig.batched("image"),
+        pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
+            "video", video_grid_sizes),
+        video_embeds=MultiModalFieldConfig.flat_from_sizes(
+            "video", video_grid_sizes),
+        video_grid_thw=MultiModalFieldConfig.batched("video"),
+    )
+
+
+class Qwen2VLMultiModalDataParser(MultiModalDataParser):
+
+    def _parse_image_data(
+        self,
+        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
+    ) -> Optional[ModalityDataItems[Any, Any]]:
+        if isinstance(data, dict):
+            return DictEmbeddingItems(
+                data,
+                modality="image",
+                required_fields={"image_embeds", "image_grid_thw"},
+                fields_factory=_qwen2vl_field_config,
+            )
+
+        return super()._parse_image_data(data)
+
+    def _parse_video_data(
+        self,
+        data: Union[dict[str, torch.Tensor], ModalityData[VideoItem]],
+    ) -> Optional[ModalityDataItems[Any, Any]]:
+        if isinstance(data, dict):
+            return DictEmbeddingItems(
+                data,
+                modality="video",
+                required_fields={"video_embeds", "video_grid_thw"},
+                fields_factory=_qwen2vl_field_config,
+            )
+
+        return super()._parse_video_data(data)
+
+
+class Qwen2VLProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(Qwen2VLConfig)
+
+    def get_hf_processor(self, **kwargs: object) -> Qwen2VLProcessor:
+        return self.ctx.get_hf_processor(
+            Qwen2VLProcessor,
+            use_fast=kwargs.pop("use_fast", True),
+            **kwargs,
+        )
+
+    def get_image_processor(self, **kwargs: object) -> Qwen2VLImageProcessor:
+        return self.get_hf_processor(**kwargs).image_processor
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None, "video": None}
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int]:
+        max_image_tokens = self.get_max_image_tokens()
+        max_video_tokens = self.get_max_video_tokens(seq_len, mm_counts)
+        return {"image": max_image_tokens, "video": max_video_tokens}
+
+    def _get_vision_info(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int = 1,
+        do_resize: bool = True,
+        image_processor: Optional[Qwen2VLImageProcessor],
+    ) -> tuple[ImageSize, int]:
+        if image_processor is None:
+            image_processor = self.get_image_processor()
+
+        hf_config = self.get_hf_config()
+        vision_config = hf_config.vision_config
+        patch_size = vision_config.patch_size
+        merge_size = vision_config.spatial_merge_size
+        temporal_patch_size = vision_config.temporal_patch_size
+
+        if do_resize:
+            resized_height, resized_width = smart_resize(
+                height=image_height,
+                width=image_width,
+                factor=patch_size * merge_size,
+                min_pixels=image_processor.min_pixels,
+                max_pixels=image_processor.max_pixels,
+            )
+            preprocessed_size = ImageSize(width=resized_width,
+                                          height=resized_height)
+        else:
+            preprocessed_size = ImageSize(width=image_width,
+                                          height=image_height)
+
+        # NOTE: Frames are padded to be divisible by `temporal_patch_size`
+        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L294
+        padded_num_frames = num_frames + num_frames % temporal_patch_size
+
+        grid_t = max(padded_num_frames // temporal_patch_size, 1)
+        grid_h = preprocessed_size.height // patch_size
+        grid_w = preprocessed_size.width // patch_size
+
+        num_patches = grid_t * grid_h * grid_w
+        num_vision_tokens = num_patches // (merge_size**2)
+
+        return preprocessed_size, num_vision_tokens
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        image_processor: Optional[Qwen2VLImageProcessor],
+    ) -> int:
+        _, num_image_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            image_processor=image_processor,
+        )
+        return num_image_tokens
+
+    def get_num_video_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        num_frames: int,
+        image_processor: Optional[Qwen2VLImageProcessor],
+    ) -> int:
+        _, num_video_tokens = self._get_vision_info(
+            image_width=image_width,
+            image_height=image_height,
+            num_frames=num_frames,
+            image_processor=image_processor,
+        )
+        return num_video_tokens
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        max_image_size, _ = self._get_vision_info(
+            image_width=9999999,
+            image_height=9999999,
+            image_processor=None,
+        )
+        return max_image_size
+
+    def get_max_image_tokens(self) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_image_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            image_processor=None,
+        )
+
+    def _get_max_video_frames(self, max_tokens: int) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        num_frames = 0
+
+        while True:
+            next_num_frames = num_frames + 1
+            next_max_tokens = self.get_num_video_tokens(
+                image_width=target_width,
+                image_height=target_height,
+                num_frames=next_num_frames,
+                image_processor=None,
+            )
+
+            if next_max_tokens > max_tokens:
+                break
+
+            num_frames = next_num_frames
+
+        return num_frames
+
+    def get_num_frames_with_most_features(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        max_images = mm_counts.get("image", 0)
+        max_videos = mm_counts.get("video", 0)
+
+        max_image_tokens = self.get_max_image_tokens() * max_images
+        max_total_frames = self._get_max_video_frames(seq_len -
+                                                      max_image_tokens)
+        max_frames_per_video = min(max_total_frames // max(max_videos, 1),
+                                   _MAX_FRAMES_PER_VIDEO)
+
+        return max(max_frames_per_video, 1)
+
+    def get_max_video_tokens(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        target_width, target_height = self.get_image_size_with_most_features()
+
+        return self.get_num_video_tokens(
+            image_width=target_width,
+            image_height=target_height,
+            num_frames=self.get_num_frames_with_most_features(
+                seq_len, mm_counts),
+            image_processor=None,
+        )
+
+
+class Qwen2VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen2VLProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        hf_processor = self.info.get_hf_processor()
+        image_token: str = hf_processor.image_token
+        video_token: str = hf_processor.video_token
+
+        return image_token * num_images + video_token * num_videos
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        num_videos = mm_counts.get("video", 0)
+
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+        target_num_frames = \
+            self.info.get_num_frames_with_most_features(seq_len, mm_counts)
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images),
+            "video":
+            self._get_dummy_videos(
+                width=target_width,
+                height=target_height,
+                num_frames=target_num_frames,
+                num_videos=num_videos,
+            )
+        }
+
+
+class Qwen2VLMultiModalProcessor(BaseMultiModalProcessor[Qwen2VLProcessingInfo]
+                                 ):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        return Qwen2VLMultiModalDataParser()
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_processor = self.info.get_image_processor(
+            **hf_processor_mm_kwargs)
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+
+        placeholder = {
+            "image": vocab[hf_processor.image_token],
+            "video": vocab[hf_processor.video_token],
+        }
+
+        merge_length = image_processor.merge_size**2
+
+        def get_replacement_qwen2vl(item_idx: int, modality: str):
+            grid_thw = out_mm_kwargs[f"{modality}_grid_thw"][item_idx]
+            assert isinstance(grid_thw, torch.Tensor)
+
+            num_tokens = int(grid_thw.prod()) // merge_length
+            return [placeholder[modality]] * num_tokens
+
+        return [
+            PromptReplacement(
+                modality=modality,
+                target=[placeholder[modality]],
+                replacement=partial(get_replacement_qwen2vl,
+                                    modality=modality),
+            ) for modality in ("image", "video")
+        ]
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return _qwen2vl_field_config(hf_inputs)
+
+
+@MULTIMODAL_REGISTRY.register_processor(Qwen2VLMultiModalProcessor,
+                                        info=Qwen2VLProcessingInfo,
+                                        dummy_inputs=Qwen2VLDummyInputsBuilder)
+class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                      SupportsLoRA, SupportsPP):
+
+    # To ensure correct weight loading and mapping.
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            # mapping for new names in checkpoint saved after transformers v4.52
+            "model.language_model.": "language_model.model.",
+            "model.visual.": "visual.",
+            # mapping for original checkpoint
+            "lm_head.": "language_model.lm_head.",
+            "model.": "language_model.model.",
+        })
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<|vision_start|><|image_pad|><|vision_end|>"
+        if modality.startswith("video"):
+            return "<|vision_start|><|video_pad|><|vision_end|>"
+
+        raise ValueError("Only image or video modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config: Qwen2VLConfig = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        if multimodal_config.get_limit_per_prompt("image") or \
+            multimodal_config.get_limit_per_prompt("video"):
+            self.visual = Qwen2VisionTransformer(
+                config.vision_config,
+                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
+                quant_config=self._maybe_ignore_quant_config(quant_config),
+                prefix=maybe_prefix(prefix, "visual"),
+            )
+        else:
+            self.visual = None
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+            architectures=["Qwen2ForCausalLM"],
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+        # GPTQ configs do not have a list of ignored modules, however AutoGPTQ
+        # seems to avoid vision encoder sections for some models.
+        # See: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4
+        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
+            return None
+        return quant_config
+
+    def _validate_and_reshape_mm_tensor(self, mm_input: object,
+                                        name: str) -> torch.Tensor:
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of {name}. "
+                             f"Got type: {type(mm_input)}")
+        if isinstance(mm_input, torch.Tensor):
+            if mm_input.ndim == 2:
+                return mm_input
+            if mm_input.ndim != 3:
+                raise ValueError(f"{name} should be 2D or batched 3D tensor. "
+                                 f"Got ndim: {mm_input.ndim} "
+                                 f"(shape={mm_input.shape})")
+            return torch.concat(list(mm_input))
+        else:
+            return torch.concat(mm_input)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Qwen2VLImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            pixel_values = self._validate_and_reshape_mm_tensor(
+                pixel_values, "image pixel values")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            if not isinstance(pixel_values, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image pixel values. "
+                                 f"Got type: {type(pixel_values)}")
+
+            return Qwen2VLImagePixelInputs(type="pixel_values",
+                                           pixel_values=pixel_values,
+                                           image_grid_thw=image_grid_thw)
+
+        if image_embeds is not None:
+            image_embeds = self._validate_and_reshape_mm_tensor(
+                image_embeds, "image embeds")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, "image grid_thw")
+
+            if not isinstance(image_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+            return Qwen2VLImageEmbeddingInputs(type="image_embeds",
+                                               image_embeds=image_embeds,
+                                               image_grid_thw=image_grid_thw)
+
+    def _parse_and_validate_video_input(
+            self, **kwargs: object) -> Optional[Qwen2VLVideoInputs]:
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
+        video_embeds = kwargs.pop("video_embeds", None)
+        video_grid_thw = kwargs.pop("video_grid_thw", None)
+
+        if pixel_values_videos is None and video_embeds is None:
+            return None
+
+        if pixel_values_videos is not None:
+            pixel_values_videos = self._validate_and_reshape_mm_tensor(
+                pixel_values_videos, "video pixel values")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            return Qwen2VLVideoPixelInputs(
+                type="pixel_values_videos",
+                pixel_values_videos=pixel_values_videos,
+                video_grid_thw=video_grid_thw,
+            )
+
+        if video_embeds is not None:
+            video_embeds = self._validate_and_reshape_mm_tensor(
+                video_embeds, "video embeds")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, "video grid_thw")
+
+            if not isinstance(video_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of video embeddings. "
+                                 f"Got type: {type(video_embeds)}")
+            return Qwen2VLVideoEmbeddingInputs(type="video_embeds",
+                                               video_embeds=video_embeds,
+                                               video_grid_thw=video_grid_thw)
+
+    def _process_image_input(
+            self, image_input: Qwen2VLImageInputs) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = image_input["image_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        if image_input["type"] == "image_embeds":
+            image_embeds = image_input["image_embeds"]
+        else:
+            pixel_values = image_input["pixel_values"]
+            image_embeds = self.visual(pixel_values, grid_thw=grid_thw)
+
+        # Split concatenated embeddings for each image item.
+        merge_size = self.visual.spatial_merge_size
+        sizes = grid_thw.prod(-1) // merge_size // merge_size
+
+        return image_embeds.split(sizes.tolist())
+
+    def _process_video_input(
+            self, video_input: Qwen2VLVideoInputs) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = video_input["video_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        if video_input["type"] == "video_embeds":
+            video_embeds = video_input["video_embeds"]
+        else:
+            pixel_values_videos = video_input["pixel_values_videos"]
+            video_embeds = self.visual(pixel_values_videos, grid_thw=grid_thw)
+
+        # Split concatenated embeddings for each video item.
+        merge_size = self.visual.spatial_merge_size
+        sizes = grid_thw.prod(-1) // merge_size // merge_size
+
+        return video_embeds.split(sizes.tolist())
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        modalities = {}
+
+        # Preserve the order of modalities if there are multiple of them
+        # from the order of kwargs.
+        for input_key in kwargs:
+            if input_key in ("pixel_values",
+                             "image_embeds") and "images" not in modalities:
+                modalities["images"] = self._parse_and_validate_image_input(
+                    **kwargs)
+            if input_key in ("pixel_values_videos",
+                             "video_embeds") and "videos" not in modalities:
+                modalities["videos"] = self._parse_and_validate_video_input(
+                    **kwargs)
+
+        return modalities
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+
+        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not modalities:
+            return []
+
+        # The result multimodal_embeddings is tuple of tensors, with each
+        # tensor correspoending to a multimodal data item (image or video).
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        # NOTE: It is important to iterate over the keys in this dictionary
+        # to preserve the order of the modalities.
+        for modality in modalities:
+            if modality == "images":
+                image_input = modalities["images"]
+                vision_embeddings = self._process_image_input(image_input)
+                multimodal_embeddings += vision_embeddings
+            if modality == "videos":
+                video_input = modalities["videos"]
+                video_embeddings = self._process_video_input(video_input)
+                multimodal_embeddings += video_embeddings
+
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                [self.config.image_token_id, self.config.video_token_id])
+        return inputs_embeds
+
+    def get_input_embeddings_v0(
+        self,
+        input_ids: torch.Tensor,
+        image_input: Optional[Qwen2VLImagePixelInputs] = None,
+        video_input: Optional[Qwen2VLVideoPixelInputs] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.get_input_embeddings(input_ids)
+        if image_input is not None:
+            image_embeds = self._process_image_input(image_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                image_embeds,
+                placeholder_token_id=self.config.image_token_id,
+            )
+
+        if video_input is not None:
+            video_embeds = self._process_video_input(video_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                video_embeds,
+                placeholder_token_id=self.config.video_token_id,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        """Run forward pass for Qwen2-VL.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            positions: Flattened (concatenated) position ids corresponding to a
+                batch.
+                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
+                opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+            pixel_values: Pixel values to be fed to a model.
+                `None` if no images are passed.
+            image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
+                `None` if no images are passed.
+            pixel_values_videos: Pixel values of videos to be fed to a model.
+                `None` if no videos are passed.
+            video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
+                `None` if no videos are passed.
+        """
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner from
+        # `get_multimodal_embeddings` and `get_input_embeddings`, this
+        # condition is only for v0 compatibility.
+        elif inputs_embeds is None:
+            image_input = self._parse_and_validate_image_input(**kwargs)
+            video_input = self._parse_and_validate_video_input(**kwargs)
+
+            if image_input is None and video_input is None:
+                inputs_embeds = None
+            else:
+                if uses_mrope(self.config):
+                    assert positions.ndim == 2 and positions.size(0) == 3, (
+                        "multimodal section rotary embedding requires "
+                        f"(3, seq_len) positions, but got {positions.size()}")
+                inputs_embeds = self.get_input_embeddings_v0(
+                    input_ids,
+                    image_input=image_input,
+                    video_input=video_input)
+                input_ids = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+
+        skip_prefixes = []
+        if self.visual is None:
+            skip_prefixes.extend(["visual."])
+        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="visual.merger.",
+            tower_model="visual.",
+        )
+
+
+class Tarsier2MultiModalProcessor(Qwen2VLMultiModalProcessor):
+    pass
+
+
+class Tarsier2ImageProcessor(Qwen2VLImageProcessor):
+
+    def __init__(
+        self,
+        size: Optional[dict[str, int]] = None,
+        **kwargs,
+    ) -> None:
+        if size is not None and "min_pixels" in size and "max_pixels" in size:
+            # Remap if Tarsier2-specific format is provided
+            remapped_size = {
+                "shortest_edge": size["min_pixels"],
+                "longest_edge": size["max_pixels"]
+            }
+            super().__init__(size=remapped_size, **kwargs)
+        else:
+            super().__init__(size=size, **kwargs)
+
+
+class Tarsier2Processor(Qwen2VLProcessor):
+
+    def __init__(
+        self,
+        vision_config: dict,
+        tokenizer: AnyTokenizer,
+        **kwargs,
+    ):
+        self.image_processor = Tarsier2ImageProcessor(**vision_config)
+        super().__init__(
+            image_processor=self.image_processor,
+            tokenizer=tokenizer,
+            video_processor=Qwen2VLVideoProcessor(**vision_config),
+            chat_template=None,
+            **kwargs)
+
+
+class Tarsier2ProcessingInfo(Qwen2VLProcessingInfo):
+
+    def get_hf_config(self) -> Qwen2VLConfig:
+        model_path = self.ctx.model_config.model
+        original_config = AutoConfig.from_pretrained(model_path)
+        config_dict = original_config.to_dict()
+        correct_config = Qwen2VLConfig.from_dict(config_dict)
+
+        return correct_config
+
+    def get_hf_processor(self, **kwargs: object) -> Tarsier2Processor:
+        return Tarsier2Processor(
+            vision_config=self.ctx.get_hf_image_processor_config(),
+            tokenizer=self.get_tokenizer(),
+            **kwargs,
+        )
+
+    def get_image_processor(self) -> Tarsier2ImageProcessor:
+        return Tarsier2ImageProcessor(
+            **self.ctx.get_hf_image_processor_config())
+
+
+@MULTIMODAL_REGISTRY.register_processor(Tarsier2MultiModalProcessor,
+                                        info=Tarsier2ProcessingInfo,
+                                        dummy_inputs=Qwen2VLDummyInputsBuilder)
+class Tarsier2ForConditionalGeneration(Qwen2VLForConditionalGeneration):
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
+        "vision_tower.": "visual.",
+    })
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        # Tarsier2 uses llava as model_type, which will create a Qwen2VLConfig
+        # as text_config, we need to reconstruct Qwen2VLConfig from LlavaConfig.
+        config = vllm_config.model_config.hf_config
+        qwen2vl_config = config.text_config
+        qwen2vl_config.architectures = config.architectures
+        vllm_config.model_config.hf_config = qwen2vl_config
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+
+        skip_prefixes = []
+        if self.visual is None:
+            skip_prefixes.extend(["visual."])
+        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/registry.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..109bc1fe5c77965dc6e40fafc5679465d2041d72
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/registry.py
@@ -0,0 +1,858 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Whenever you add an architecture to this page, please also update
+`tests/models/registry.py` with example HuggingFace models for it.
+"""
+import importlib
+import os
+import pickle
+import subprocess
+import sys
+import tempfile
+from abc import ABC, abstractmethod
+from collections.abc import Set
+from dataclasses import dataclass, field
+from functools import lru_cache
+from typing import Callable, Optional, TypeVar, Union
+
+import torch.nn as nn
+import transformers
+
+from vllm.config import (ModelConfig, ModelImpl, iter_architecture_defaults,
+                         try_match_architecture_defaults)
+from vllm.logger import init_logger
+from vllm.transformers_utils.dynamic_module import (
+    try_get_class_from_dynamic_module)
+
+from .interfaces import (get_default_pooling_type, has_inner_state, has_noops,
+                         is_attention_free, is_hybrid, supports_cross_encoding,
+                         supports_multimodal, supports_multimodal_raw_input,
+                         supports_pp, supports_transcription, supports_v0_only)
+from .interfaces_base import is_pooling_model, is_text_generation_model
+
+logger = init_logger(__name__)
+
+# yapf: disable
+_TEXT_GENERATION_MODELS = {
+    # [Decoder-only]
+    "AquilaModel": ("llama", "LlamaForCausalLM"),
+    "AquilaForCausalLM": ("llama", "LlamaForCausalLM"),  # AquilaChat2
+    "ArceeForCausalLM": ("arcee", "ArceeForCausalLM"),
+    "ArcticForCausalLM": ("arctic", "ArcticForCausalLM"),
+    "MiniMaxForCausalLM": ("minimax_text_01", "MiniMaxText01ForCausalLM"),
+    "MiniMaxText01ForCausalLM": ("minimax_text_01", "MiniMaxText01ForCausalLM"),
+    "MiniMaxM1ForCausalLM": ("minimax_text_01", "MiniMaxText01ForCausalLM"),
+    # baichuan-7b, upper case 'C' in the class name
+    "BaiChuanForCausalLM": ("baichuan", "BaiChuanForCausalLM"),
+    # baichuan-13b, lower case 'c' in the class name
+    "BaichuanForCausalLM": ("baichuan", "BaichuanForCausalLM"),
+    "BailingMoeForCausalLM": ("bailing_moe", "BailingMoeForCausalLM"),
+    "BambaForCausalLM": ("bamba", "BambaForCausalLM"),
+    "BloomForCausalLM": ("bloom", "BloomForCausalLM"),
+    "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),
+    "ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"),
+    "CohereForCausalLM": ("commandr", "CohereForCausalLM"),
+    "Cohere2ForCausalLM": ("commandr", "CohereForCausalLM"),
+    "DbrxForCausalLM": ("dbrx", "DbrxForCausalLM"),
+    "DeciLMForCausalLM": ("nemotron_nas", "DeciLMForCausalLM"),
+    "DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"),
+    "DeepseekV2ForCausalLM": ("deepseek_v2", "DeepseekV2ForCausalLM"),
+    "DeepseekV3ForCausalLM": ("deepseek_v2", "DeepseekV3ForCausalLM"),
+    "Dots1ForCausalLM": ("dots1", "Dots1ForCausalLM"),
+    "Ernie4_5ForCausalLM": ("ernie45", "Ernie4_5ForCausalLM"),
+    "Ernie4_5_MoeForCausalLM": ("ernie45_moe", "Ernie4_5_MoeForCausalLM"),
+    "ExaoneForCausalLM": ("exaone", "ExaoneForCausalLM"),
+    "Exaone4ForCausalLM": ("exaone4", "Exaone4ForCausalLM"),
+    "FalconForCausalLM": ("falcon", "FalconForCausalLM"),
+    "Fairseq2LlamaForCausalLM": ("fairseq2_llama", "Fairseq2LlamaForCausalLM"),
+    "GemmaForCausalLM": ("gemma", "GemmaForCausalLM"),
+    "Gemma2ForCausalLM": ("gemma2", "Gemma2ForCausalLM"),
+    "Gemma3ForCausalLM": ("gemma3", "Gemma3ForCausalLM"),
+    "Gemma3nForCausalLM": ("gemma3n", "Gemma3nForCausalLM"),
+    "GlmForCausalLM": ("glm", "GlmForCausalLM"),
+    "Glm4ForCausalLM": ("glm4", "Glm4ForCausalLM"),
+    "Glm4MoeForCausalLM": ("glm4_moe", "Glm4MoeForCausalLM"),
+    "GptOssForCausalLM": ("gpt_oss", "GptOssForCausalLM"),
+    "GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"),
+    "GPTBigCodeForCausalLM": ("gpt_bigcode", "GPTBigCodeForCausalLM"),
+    "GPTJForCausalLM": ("gpt_j", "GPTJForCausalLM"),
+    "GPTNeoXForCausalLM": ("gpt_neox", "GPTNeoXForCausalLM"),
+    "GraniteForCausalLM": ("granite", "GraniteForCausalLM"),
+    "GraniteMoeForCausalLM": ("granitemoe", "GraniteMoeForCausalLM"),
+    "GraniteMoeHybridForCausalLM": ("granitemoehybrid", "GraniteMoeHybridForCausalLM"),   # noqa: E501
+    "GraniteMoeSharedForCausalLM": ("granitemoeshared", "GraniteMoeSharedForCausalLM"),   # noqa: E501
+    "GritLM": ("gritlm", "GritLM"),
+    "Grok1ModelForCausalLM": ("grok1", "Grok1ForCausalLM"),
+    "HunYuanMoEV1ForCausalLM": ("hunyuan_v1", "HunYuanMoEV1ForCausalLM"),
+    "HunYuanDenseV1ForCausalLM": ("hunyuan_v1", "HunYuanDenseV1ForCausalLM"),
+    "HCXVisionForCausalLM": ("hyperclovax_vision", "HCXVisionForCausalLM"),
+    "InternLMForCausalLM": ("llama", "LlamaForCausalLM"),
+    "InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"),
+    "InternLM2VEForCausalLM": ("internlm2_ve", "InternLM2VEForCausalLM"),
+    "InternLM3ForCausalLM": ("llama", "LlamaForCausalLM"),
+    "JAISLMHeadModel": ("jais", "JAISLMHeadModel"),
+    "JambaForCausalLM": ("jamba", "JambaForCausalLM"),
+    "LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
+    "Llama4ForCausalLM": ("llama4", "Llama4ForCausalLM"),  # noqa: E501
+    # For decapoda-research/llama-*
+    "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
+    "MambaForCausalLM": ("mamba", "MambaForCausalLM"),
+    "FalconMambaForCausalLM": ("mamba", "MambaForCausalLM"),
+    "FalconH1ForCausalLM":("falcon_h1", "FalconH1ForCausalLM"),
+    "Mamba2ForCausalLM": ("mamba2", "Mamba2ForCausalLM"),
+    "MiniCPMForCausalLM": ("minicpm", "MiniCPMForCausalLM"),
+    "MiniCPM3ForCausalLM": ("minicpm3", "MiniCPM3ForCausalLM"),
+    "MistralForCausalLM": ("llama", "LlamaForCausalLM"),
+    "MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"),
+    "QuantMixtralForCausalLM": ("mixtral_quant", "MixtralForCausalLM"),
+    # transformers's mpt class has lower case
+    "MptForCausalLM": ("mpt", "MPTForCausalLM"),
+    "MPTForCausalLM": ("mpt", "MPTForCausalLM"),
+    "MiMoForCausalLM": ("mimo", "MiMoForCausalLM"),
+    "NemotronForCausalLM": ("nemotron", "NemotronForCausalLM"),
+    "NemotronHForCausalLM": ("nemotron_h", "NemotronHForCausalLM"),
+    "OlmoForCausalLM": ("olmo", "OlmoForCausalLM"),
+    "Olmo2ForCausalLM": ("olmo2", "Olmo2ForCausalLM"),
+    "OlmoeForCausalLM": ("olmoe", "OlmoeForCausalLM"),
+    "OPTForCausalLM": ("opt", "OPTForCausalLM"),
+    "OrionForCausalLM": ("orion", "OrionForCausalLM"),
+    "PersimmonForCausalLM": ("persimmon", "PersimmonForCausalLM"),
+    "PhiForCausalLM": ("phi", "PhiForCausalLM"),
+    "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
+    "PhiMoEForCausalLM": ("phimoe", "PhiMoEForCausalLM"),
+    "Phi4FlashForCausalLM": ("phi4flash", "Phi4FlashForCausalLM"),
+    "Plamo2ForCausalLM": ("plamo2", "Plamo2ForCausalLM"),
+    "QWenLMHeadModel": ("qwen", "QWenLMHeadModel"),
+    "Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),
+    "Qwen2MoeForCausalLM": ("qwen2_moe", "Qwen2MoeForCausalLM"),
+    "Qwen3ForCausalLM": ("qwen3", "Qwen3ForCausalLM"),
+    "Qwen3MoeForCausalLM": ("qwen3_moe", "Qwen3MoeForCausalLM"),
+    "RWForCausalLM": ("falcon", "FalconForCausalLM"),
+    "Step3TextForCausalLM": ("step3_text", "Step3TextForCausalLM"),
+    "StableLMEpochForCausalLM": ("stablelm", "StablelmForCausalLM"),
+    "StableLmForCausalLM": ("stablelm", "StablelmForCausalLM"),
+    "Starcoder2ForCausalLM": ("starcoder2", "Starcoder2ForCausalLM"),
+    "SolarForCausalLM": ("solar", "SolarForCausalLM"),
+    "TeleChat2ForCausalLM": ("telechat2", "TeleChat2ForCausalLM"),
+    "TeleFLMForCausalLM": ("teleflm", "TeleFLMForCausalLM"),
+    "XverseForCausalLM": ("llama", "LlamaForCausalLM"),
+    "Zamba2ForCausalLM": ("zamba2", "Zamba2ForCausalLM"),
+    # [Encoder-decoder]
+    "BartModel": ("bart", "BartForConditionalGeneration"),
+    "BartForConditionalGeneration": ("bart", "BartForConditionalGeneration"),
+    "MBartForConditionalGeneration": ("bart", "MBartForConditionalGeneration"),
+}
+
+_EMBEDDING_MODELS = {
+    # [Text-only]
+    "BertModel": ("bert", "BertEmbeddingModel"),
+    "DeciLMForCausalLM": ("nemotron_nas", "DeciLMForCausalLM"),
+    "Gemma2Model": ("gemma2", "Gemma2ForCausalLM"),
+    "GlmForCausalLM": ("glm", "GlmForCausalLM"),
+    "GPT2ForSequenceClassification": ("gpt2", "GPT2ForSequenceClassification"),
+    "GritLM": ("gritlm", "GritLM"),
+    "GteModel": ("bert_with_rope", "SnowflakeGteNewModel"),
+    "GteNewModel": ("bert_with_rope", "GteNewModel"),
+    "InternLM2ForRewardModel": ("internlm2", "InternLM2ForRewardModel"),
+    "JambaForSequenceClassification": ("jamba", "JambaForSequenceClassification"),  # noqa: E501
+    "LlamaModel": ("llama", "LlamaForCausalLM"),
+    **{
+        # Multiple models share the same architecture, so we include them all
+        k: (mod, arch) for k, (mod, arch) in _TEXT_GENERATION_MODELS.items()
+        if arch == "LlamaForCausalLM"
+    },
+    "MistralModel": ("llama", "LlamaForCausalLM"),
+    "ModernBertModel": ("modernbert", "ModernBertModel"),
+    "NomicBertModel": ("bert_with_rope", "NomicBertModel"),
+    "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),
+    "Qwen2Model": ("qwen2", "Qwen2ForCausalLM"),
+    "Qwen2ForCausalLM": ("qwen2", "Qwen2ForCausalLM"),
+    "Qwen2ForRewardModel": ("qwen2_rm", "Qwen2ForRewardModel"),
+    "Qwen2ForProcessRewardModel": ("qwen2_rm", "Qwen2ForProcessRewardModel"),
+    "RobertaForMaskedLM": ("roberta", "RobertaEmbeddingModel"),
+    "RobertaModel": ("roberta", "RobertaEmbeddingModel"),
+    "TeleChat2ForCausalLM": ("telechat2", "TeleChat2ForCausalLM"),
+    "XLMRobertaModel": ("roberta", "RobertaEmbeddingModel"),
+    # [Multimodal]
+    "LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"),  # noqa: E501
+    "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
+    "Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"),  # noqa: E501
+    # Technically PrithviGeoSpatialMAE is a model that works on images, both in
+    # input and output. I am adding it here because it piggy-backs on embedding
+    # models for the time being.
+    "PrithviGeoSpatialMAE": ("prithvi_geospatial_mae", "PrithviGeoSpatialMAE"),
+}
+
+_CROSS_ENCODER_MODELS = {
+    "BertForSequenceClassification": ("bert", "BertForSequenceClassification"),
+    "RobertaForSequenceClassification": ("roberta",
+                                         "RobertaForSequenceClassification"),
+    "XLMRobertaForSequenceClassification": ("roberta",
+                                            "RobertaForSequenceClassification"),
+    "ModernBertForSequenceClassification": ("modernbert",
+                                            "ModernBertForSequenceClassification"),
+    # [Auto-converted (see adapters.py)]
+    "JinaVLForRanking": ("jina_vl", "JinaVLForSequenceClassification"), # noqa: E501,
+}
+
+_MULTIMODAL_MODELS = {
+    # [Decoder-only]
+    "AriaForConditionalGeneration": ("aria", "AriaForConditionalGeneration"),
+    "AyaVisionForConditionalGeneration": ("aya_vision", "AyaVisionForConditionalGeneration"),  # noqa: E501
+    "Blip2ForConditionalGeneration": ("blip2", "Blip2ForConditionalGeneration"),
+    "ChameleonForConditionalGeneration": ("chameleon", "ChameleonForConditionalGeneration"),  # noqa: E501
+    "Cohere2VisionForConditionalGeneration": ("cohere2_vision", "Cohere2VisionForConditionalGeneration"),  # noqa: E501
+    "DeepseekVLV2ForCausalLM": ("deepseek_vl2", "DeepseekVLV2ForCausalLM"),
+    "FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"),
+    "Gemma3ForConditionalGeneration": ("gemma3_mm", "Gemma3ForConditionalGeneration"),  # noqa: E501
+    "Gemma3nForConditionalGeneration": ("gemma3n_mm", "Gemma3nForConditionalGeneration"),    # noqa: E501
+    "GLM4VForCausalLM": ("glm4v", "GLM4VForCausalLM"),
+    "Glm4vForConditionalGeneration": ("glm4_1v", "Glm4vForConditionalGeneration"),  # noqa: E501
+    "Glm4vMoeForConditionalGeneration": ("glm4_1v", "Glm4vMoeForConditionalGeneration"),  # noqa: E501
+    "GraniteSpeechForConditionalGeneration": ("granite_speech", "GraniteSpeechForConditionalGeneration"),  # noqa: E501
+    "H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
+    "InternVLChatModel": ("internvl", "InternVLChatModel"),
+    "InternS1ForConditionalGeneration": ("interns1", "InternS1ForConditionalGeneration"),  # noqa: E501
+    "Idefics3ForConditionalGeneration":("idefics3","Idefics3ForConditionalGeneration"),
+    "SmolVLMForConditionalGeneration": ("smolvlm","SmolVLMForConditionalGeneration"),  # noqa: E501
+    "KeyeForConditionalGeneration": ("keye", "KeyeForConditionalGeneration"),
+    "KimiVLForConditionalGeneration": ("kimi_vl", "KimiVLForConditionalGeneration"),  # noqa: E501
+    "Llama_Nemotron_Nano_VL": ("nemotron_vl", "LlamaNemotronVLChatModel"),
+    "LlavaForConditionalGeneration": ("llava", "LlavaForConditionalGeneration"),
+    "LlavaNextForConditionalGeneration": ("llava_next", "LlavaNextForConditionalGeneration"),  # noqa: E501
+    "LlavaNextVideoForConditionalGeneration": ("llava_next_video", "LlavaNextVideoForConditionalGeneration"),  # noqa: E501
+    "LlavaOnevisionForConditionalGeneration": ("llava_onevision", "LlavaOnevisionForConditionalGeneration"),  # noqa: E501
+    "MantisForConditionalGeneration": ("llava", "MantisForConditionalGeneration"),  # noqa: E501
+    "MiniMaxVL01ForConditionalGeneration": ("minimax_vl_01", "MiniMaxVL01ForConditionalGeneration"),  # noqa: E501
+    "MiniCPMO": ("minicpmo", "MiniCPMO"),
+    "MiniCPMV": ("minicpmv", "MiniCPMV"),
+    "Mistral3ForConditionalGeneration": ("mistral3", "Mistral3ForConditionalGeneration"),  # noqa: E501
+    "MolmoForCausalLM": ("molmo", "MolmoForCausalLM"),
+    "NVLM_D": ("nvlm_d", "NVLM_D_Model"),
+    "Ovis": ("ovis", "Ovis"),
+    "PaliGemmaForConditionalGeneration": ("paligemma", "PaliGemmaForConditionalGeneration"),  # noqa: E501
+    "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
+    "Phi4MMForCausalLM": ("phi4mm", "Phi4MMForCausalLM"),
+    "Phi4MultimodalForCausalLM": ("phi4_multimodal", "Phi4MultimodalForCausalLM"),  # noqa: E501
+    "PixtralForConditionalGeneration": ("pixtral", "PixtralForConditionalGeneration"),  # noqa: E501
+    "QwenVLForConditionalGeneration": ("qwen_vl", "QwenVLForConditionalGeneration"),  # noqa: E501
+    "Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"),  # noqa: E501
+    "Qwen2_5_VLForConditionalGeneration": ("qwen2_5_vl", "Qwen2_5_VLForConditionalGeneration"),  # noqa: E501
+    "Qwen2AudioForConditionalGeneration": ("qwen2_audio", "Qwen2AudioForConditionalGeneration"),  # noqa: E501
+    "Qwen2_5OmniModel": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"),  # noqa: E501
+    "Qwen2_5OmniForConditionalGeneration": ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"),  # noqa: E501
+    "UltravoxModel": ("ultravox", "UltravoxModel"),
+    "Step3VLForConditionalGeneration": ("step3_vl", "Step3VLForConditionalGeneration"),  # noqa: E501
+    "TarsierForConditionalGeneration": ("tarsier", "TarsierForConditionalGeneration"),  # noqa: E501
+    "Tarsier2ForConditionalGeneration": ("qwen2_vl", "Tarsier2ForConditionalGeneration"),  # noqa: E501
+    "VoxtralForConditionalGeneration": ("voxtral", "VoxtralForConditionalGeneration"),  # noqa: E501
+    # [Encoder-decoder]
+    "Florence2ForConditionalGeneration": ("florence2", "Florence2ForConditionalGeneration"),  # noqa: E501
+    "MllamaForConditionalGeneration": ("mllama", "MllamaForConditionalGeneration"),  # noqa: E501
+    "Llama4ForConditionalGeneration": ("mllama4", "Llama4ForConditionalGeneration"),  # noqa: E501
+    "SkyworkR1VChatModel": ("skyworkr1v", "SkyworkR1VChatModel"),
+    "WhisperForConditionalGeneration": ("whisper", "WhisperForConditionalGeneration"),  # noqa: E501
+}
+
+_SPECULATIVE_DECODING_MODELS = {
+    "MiMoMTPModel": ("mimo_mtp", "MiMoMTP"),
+    "EagleLlamaForCausalLM": ("llama_eagle", "EagleLlamaForCausalLM"),
+    "EagleLlama4ForCausalLM": ("llama4_eagle", "EagleLlama4ForCausalLM"),
+    "EagleMiniCPMForCausalLM": ("minicpm_eagle", "EagleMiniCPMForCausalLM"),
+    "Eagle3LlamaForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
+    # TODO: Re-enable this once tests/models/test_initialization.py is fixed, see PR #22333 #22611  # noqa: E501
+    # "LlamaForCausalLMEagle3": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
+    "DeepSeekMTPModel": ("deepseek_mtp", "DeepSeekMTP"),
+    "Glm4MoeMTPModel": ("glm4_moe_mtp", "Glm4MoeMTP"),
+    "MedusaModel": ("medusa", "Medusa"),
+    # Temporarily disabled.
+    # # TODO(woosuk): Re-enable this once the MLP Speculator is supported in V1.
+    # "MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"),
+}
+
+_TRANSFORMERS_SUPPORTED_MODELS = {
+    # Text generation models
+    "SmolLM3ForCausalLM": ("transformers", "TransformersForCausalLM"),
+    # Multimodal models
+    "Emu3ForConditionalGeneration": ("transformers", "TransformersForMultimodalLM"),  # noqa: E501
+}
+
+_TRANSFORMERS_BACKEND_MODELS = {
+    "TransformersModel": ("transformers", "TransformersModel"),
+    "TransformersForCausalLM": ("transformers", "TransformersForCausalLM"),
+    "TransformersForMultimodalLM": ("transformers", "TransformersForMultimodalLM"), # noqa: E501
+}
+# yapf: enable
+
+_VLLM_MODELS = {
+    **_TEXT_GENERATION_MODELS,
+    **_EMBEDDING_MODELS,
+    **_CROSS_ENCODER_MODELS,
+    **_MULTIMODAL_MODELS,
+    **_SPECULATIVE_DECODING_MODELS,
+    **_TRANSFORMERS_SUPPORTED_MODELS,
+    **_TRANSFORMERS_BACKEND_MODELS,
+}
+
+# This variable is used as the args for subprocess.run(). We
+# can modify  this variable to alter the args if needed. e.g.
+# when we use par format to pack things together, sys.executable
+# might not be the target we want to run.
+_SUBPROCESS_COMMAND = [
+    sys.executable, "-m", "vllm.model_executor.models.registry"
+]
+
+_PREVIOUSLY_SUPPORTED_MODELS = {"Phi3SmallForCausalLM": "0.9.2"}
+
+
+@dataclass(frozen=True)
+class _ModelInfo:
+    architecture: str
+    is_text_generation_model: bool
+    is_pooling_model: bool
+    default_pooling_type: str
+    supports_cross_encoding: bool
+    supports_multimodal: bool
+    supports_multimodal_raw_input: bool
+    supports_pp: bool
+    has_inner_state: bool
+    is_attention_free: bool
+    is_hybrid: bool
+    has_noops: bool
+    supports_transcription: bool
+    supports_transcription_only: bool
+    supports_v0_only: bool
+
+    @staticmethod
+    def from_model_cls(model: type[nn.Module]) -> "_ModelInfo":
+        return _ModelInfo(
+            architecture=model.__name__,
+            is_text_generation_model=is_text_generation_model(model),
+            is_pooling_model=is_pooling_model(model),
+            default_pooling_type=get_default_pooling_type(model),
+            supports_cross_encoding=supports_cross_encoding(model),
+            supports_multimodal=supports_multimodal(model),
+            supports_multimodal_raw_input=supports_multimodal_raw_input(model),
+            supports_pp=supports_pp(model),
+            has_inner_state=has_inner_state(model),
+            is_attention_free=is_attention_free(model),
+            is_hybrid=is_hybrid(model),
+            supports_transcription=supports_transcription(model),
+            supports_transcription_only=(supports_transcription(model) and
+                                         model.supports_transcription_only),
+            supports_v0_only=supports_v0_only(model),
+            has_noops=has_noops(model),
+        )
+
+
+class _BaseRegisteredModel(ABC):
+
+    @abstractmethod
+    def inspect_model_cls(self) -> _ModelInfo:
+        raise NotImplementedError
+
+    @abstractmethod
+    def load_model_cls(self) -> type[nn.Module]:
+        raise NotImplementedError
+
+
+@dataclass(frozen=True)
+class _RegisteredModel(_BaseRegisteredModel):
+    """
+    Represents a model that has already been imported in the main process.
+    """
+
+    interfaces: _ModelInfo
+    model_cls: type[nn.Module]
+
+    @staticmethod
+    def from_model_cls(model_cls: type[nn.Module]):
+        return _RegisteredModel(
+            interfaces=_ModelInfo.from_model_cls(model_cls),
+            model_cls=model_cls,
+        )
+
+    def inspect_model_cls(self) -> _ModelInfo:
+        return self.interfaces
+
+    def load_model_cls(self) -> type[nn.Module]:
+        return self.model_cls
+
+
+@dataclass(frozen=True)
+class _LazyRegisteredModel(_BaseRegisteredModel):
+    """
+    Represents a model that has not been imported in the main process.
+    """
+    module_name: str
+    class_name: str
+
+    # Performed in another process to avoid initializing CUDA
+    def inspect_model_cls(self) -> _ModelInfo:
+        return _run_in_subprocess(
+            lambda: _ModelInfo.from_model_cls(self.load_model_cls()))
+
+    def load_model_cls(self) -> type[nn.Module]:
+        mod = importlib.import_module(self.module_name)
+        return getattr(mod, self.class_name)
+
+
+@lru_cache(maxsize=128)
+def _try_load_model_cls(
+    model_arch: str,
+    model: _BaseRegisteredModel,
+) -> Optional[type[nn.Module]]:
+    from vllm.platforms import current_platform
+    current_platform.verify_model_arch(model_arch)
+    try:
+        return model.load_model_cls()
+    except Exception:
+        logger.exception("Error in loading model architecture '%s'",
+                         model_arch)
+        return None
+
+
+@lru_cache(maxsize=128)
+def _try_inspect_model_cls(
+    model_arch: str,
+    model: _BaseRegisteredModel,
+) -> Optional[_ModelInfo]:
+    try:
+        return model.inspect_model_cls()
+    except Exception:
+        logger.exception("Error in inspecting model architecture '%s'",
+                         model_arch)
+        return None
+
+
+@dataclass
+class _ModelRegistry:
+    # Keyed by model_arch
+    models: dict[str, _BaseRegisteredModel] = field(default_factory=dict)
+
+    def get_supported_archs(self) -> Set[str]:
+        return self.models.keys()
+
+    def register_model(
+        self,
+        model_arch: str,
+        model_cls: Union[type[nn.Module], str],
+    ) -> None:
+        """
+        Register an external model to be used in vLLM.
+
+        `model_cls` can be either:
+
+        - A [`torch.nn.Module`][] class directly referencing the model.
+        - A string in the format `<module>:<class>` which can be used to
+          lazily import the model. This is useful to avoid initializing CUDA
+          when importing the model and thus the related error
+          `RuntimeError: Cannot re-initialize CUDA in forked subprocess`.
+        """
+        if not isinstance(model_arch, str):
+            msg = f"`model_arch` should be a string, not a {type(model_arch)}"
+            raise TypeError(msg)
+
+        if model_arch in self.models:
+            logger.warning(
+                "Model architecture %s is already registered, and will be "
+                "overwritten by the new model class %s.", model_arch,
+                model_cls)
+
+        if isinstance(model_cls, str):
+            split_str = model_cls.split(":")
+            if len(split_str) != 2:
+                msg = "Expected a string in the format `<module>:<class>`"
+                raise ValueError(msg)
+
+            model = _LazyRegisteredModel(*split_str)
+        elif isinstance(model_cls, type) and issubclass(model_cls, nn.Module):
+            model = _RegisteredModel.from_model_cls(model_cls)
+        else:
+            msg = ("`model_cls` should be a string or PyTorch model class, "
+                   f"not a {type(model_arch)}")
+            raise TypeError(msg)
+
+        self.models[model_arch] = model
+
+    def _raise_for_unsupported(self, architectures: list[str]):
+        all_supported_archs = self.get_supported_archs()
+
+        if any(arch in all_supported_archs for arch in architectures):
+            raise ValueError(
+                f"Model architectures {architectures} failed "
+                "to be inspected. Please check the logs for more details.")
+
+        for arch in architectures:
+            if arch in _PREVIOUSLY_SUPPORTED_MODELS:
+                previous_version = _PREVIOUSLY_SUPPORTED_MODELS[arch]
+
+                raise ValueError(
+                    f"Model architecture {arch} was supported in vLLM until "
+                    f"v{previous_version}, and is not supported anymore. "
+                    "Please use an older version of vLLM if you want to "
+                    "use this model architecture.")
+
+        raise ValueError(
+            f"Model architectures {architectures} are not supported for now. "
+            f"Supported architectures: {all_supported_archs}")
+
+    def _try_load_model_cls(self,
+                            model_arch: str) -> Optional[type[nn.Module]]:
+        if model_arch not in self.models:
+            return None
+
+        return _try_load_model_cls(model_arch, self.models[model_arch])
+
+    def _try_inspect_model_cls(self, model_arch: str) -> Optional[_ModelInfo]:
+        if model_arch not in self.models:
+            return None
+
+        return _try_inspect_model_cls(model_arch, self.models[model_arch])
+
+    def _try_resolve_transformers(
+        self,
+        architecture: str,
+        model_config: ModelConfig,
+    ) -> Optional[str]:
+        if architecture in _TRANSFORMERS_BACKEND_MODELS:
+            return architecture
+
+        auto_map: dict[str, str] = getattr(model_config.hf_config, "auto_map",
+                                           None) or dict()
+
+        # Make sure that config class is always initialized before model class,
+        # otherwise the model class won't be able to access the config class,
+        # the expected auto_map should have correct order like:
+        # "auto_map": {
+        #     "AutoConfig": "<your-repo-name>--<config-name>",
+        #     "AutoModel": "<your-repo-name>--<config-name>",
+        #     "AutoModelFor<Task>": "<your-repo-name>--<config-name>",
+        # },
+        for prefix in ("AutoConfig", "AutoModel"):
+            for name, module in auto_map.items():
+                if name.startswith(prefix):
+                    try_get_class_from_dynamic_module(
+                        module,
+                        model_config.model,
+                        revision=model_config.revision,
+                        warn_on_fail=False,
+                    )
+
+        model_module = getattr(transformers, architecture, None)
+
+        if model_module is None:
+            for name, module in auto_map.items():
+                if name.startswith("AutoModel"):
+                    model_module = try_get_class_from_dynamic_module(
+                        module,
+                        model_config.model,
+                        revision=model_config.revision,
+                        warn_on_fail=True,
+                    )
+                    if model_module is not None:
+                        break
+            else:
+                if model_config.model_impl != ModelImpl.TRANSFORMERS:
+                    return None
+
+                raise ValueError(
+                    f"Cannot find model module. {architecture!r} is not a "
+                    "registered model in the Transformers library (only "
+                    "relevant if the model is meant to be in Transformers) "
+                    "and 'AutoModel' is not present in the model config's "
+                    "'auto_map' (relevant if the model is custom).")
+
+        if not model_module.is_backend_compatible():
+            if model_config.model_impl != ModelImpl.TRANSFORMERS:
+                return None
+
+            raise ValueError(
+                f"The Transformers implementation of {architecture!r} "
+                "is not compatible with vLLM.")
+
+        return model_config._get_transformers_backend_cls()
+
+    def _normalize_arch(
+        self,
+        architecture: str,
+        model_config: ModelConfig,
+    ) -> str:
+        if architecture in self.models:
+            return architecture
+
+        # This may be called in order to resolve runner_type and convert_type
+        # in the first place, in which case we consider the default match
+        match = try_match_architecture_defaults(
+            architecture,
+            runner_type=getattr(model_config, "runner_type", None),
+            convert_type=getattr(model_config, "convert_type", None),
+        )
+        if match:
+            suffix, _ = match
+
+            # Get the name of the base model to convert
+            for repl_suffix, _ in iter_architecture_defaults():
+                base_arch = architecture.replace(suffix, repl_suffix)
+                if base_arch in self.models:
+                    return base_arch
+
+        return architecture
+
+    def inspect_model_cls(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> tuple[_ModelInfo, str]:
+        if isinstance(architectures, str):
+            architectures = [architectures]
+        if not architectures:
+            raise ValueError("No model architectures are specified")
+
+        # Require transformers impl
+        if model_config.model_impl == ModelImpl.TRANSFORMERS:
+            arch = self._try_resolve_transformers(architectures[0],
+                                                  model_config)
+            if arch is not None:
+                model_info = self._try_inspect_model_cls(arch)
+                if model_info is not None:
+                    return (model_info, arch)
+
+        # Fallback to transformers impl (after resolving convert_type)
+        if (all(arch not in self.models for arch in architectures)
+                and model_config.model_impl == ModelImpl.AUTO
+                and getattr(model_config, "convert_type", "none") == "none"):
+            arch = self._try_resolve_transformers(architectures[0],
+                                                  model_config)
+            if arch is not None:
+                model_info = self._try_inspect_model_cls(arch)
+                if model_info is not None:
+                    return (model_info, arch)
+
+        for arch in architectures:
+            normalized_arch = self._normalize_arch(arch, model_config)
+            model_info = self._try_inspect_model_cls(normalized_arch)
+            if model_info is not None:
+                return (model_info, arch)
+
+        # Fallback to transformers impl (before resolving runner_type)
+        if (all(arch not in self.models for arch in architectures)
+                and model_config.model_impl == ModelImpl.AUTO):
+            arch = self._try_resolve_transformers(architectures[0],
+                                                  model_config)
+            if arch is not None:
+                model_info = self._try_inspect_model_cls(arch)
+                if model_info is not None:
+                    return (model_info, arch)
+
+        return self._raise_for_unsupported(architectures)
+
+    def resolve_model_cls(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> tuple[type[nn.Module], str]:
+        if isinstance(architectures, str):
+            architectures = [architectures]
+        if not architectures:
+            raise ValueError("No model architectures are specified")
+
+        # Require transformers impl
+        if model_config.model_impl == ModelImpl.TRANSFORMERS:
+            arch = self._try_resolve_transformers(architectures[0],
+                                                  model_config)
+            if arch is not None:
+                model_cls = self._try_load_model_cls(arch)
+                if model_cls is not None:
+                    return (model_cls, arch)
+
+        # Fallback to transformers impl (after resolving convert_type)
+        if (all(arch not in self.models for arch in architectures)
+                and model_config.model_impl == ModelImpl.AUTO
+                and getattr(model_config, "convert_type", "none") == "none"):
+            arch = self._try_resolve_transformers(architectures[0],
+                                                  model_config)
+            if arch is not None:
+                model_cls = self._try_load_model_cls(arch)
+                if model_cls is not None:
+                    return (model_cls, arch)
+
+        for arch in architectures:
+            normalized_arch = self._normalize_arch(arch, model_config)
+            model_cls = self._try_load_model_cls(normalized_arch)
+            if model_cls is not None:
+                return (model_cls, arch)
+
+        # Fallback to transformers impl (before resolving runner_type)
+        if (all(arch not in self.models for arch in architectures)
+                and model_config.model_impl == ModelImpl.AUTO):
+            arch = self._try_resolve_transformers(architectures[0],
+                                                  model_config)
+            if arch is not None:
+                model_cls = self._try_load_model_cls(arch)
+                if model_cls is not None:
+                    return (model_cls, arch)
+
+        return self._raise_for_unsupported(architectures)
+
+    def is_text_generation_model(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.is_text_generation_model
+
+    def is_pooling_model(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.is_pooling_model
+
+    def is_cross_encoder_model(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.supports_cross_encoding
+
+    def is_multimodal_model(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.supports_multimodal
+
+    def supports_multimodal_raw_input(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.supports_multimodal_raw_input
+
+    def is_pp_supported_model(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.supports_pp
+
+    def model_has_inner_state(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.has_inner_state
+
+    def is_attention_free_model(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.is_attention_free
+
+    def is_hybrid_model(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.is_hybrid
+
+    def is_noops_model(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.has_noops
+
+    def is_transcription_model(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.supports_transcription
+
+    def is_transcription_only_model(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return model_cls.supports_transcription_only
+
+    def is_v1_compatible(
+        self,
+        architectures: Union[str, list[str]],
+        model_config: ModelConfig,
+    ) -> bool:
+        model_cls, _ = self.inspect_model_cls(architectures, model_config)
+        return not model_cls.supports_v0_only
+
+
+ModelRegistry = _ModelRegistry({
+    model_arch:
+    _LazyRegisteredModel(
+        module_name=f"vllm.model_executor.models.{mod_relname}",
+        class_name=cls_name,
+    )
+    for model_arch, (mod_relname, cls_name) in _VLLM_MODELS.items()
+})
+
+_T = TypeVar("_T")
+
+
+def _run_in_subprocess(fn: Callable[[], _T]) -> _T:
+    # NOTE: We use a temporary directory instead of a temporary file to avoid
+    # issues like https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file
+    with tempfile.TemporaryDirectory() as tempdir:
+        output_filepath = os.path.join(tempdir, "registry_output.tmp")
+
+        # `cloudpickle` allows pickling lambda functions directly
+        import cloudpickle
+        input_bytes = cloudpickle.dumps((fn, output_filepath))
+
+        # cannot use `sys.executable __file__` here because the script
+        # contains relative imports
+        returned = subprocess.run(_SUBPROCESS_COMMAND,
+                                  input=input_bytes,
+                                  capture_output=True)
+
+        # check if the subprocess is successful
+        try:
+            returned.check_returncode()
+        except Exception as e:
+            # wrap raised exception to provide more information
+            raise RuntimeError(f"Error raised in subprocess:\n"
+                               f"{returned.stderr.decode()}") from e
+
+        with open(output_filepath, "rb") as f:
+            return pickle.load(f)
+
+
+def _run() -> None:
+    # Setup plugins
+    from vllm.plugins import load_general_plugins
+    load_general_plugins()
+
+    fn, output_file = pickle.loads(sys.stdin.buffer.read())
+
+    result = fn()
+
+    with open(output_file, "wb") as f:
+        f.write(pickle.dumps(result))
+
+
+if __name__ == "__main__":
+    _run()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/roberta.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/roberta.py
new file mode 100644
index 0000000000000000000000000000000000000000..32a4a2c9a26941bb902ee87d0d34e87bd8452ceb
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/roberta.py
@@ -0,0 +1,290 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import RobertaConfig
+
+from vllm.config import VllmConfig
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.pooler import (ClassifierPooler, CLSPool,
+                                               DispatchPooler, Pooler)
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.models.bert import (TOKEN_TYPE_SHIFT,
+                                             BertEmbeddingModel, BertModel,
+                                             _decode_token_type_ids,
+                                             _encode_token_type_ids)
+from vllm.model_executor.models.utils import (AutoWeightsLoader, WeightsMapper,
+                                              maybe_prefix)
+from vllm.sequence import IntermediateTensors
+
+from .bert_with_rope import BertWithRope, JinaRobertaModel
+from .interfaces import SupportsCrossEncoding, default_pooling_type
+
+
+class RobertaEmbedding(nn.Module):
+
+    def __init__(self, config: RobertaConfig):
+        super().__init__()
+        self.size = config.hidden_size
+        self.word_embeddings = VocabParallelEmbedding(config.vocab_size,
+                                                      config.hidden_size)
+        self.padding_idx = config.pad_token_id
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings,
+                                                config.hidden_size,
+                                                padding_idx=self.padding_idx)
+
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
+                                                  config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size,
+                                      eps=config.layer_norm_eps)
+        self.register_buffer(
+            "position_ids",
+            torch.arange(config.max_position_embeddings).unsqueeze(0),
+        )
+
+        self.position_embedding_type = config.position_embedding_type
+        if self.position_embedding_type != "absolute":
+            raise ValueError("Only 'absolute' position_embedding_type" +
+                             " is supported")
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+    ) -> torch.Tensor:
+
+        token_type_ids = _decode_token_type_ids(input_ids)
+
+        inputs_embeds = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+        embeddings = inputs_embeds + token_type_embeddings + position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        return embeddings
+
+
+# Adapted from transformers
+class RobertaClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config: RobertaConfig):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # CLSPool has already been applied in `pooling`
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.out_proj(x)
+        return x
+
+
+@default_pooling_type("CLS")
+class RobertaEmbeddingModel(BertEmbeddingModel):
+    """A model that uses Roberta to provide embedding functionalities.
+
+   This class encapsulates the BertModel and provides an interface for
+   embedding operations and customized pooling functions.
+
+   Attributes:
+       model: An instance of BertModel used for forward operations.
+       _pooler: An instance of Pooler used for pooling operations.
+   """
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        self.padding_idx = vllm_config.model_config.hf_config.pad_token_id
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        # Fix Roberta positions here outside of the CUDA graph.
+        # Because we need the to extract the sequences from
+        # input_ids the control flow is data dependent.
+        replace_roberta_positions(input_ids=input_ids,
+                                  position_ids=positions,
+                                  padding_idx=self.padding_idx)
+
+        return self.model(input_ids=input_ids,
+                          positions=positions,
+                          inputs_embeds=inputs_embeds,
+                          intermediate_tensors=intermediate_tensors)
+
+    def _build_model(self,
+                     vllm_config: VllmConfig,
+                     prefix: str = "") -> Union[BertModel, BertWithRope]:
+        if (vllm_config.model_config.hf_config.position_embedding_type ==
+                "rotary"):
+            return JinaRobertaModel(vllm_config=vllm_config, prefix=prefix)
+        else:
+            return BertModel(vllm_config=vllm_config,
+                             prefix=prefix,
+                             embedding_class=RobertaEmbedding)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        weights_list = list(weights)
+        has_roberta_prefix = any(
+            name.startswith("roberta.") for name, _ in weights_list)
+        if has_roberta_prefix:
+            # For models with the `roberta.` prefix e.g.
+            # `FacebookAI/roberta-base`
+            mapper = WeightsMapper(orig_to_new_prefix={"roberta.": "model."})
+        else:
+            # For models without the `roberta.` prefix e.g.
+            # `sentence-transformers/stsb-roberta-base-v2`
+            mapper = WeightsMapper(orig_to_new_prefix={"": "model."})
+
+        loader = AutoWeightsLoader(self, skip_prefixes=["lm_head."])
+        return loader.load_weights(weights_list, mapper=mapper)
+
+
+@default_pooling_type("CLS")
+class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding):
+    """A model that uses Roberta to provide embedding functionalities.
+
+   This class encapsulates the BertModel and provides an interface for
+   embedding operations and customized pooling functions.
+
+   Attributes:
+       roberta: An instance of BertModel used for forward operations.
+       _pooler: An instance of Pooler used for pooling operations.
+   """
+
+    is_pooling_model = True
+    jina_to_vllm_mapper = WeightsMapper(
+        orig_to_new_substr={
+            'emb_ln': "embeddings.LayerNorm",
+            'layers': "layer",
+            'mixer.Wqkv': "attention.self.qkv_proj",
+            'mixer.out_proj': "attention.output.dense",
+            'norm1': "attention.output.LayerNorm",
+            'mlp.fc1': "intermediate.dense",
+            'mlp.fc2': "output.dense",
+            'norm2': "output.LayerNorm",
+        })
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.padding_idx = vllm_config.model_config.hf_config.pad_token_id
+
+        self.num_labels = config.num_labels
+        self.roberta = BertModel(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "bert"),
+                                 embedding_class=RobertaEmbedding)
+        self.classifier = RobertaClassificationHead(config)
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.pooler = DispatchPooler({
+            "encode":
+            Pooler.for_encode(pooler_config),
+            "classify":
+            ClassifierPooler(
+                pooling=CLSPool(),
+                classifier=self.classifier,
+                act_fn=ClassifierPooler.act_fn_for_seq_cls(
+                    vllm_config.model_config),
+            ),
+            "score":
+            ClassifierPooler(
+                pooling=CLSPool(),
+                classifier=self.classifier,
+                act_fn=ClassifierPooler.act_fn_for_cross_encoder(
+                    vllm_config.model_config),
+            ),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.jina_to_vllm_mapper)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        replace_roberta_positions(input_ids=input_ids,
+                                  position_ids=positions,
+                                  padding_idx=self.padding_idx)
+        if token_type_ids is not None:
+            assert self.roberta.config.vocab_size < (1 << TOKEN_TYPE_SHIFT)
+            assert input_ids is not None
+            _encode_token_type_ids(input_ids, token_type_ids)
+        return self.roberta(input_ids=input_ids,
+                            positions=positions,
+                            inputs_embeds=inputs_embeds,
+                            intermediate_tensors=intermediate_tensors)
+
+
+# Adapted from transformers
+def create_position_ids_from_input_ids(input_ids,
+                                       padding_idx,
+                                       past_key_values_length=0):
+    """
+    Replace non-padding symbols with their position numbers.
+    Position numbers begin at padding_idx+1. Padding symbols
+    are ignored. This is modified from fairseq's `utils.make_positions`.
+
+    Args:
+        x: torch.Tensor x:
+
+    Returns: torch.Tensor
+    """
+    # The series of casts and type-conversions here are carefully
+    # balanced to both work with ONNX export and XLA.
+    mask = input_ids.ne(padding_idx).int()
+
+    incremental_indices = (torch.cumsum(mask, dim=0).type_as(mask) +
+                           past_key_values_length) * mask
+
+    return incremental_indices.long() + padding_idx
+
+
+def replace_roberta_positions(input_ids: torch.Tensor,
+                              position_ids: torch.Tensor,
+                              padding_idx: int) -> None:
+
+    seq_lens: Optional[torch.Tensor] = None
+    attn_metadata = get_forward_context().attn_metadata
+    if attn_metadata is not None:  # can be None during warmup
+        if isinstance(attn_metadata, dict):
+            attn_metadata = next(iter(attn_metadata.values()))
+        # TODO: remove "seq_lens_tensor" after V0 is removed
+        seq_lens = getattr(attn_metadata, "seq_lens_tensor",
+                           getattr(attn_metadata, "seq_lens", None))
+
+    if seq_lens is not None:
+        assert isinstance(seq_lens, torch.Tensor)
+
+        # Replace position ids because in RoBERTa models
+        # they have to start at padding_idx + 1 and ignore
+        # existing padding tokens
+        # References:
+        # - https://github.com/huggingface/transformers/blob/a3d69a8994d673899608a7c17fbf4f953f50474e/src/transformers/models/roberta/modeling_roberta.py#L133
+        # - https://github.com/huggingface/transformers/blob/a3d69a8994d673899608a7c17fbf4f953f50474e/src/transformers/models/roberta/modeling_roberta.py#L1669
+        token_list = torch.split(input_ids[:torch.sum(seq_lens)],
+                                 seq_lens.tolist())
+
+        offset = 0
+        for tokens in token_list:
+            length = tokens.shape[0]
+            position_ids[offset:offset+length] = \
+                create_position_ids_from_input_ids(tokens, padding_idx)
+            offset = offset + length
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/skyworkr1v.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/skyworkr1v.py
new file mode 100644
index 0000000000000000000000000000000000000000..c76aabcd27ccb00f284c55ba81a760a84d90fd7c
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/skyworkr1v.py
@@ -0,0 +1,915 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B/blob/main/modeling_skywork_chat.py
+# --------------------------------------------------------
+# SkyworkR1V
+# Copyright (c) 2025 Skywork
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Literal, Optional, TypedDict, Union
+
+import torch
+import torch.nn as nn
+import torchvision.transforms as T
+from PIL import Image
+from transformers import BatchEncoding, PretrainedConfig, TensorType
+
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.awq import AWQConfig
+from vllm.model_executor.models.intern_vit import (InternVisionModel,
+                                                   InternVisionPatchModel)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.image import convert_image_mode
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs, NestedTensors)
+from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
+                                   ImageSize, MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement,
+                                        PromptUpdate, PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
+                    maybe_prefix, merge_multimodal_embeddings)
+
+IMG_START = '<img>'
+IMG_END = '</img>'
+IMG_CONTEXT = '<IMG_CONTEXT>'
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+class SkyworkR1VImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values_flat: torch.Tensor
+    """
+    Shape:
+    `(batch_size * num_images * (1 + num_patches), num_channels, height, width)`
+    """
+
+    num_patches: torch.Tensor
+    """Shape: `(batch_size * num_images)`"""
+
+
+class SkyworkR1VImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: Union[torch.Tensor, list[torch.Tensor]]
+    """ 
+    A tensor of shape `(num_images, total_image_feature_size, hidden_size)`
+    or a list of tensors of shape `(total_image_feature_size, hidden_size)`
+
+    `hidden_size` must match the hidden size of language model backbone.
+    """
+
+
+SkyworkR1VImageInputs = Union[SkyworkR1VImagePixelInputs,
+                              SkyworkR1VImageEmbeddingInputs]
+
+
+# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B/
+def build_transform(input_size: int):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    return T.Compose([
+        T.Lambda(lambda img: convert_image_mode(img, 'RGB')),
+        T.Resize((input_size, input_size),
+                 interpolation=T.InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+
+
+# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B/
+def find_closest_aspect_ratio(
+    aspect_ratio: float,
+    target_ratios: list[tuple[int, int]],
+    *,
+    width: int,
+    height: int,
+    image_size: int,
+) -> tuple[int, int]:
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+
+def resolve_skyworkr1v_min_max_num(
+    *,
+    min_dynamic_patch: int,
+    max_dynamic_patch: int,
+    dynamic_image_size: bool,
+    use_thumbnail: bool,
+) -> tuple[int, int]:
+    min_dynamic_patch = min_dynamic_patch if dynamic_image_size else 1
+    max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1
+
+    if use_thumbnail and max_dynamic_patch != 1:
+        max_dynamic_patch += 1
+
+    return min_dynamic_patch, max_dynamic_patch
+
+
+def get_skyworkr1v_target_ratios(
+    min_num: int,
+    max_num: int,
+) -> list[tuple[int, int]]:
+    target_ratios = {(i, j)
+                     for n in range(min_num, max_num + 1)
+                     for i in range(1, n + 1)
+                     for j in range(1, n + 1) if min_num <= i * j <= max_num}
+    return sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+
+def calculate_skyworkr1v_targets(
+    *,
+    orig_width: int,
+    orig_height: int,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> tuple[int, int, int]:
+    aspect_ratio = orig_width / orig_height
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio,
+        target_ratios,
+        width=orig_width,
+        height=orig_height,
+        image_size=image_size,
+    )
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # add thumbnail image if num_blocks != 1
+    if use_thumbnail and blocks != 1:
+        blocks += 1
+
+    return blocks, target_width, target_height
+
+
+def dynamic_preprocess_skyworkr1v(
+    image: Image.Image,
+    *,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> list[Image.Image]:
+    orig_width, orig_height = image.size
+
+    # calculate the number of blocks without thumbnail
+    blocks, target_width, target_height = calculate_skyworkr1v_targets(
+        orig_width=orig_width,
+        orig_height=orig_height,
+        target_ratios=target_ratios,
+        image_size=image_size,
+        use_thumbnail=False,
+    )
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = ((i % (target_width // image_size)) * image_size,
+               (i // (target_width // image_size)) * image_size,
+               ((i % (target_width // image_size)) + 1) * image_size,
+               ((i // (target_width // image_size)) + 1) * image_size)
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+
+    assert len(processed_images) == blocks
+
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+
+    return processed_images
+
+
+# adapted from https://huggingface.co/Skywork/Skywork-R1V-38B
+def image_to_pixel_values_skyworkr1v(
+    image: Image.Image,
+    *,
+    input_size: int,
+    min_num: int,
+    max_num: int,
+    use_thumbnail: bool,
+) -> torch.Tensor:
+    target_ratios = get_skyworkr1v_target_ratios(min_num, max_num)
+
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess_skyworkr1v(
+        image,
+        target_ratios=target_ratios,
+        image_size=input_size,
+        use_thumbnail=use_thumbnail,
+    )
+
+    pixel_values = torch.stack([transform(image) for image in images])
+    return pixel_values
+
+
+class SkyworkR1VProcessor:
+    """
+    This model doesn't define its own HF processor,
+    so we implement our own one here.
+
+    The code to insert image tokens is based on:
+    https://huggingface.co/Skywork/Skywork-R1V-38B/blob/main/modeling_skywork_chat.py#L252
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        tokenizer: AnyTokenizer,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.tokenizer = tokenizer
+
+        image_size: int = config.vision_config.image_size
+        patch_size: int = config.vision_config.patch_size
+
+        if min_dynamic_patch is None:
+            min_dynamic_patch = config.min_dynamic_patch
+        assert isinstance(min_dynamic_patch, int)
+
+        if max_dynamic_patch is None:
+            max_dynamic_patch = config.max_dynamic_patch
+        assert isinstance(max_dynamic_patch, int)
+
+        if dynamic_image_size is None:
+            dynamic_image_size = config.dynamic_image_size
+        assert isinstance(dynamic_image_size, bool)
+
+        self.num_image_token = int(
+            (image_size // patch_size)**2 * (config.downsample_ratio**2))
+        self.image_size = image_size
+        self.min_dynamic_patch = min_dynamic_patch
+        self.max_dynamic_patch = max_dynamic_patch
+        self.dynamic_image_size = dynamic_image_size
+        self.use_thumbnail: bool = config.use_thumbnail
+
+    @property
+    def image_token_id(self) -> int:
+        return self.tokenizer.get_vocab()[IMG_CONTEXT]
+
+    def get_image_repl(
+        self,
+        feature_size: int,
+        num_patches: Optional[int],
+    ) -> PromptUpdateDetails[str]:
+        repl_features = IMG_CONTEXT * feature_size
+        repl_full = IMG_START + repl_features + IMG_END
+
+        return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
+
+    def resolve_min_max_num(
+        self,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        use_thumbnail: Optional[bool] = None,
+    ) -> tuple[int, int]:
+        min_dynamic_patch = (self.min_dynamic_patch if min_dynamic_patch
+                             is None else min_dynamic_patch)
+        max_dynamic_patch = (self.max_dynamic_patch if max_dynamic_patch
+                             is None else max_dynamic_patch)
+        dynamic_image_size = (self.dynamic_image_size if dynamic_image_size
+                              is None else dynamic_image_size)
+        use_thumbnail = (self.use_thumbnail
+                         if use_thumbnail is None else use_thumbnail)
+
+        return resolve_skyworkr1v_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=use_thumbnail,
+        )
+
+    def resolve_target_ratios(
+        self,
+        *,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        use_thumbnail: Optional[bool] = None,
+    ) -> list[tuple[int, int]]:
+        min_num, max_num = self.resolve_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=use_thumbnail,
+        )
+
+        return get_skyworkr1v_target_ratios(min_num, max_num)
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        target_ratios = self.resolve_target_ratios(
+            use_thumbnail=False,  # Applied in calculate_targets
+        )
+
+        num_patches, _, _ = calculate_skyworkr1v_targets(
+            orig_width=image_width,
+            orig_height=image_height,
+            image_size=self.image_size,
+            target_ratios=target_ratios,
+            use_thumbnail=self.use_thumbnail,
+        )
+
+        return num_patches * self.num_image_token
+
+    def _images_to_pixel_values_lst(
+        self,
+        images: list[Image.Image],
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> list[torch.Tensor]:
+        min_num, max_num = self.resolve_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=False,  # Applied in image_to_pixel_values
+        )
+
+        return [
+            image_to_pixel_values_skyworkr1v(
+                image,
+                input_size=self.image_size,
+                min_num=min_num,
+                max_num=max_num,
+                use_thumbnail=self.use_thumbnail,
+            ) for image in images
+        ]
+
+    def __call__(
+        self,
+        text: Optional[Union[str, list[str]]] = None,
+        images: Optional[Union[Image.Image, list[Image.Image]]] = None,
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+    ) -> Mapping[str, NestedTensors]:
+        if text is None:
+            text = []
+        if not isinstance(text, list):
+            text = [text]
+        if images is None:
+            images = []
+        if not isinstance(images, list):
+            images = [images]
+
+        if len(images) == 0:
+            image_inputs = {}
+        else:
+            pixel_values_lst = self._images_to_pixel_values_lst(
+                images,
+                min_dynamic_patch=min_dynamic_patch,
+                max_dynamic_patch=max_dynamic_patch,
+                dynamic_image_size=dynamic_image_size,
+            )
+            image_inputs: dict[str, NestedTensors] = {
+                "pixel_values_flat":
+                torch.cat(pixel_values_lst),
+                "image_num_patches":
+                torch.tensor([len(item) for item in pixel_values_lst]),
+            }
+
+            for pixel_values in pixel_values_lst:
+                num_patches = pixel_values.shape[0]
+                feature_size = num_patches * self.num_image_token
+
+                image_repl = self.get_image_repl(feature_size, num_patches)
+
+                text = [t.replace('<image>', image_repl.full, 1) for t in text]
+
+        text_inputs = self.tokenizer(text)
+
+        return {
+            **BatchEncoding(text_inputs, tensor_type=return_tensors),
+            **image_inputs,
+        }
+
+
+class SkyworkR1VProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_processor(self, **kwargs: object) -> SkyworkR1VProcessor:
+        return self.ctx.init_processor(
+            SkyworkR1VProcessor,
+            config=self.get_hf_config(),
+            tokenizer=self.get_tokenizer(),
+            **kwargs,
+        )
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[SkyworkR1VProcessor],
+    ) -> int:
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        return processor.get_num_image_tokens(
+            image_width=image_width,
+            image_height=image_height,
+        )
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        processor = self.get_hf_processor()
+
+        base_size = processor.image_size
+        target_ratios = processor.resolve_target_ratios()
+
+        largest_feature_size, largest_feature_pinpoint = 0, None
+        for wr, hr in target_ratios:
+            width, height = base_size * wr, base_size * hr
+
+            feat_size = self.get_num_image_tokens(
+                image_width=width,
+                image_height=height,
+                processor=processor,
+            )
+            if feat_size > largest_feature_size:
+                largest_feature_size = feat_size
+                largest_feature_pinpoint = ImageSize(width=width,
+                                                     height=height)
+
+        if largest_feature_size == 0 or largest_feature_pinpoint is None:
+            raise ValueError("Cannot have a largest feature size of 0!")
+
+        return largest_feature_pinpoint
+
+
+class SkyworkR1VDummyInputsBuilder(
+        BaseDummyInputsBuilder[SkyworkR1VProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        return "<image>" * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+        num_images = mm_counts.get("image", 0)
+
+        return {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images)
+        }
+
+
+class SkyworkR1VMultiModalProcessor(
+        BaseMultiModalProcessor[SkyworkR1VProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> Mapping[str, NestedTensors]:
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+
+        hf_processor = self.info.get_hf_processor(**mm_kwargs)
+        image_token_id = hf_processor.image_token_id
+
+        # Since there may be extra tokens in the feature placeholders,
+        # we need to pass the image token ID to the model to select the
+        # tokens to merge from the vision encoder outputs
+        processed_outputs["image_token_id"] = torch.tensor(image_token_id)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: Mapping[str, NestedTensors],
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        image_num_patches = hf_inputs.get("image_num_patches", torch.empty(0))
+        num_images = len(image_num_patches)
+
+        return dict(
+            pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
+                "image", image_num_patches),
+            image_num_patches=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+            image_token_id=MultiModalFieldConfig.shared("image", num_images),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+
+        if "image_num_patches" in out_mm_kwargs:
+            image_num_patches = out_mm_kwargs["image_num_patches"]
+            assert isinstance(image_num_patches, torch.Tensor)
+            image_num_patches = image_num_patches.tolist()
+        elif "image_embeds" in out_mm_kwargs:
+            # TODO: Use image size information in dictionary embedding inputs
+            # to compute num_patches (similar to Qwen2-VL)
+            image_num_patches = [None] * len(out_mm_kwargs["image_embeds"])
+        else:
+            image_num_patches = []
+
+        def get_replacement_skyworkr1v(item_idx: int):
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems))
+
+            if isinstance(images, ImageEmbeddingItems):
+                feature_size = images.get_feature_size(item_idx)
+            else:
+                image_size = images.get_image_size(item_idx)
+                feature_size = self.info.get_num_image_tokens(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                    processor=hf_processor,
+                )
+
+            num_patches = image_num_patches[item_idx]
+            if num_patches is not None:
+                assert isinstance(num_patches, int)
+
+            return hf_processor.get_image_repl(feature_size, num_patches)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target="<image>",
+                replacement=get_replacement_skyworkr1v,
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    SkyworkR1VMultiModalProcessor,
+    info=SkyworkR1VProcessingInfo,
+    dummy_inputs=SkyworkR1VDummyInputsBuilder)
+class SkyworkR1VChatModel(nn.Module, SupportsMultiModal, SupportsPP):
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("image"):
+            return "<image>"
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+        self._patch_quant_config(config, quant_config)
+
+        image_size = config.force_image_size or config.vision_config.image_size
+        patch_size = config.vision_config.patch_size
+        self.patch_size = patch_size
+        self.num_image_token = int(
+            (image_size // patch_size)**2 * (config.downsample_ratio**2))
+        self.downsample_ratio = config.downsample_ratio
+        self.ps_version = config.ps_version
+
+        self.llm_arch_name = config.text_config.architectures[0]
+        self.is_mono = self.llm_arch_name == 'SkyworkLM2VEForCausalLM'
+        self.vision_model = self._init_vision_model(
+            config,
+            quant_config=quant_config,
+            is_mono=self.is_mono,
+            prefix=maybe_prefix(prefix, "vision_model"),
+        )
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+
+        self.mlp1 = self._init_mlp1(config)
+
+        self.img_context_token_id = None
+        self.visual_token_mask = None
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _patch_quant_config(self, config: PretrainedConfig,
+                            quant_config: QuantizationConfig):
+        # the awq models from OpenGVLab missing `modules_to_not_convert`
+        # patch the quant_config to add `modules_to_not_convert` back
+        if isinstance(quant_config, AWQConfig):
+            text_config = config.text_config
+            llm_quant_config = getattr(text_config, "quantization_config",
+                                       None)
+            if (not quant_config.modules_to_not_convert) and \
+                (llm_quant_config is not None):
+                quant_config.modules_to_not_convert.append("vision_model")
+
+    def _init_vision_model(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig],
+        *,
+        is_mono: bool,
+        prefix: str,
+    ):
+        if not is_mono:
+            vision_feature_layer = config.select_layer
+            if vision_feature_layer < 0:
+                num_hidden_layers = config.vision_config.num_hidden_layers \
+                    + vision_feature_layer + 1
+            else:
+                num_hidden_layers = vision_feature_layer + 1
+
+            return InternVisionModel(
+                config.vision_config,
+                quant_config=quant_config,
+                num_hidden_layers_override=num_hidden_layers,
+                prefix=prefix,
+            )
+        else:
+            return InternVisionPatchModel(config.vision_config)
+
+    def _init_mlp1(self, config: PretrainedConfig) -> nn.Sequential:
+        vit_hidden_size = config.vision_config.hidden_size
+        llm_hidden_size = config.text_config.hidden_size
+
+        return nn.Sequential(
+            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio)**2),
+            ReplicatedLinear(vit_hidden_size *
+                             int(1 / self.downsample_ratio)**2,
+                             llm_hidden_size,
+                             return_bias=False),
+            nn.GELU(),
+            ReplicatedLinear(llm_hidden_size,
+                             llm_hidden_size,
+                             return_bias=False),
+        )
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        x = x.view(n, int(h * scale_factor), int(w * scale_factor),
+                   int(c / (scale_factor * scale_factor)))
+        if self.ps_version == 'v1':
+            pass
+        else:
+            x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        vit_embeds = self.vision_model(pixel_values=pixel_values)
+        vit_embeds = vit_embeds[:, 1:, :]
+
+        h = w = int(vit_embeds.shape[1]**0.5)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.pixel_shuffle(vit_embeds,
+                                        scale_factor=self.downsample_ratio)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1,
+                                        vit_embeds.shape[-1])
+        vit_embeds = self.mlp1(vit_embeds)
+        return vit_embeds
+
+    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
+
+        h = w = self.config.vision_config.image_size
+        expected_dims = (3, h, w)
+
+        def _validate_shape(d: torch.Tensor):
+            actual_dims = tuple(d.shape)
+
+            if actual_dims != expected_dims:
+                expected_expr = str(expected_dims)
+                raise ValueError(
+                    "The expected shape of pixel values per image per batch "
+                    f" per patch is {expected_expr}. "
+                    f"You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
+
+        return data
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[SkyworkR1VImageInputs]:
+        pixel_values_flat = kwargs.pop("pixel_values_flat", None)
+        image_num_patches = kwargs.pop("image_num_patches", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values_flat is None and image_embeds is None:
+            return None
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+
+            return SkyworkR1VImageEmbeddingInputs(
+                type="image_embeds",
+                data=flatten_bn(image_embeds),
+            )
+
+        image_token_id = kwargs["image_token_id"]
+        assert isinstance(image_token_id, torch.Tensor)
+        self.img_context_token_id = image_token_id.flatten().unique().item()
+
+        if pixel_values_flat is not None:
+            if not isinstance(pixel_values_flat, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values_flat)}")
+
+            if not isinstance(image_num_patches, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image_num_patches. "
+                                 f"Got type: {type(image_num_patches)}")
+
+            pixel_values_flat = flatten_bn(pixel_values_flat, concat=True)
+            image_num_patches = flatten_bn(image_num_patches, concat=True)
+
+            return SkyworkR1VImagePixelInputs(
+                type="pixel_values",
+                pixel_values_flat=self._validate_pixel_values(
+                    pixel_values_flat),
+                num_patches=image_num_patches,
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _process_image_input(
+        self,
+        image_input: SkyworkR1VImageInputs,
+    ) -> Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor, ...]]:
+        if image_input["type"] == "image_embeds":
+            return image_input["data"]
+
+        assert self.vision_model is not None
+
+        image_embeds = self.extract_feature(image_input["pixel_values_flat"])
+
+        num_patches = image_input["num_patches"]
+
+        # Only one image in the current batch
+        if len(num_patches) == 1:
+            return image_embeds.view(
+                -1, self.config.text_config.hidden_size).unsqueeze(0)
+
+        # NOTE: Image embeddings are split into separate tensors for each image
+        # by the size of each embedding.
+        feature_size = image_embeds.shape[1]
+        image_embeds = image_embeds.view(-1,
+                                         self.config.text_config.hidden_size)
+        image_feature_sizes = [
+            num_patches * feature_size for num_patches in num_patches
+        ]
+        return image_embeds.split(image_feature_sizes)
+
+    def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None:
+        if self.is_mono:
+            self.visual_token_mask = (
+                input_ids == self.img_context_token_id).reshape(-1, 1)
+        else:
+            self.visual_token_mask = None
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return []
+
+        return self._process_image_input(image_input)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None \
+            and len(multimodal_embeddings) != 0:
+            assert self.img_context_token_id is not None
+            self._set_visual_token_mask(input_ids)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                self.img_context_token_id,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> IntermediateTensors:
+
+        if intermediate_tensors is not None:
+            input_ids = None
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        forward_kwargs = {
+            "input_ids": input_ids,
+            "positions": positions,
+            "intermediate_tensors": intermediate_tensors,
+            "inputs_embeds": inputs_embeds,
+        }
+
+        # Only required if the model is mono-architecture
+        if self.visual_token_mask is not None:
+            forward_kwargs.update(
+                {"visual_token_mask": self.visual_token_mask})
+            self.visual_token_mask = None
+
+        hidden_states = self.language_model.model(**forward_kwargs)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        skip_prefixes = [
+            "action_embed", "temporal_embed", "track_embed",
+            "track_embed_decoder", "box_token", "cg_criterion", "cg_model",
+            "loc_encoder", "loc_decoder", "sam", "temporal_token",
+            "track_token"
+        ]
+        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/smolvlm.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/smolvlm.py
new file mode 100644
index 0000000000000000000000000000000000000000..2adfad67152b3dfb6310cdbc84ce28c4a3db118c
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/smolvlm.py
@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+from transformers import SmolVLMProcessor
+
+from vllm.config import VllmConfig
+from vllm.multimodal import MULTIMODAL_REGISTRY
+
+# yapf: disable
+from .idefics3 import Idefics3DummyInputsBuilder as SmolVLMDummyInputsBuilder
+from .idefics3 import Idefics3ForConditionalGeneration
+from .idefics3 import Idefics3MultiModalProcessor as SmolVLMMultiModalProcessor
+from .idefics3 import Idefics3ProcessingInfo
+
+# yapf: enable
+
+
+class SmolVLMProcessingInfo(Idefics3ProcessingInfo):
+
+    def get_hf_processor(self, **kwargs: object) -> SmolVLMProcessor:
+        return self.ctx.get_hf_processor(SmolVLMProcessor, **kwargs)
+
+    def _get_image_token(
+            self, processor: Optional[SmolVLMProcessor]) -> tuple[str, str]:
+        if processor is None:
+            processor = self.get_hf_processor()
+        image_token = processor.image_token
+        fake_image_token = processor.fake_image_token
+        global_image_token = processor.global_image_token
+        return image_token, fake_image_token, global_image_token
+
+
+@MULTIMODAL_REGISTRY.register_processor(SmolVLMMultiModalProcessor,
+                                        info=SmolVLMProcessingInfo,
+                                        dummy_inputs=SmolVLMDummyInputsBuilder)
+class SmolVLMForConditionalGeneration(Idefics3ForConditionalGeneration):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(
+            vllm_config=vllm_config,
+            prefix=prefix,
+        )
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/starcoder2.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/starcoder2.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d9a2bff0e43fc6e43175b2b9f03b3f10b710e2b
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/starcoder2.py
@@ -0,0 +1,356 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2024 BigCode and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Starcoder2 model."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import Starcoder2Config
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+
+class Starcoder2Attention(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.config = config
+
+        self.hidden_size = config.hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = config.num_key_value_heads
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = self.hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = config.rope_theta
+        self.max_position_embeddings = config.max_position_embeddings
+        self.use_bias = config.use_bias
+
+        self.qkv_proj = QKVParallelLinear(
+            self.hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=self.use_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=self.use_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=int(self.rope_theta),
+            is_neox_style=True,
+        )
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Starcoder2MLP(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.c_fc = ColumnParallelLinear(
+            config.hidden_size,
+            config.intermediate_size,
+            bias=config.use_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.c_fc",
+        )
+        self.c_proj = RowParallelLinear(
+            config.intermediate_size,
+            config.hidden_size,
+            bias=config.use_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.c_proj",
+        )
+        self.act = get_act_fn(config.hidden_act)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.c_proj(hidden_states)
+        return hidden_states
+
+
+class Starcoder2DecoderLayer(nn.Module):
+
+    def __init__(self,
+                 config: Starcoder2Config,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Starcoder2Attention(config,
+                                             cache_config,
+                                             quant_config=quant_config,
+                                             prefix=f"{prefix}.self_attn")
+        self.mlp = Starcoder2MLP(config,
+                                 quant_config=quant_config,
+                                 prefix=f"{prefix}.mlp")
+        self.input_layernorm = nn.LayerNorm(config.hidden_size,
+                                            eps=config.norm_epsilon)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
+                                                     eps=config.norm_epsilon)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+@support_torch_compile
+class Starcoder2Model(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.embed_tokens")
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Starcoder2DecoderLayer(
+                config, cache_config, quant_config=quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers",
+        )
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states = layer(positions, hidden_states)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class Starcoder2ForCausalLM(nn.Module, SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.model = Starcoder2Model(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(prefix, "model"))
+        self.vocab_size = config.vocab_size
+        self.unpadded_vocab_size = config.vocab_size
+        if config.tie_word_embeddings:
+            self.lm_head = self.model.embed_tokens
+        else:
+            self.unpadded_vocab_size = config.vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                quant_config=quant_config,
+                prefix=f"{prefix}.lm_head",
+            )
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            # Models trained using ColossalAI may include these tensors in
+            # the checkpoint. Skip them.
+            skip_prefixes=(["lm_head.weight"]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/step3_text.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/step3_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..47d2af5c2a1400a9efebca82e40d34169f5ffb71
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/step3_text.py
@@ -0,0 +1,521 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inference-only Jurassic model."""
+from collections.abc import Iterable
+from typing import Any, Optional
+
+import torch
+from torch import nn
+
+from vllm.attention import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.distributed import (get_pp_group,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .utils import (PPMissingLayer, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers)
+
+logger = init_logger(__name__)
+
+
+class FusedMoEBlock(nn.Module):
+
+    def __init__(self,
+                 config: ModelConfig,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+        if self.tp_size > config.moe_num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.moe_num_experts}.")
+
+        self.experts = FusedMoE(num_experts=config.moe_num_experts,
+                                top_k=config.moe_top_k,
+                                hidden_size=config.hidden_size,
+                                intermediate_size=config.moe_intermediate_size,
+                                reduce_results=False,
+                                renormalize=config.norm_expert_weight,
+                                quant_config=quant_config,
+                                prefix=f"{prefix}.experts")
+        self.gate = ReplicatedLinear(config.hidden_size,
+                                     config.moe_num_experts,
+                                     bias=False,
+                                     quant_config=None,
+                                     prefix=f"{prefix}.gate")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        orig_shape = hidden_states.shape
+        hidden_dim = hidden_states.shape[-1]
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        router_logits, _ = self.gate(hidden_states)
+
+        final_hidden_states = self.experts(hidden_states=hidden_states,
+                                           router_logits=router_logits)
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(
+                final_hidden_states)
+
+        return final_hidden_states.view(orig_shape)
+
+
+class Step3TextMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config,
+                                           prefix=f"{prefix}.down_proj")
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+        self.hidden_size = hidden_size
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(hidden_states)
+        intermediate_act = self.act_fn(gate_up)
+        output, _ = self.down_proj(intermediate_act)
+        return output
+
+
+class Step3TextAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        norm_eps: float,
+        rope_theta: int,
+        share_q_dim: Optional[int] = None,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embedding: int = 8192,
+        head_dim: int = 256,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+
+        if num_kv_heads != 1:
+            raise ValueError(f"Step3TextAttention num_kv_heads must be 1, "
+                             f"but got {num_kv_heads}.")
+        self.num_kv_heads = num_kv_heads
+
+        self.head_dim = head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.q_size = share_q_dim if share_q_dim else self.head_dim
+
+        self.qkv_proj = ReplicatedLinear(
+            hidden_size,
+            self.q_size + self.kv_size * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        self.inter_norm = RMSNorm(self.q_size, eps=norm_eps)
+        self.wq = ColumnParallelLinear(
+            self.q_size,
+            self.head_dim * self.total_num_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.wq",
+        )
+        self.rotary_emb = get_rope(self.head_dim,
+                                   rotary_dim=self.head_dim,
+                                   max_position=max_position_embedding,
+                                   base=rope_theta,
+                                   rope_scaling=rope_scaling)
+        scaling = self.head_dim**-0.5
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              scaling,
+                              self.num_kv_heads,
+                              cache_config=cache_config,
+                              prefix=f"{prefix}.attn")
+
+    def forward(self, positions: torch.Tensor,
+                hidden_states: torch.Tensor) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q = self.inter_norm(q)
+        q = self.wq(q)[0]
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        residual, _ = self.o_proj(attn_output)
+        return residual
+
+
+class Step3TextDecoderLayer(nn.Module):
+
+    def __init__(self,
+                 config: ModelConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = "") -> None:
+        super().__init__()
+        config = config.hf_config
+        self.hidden_size = config.hidden_size
+        rope_scaling = getattr(config, "rope_scaling", None)
+
+        self.self_attn = Step3TextAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=1,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            norm_eps=config.rms_norm_eps,
+            max_position_embedding=config.max_position_embedding,
+            head_dim=config.head_dim,
+            share_q_dim=config.share_q_dim,
+            rope_theta=config.rope_theta,
+            rope_scaling=rope_scaling,
+            prefix=f"{prefix}.self_attn")
+
+        layer_idx = int(prefix.split("layers.")[1].split(".")[0])
+        moe_layers_enum = getattr(config, "moe_layers_enum", None)
+        if moe_layers_enum is not None:
+            moe_layers_idx = [
+                int(i) for i in moe_layers_enum.strip().split(',')
+            ]
+        else:
+            # Default to 1dense.
+            moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
+
+        if layer_idx in moe_layers_idx:
+            self.moe = FusedMoEBlock(config=config,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.moe")
+            self.share_expert = Step3TextMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=config.share_expert_dim,
+                hidden_act="silu",
+                quant_config=quant_config,
+                prefix=f"{prefix}.share_expert")
+            self.use_moe = True
+        else:
+            self.mlp = Step3TextMLP(hidden_size=config.hidden_size,
+                                    intermediate_size=config.intermediate_size,
+                                    hidden_act="silu",
+                                    quant_config=quant_config,
+                                    prefix=f"{prefix}.mlp")
+            self.use_moe = False
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+            self, positions: torch.Tensor, hidden_states: torch.Tensor,
+            residual: Optional[torch.Tensor]
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+
+        if self.use_moe:
+            share_output = self.share_expert(hidden_states)
+            moe_output = self.moe(hidden_states)
+            hidden_states = share_output + moe_output
+        else:
+            hidden_states = self.mlp(hidden_states)
+
+        return hidden_states, residual
+
+
+@support_torch_compile
+class Step3TextModel(nn.Module):
+
+    def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        self.vocab_size = config.vocab_size
+        self.config = config
+
+        if get_pp_group().is_first_rank or (config.tie_word_embeddings
+                                            and get_pp_group().is_last_rank):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Step3TextDecoderLayer(config=vllm_config.
+                                                 model_config,
+                                                 cache_config=cache_config,
+                                                 quant_config=quant_config,
+                                                 prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(["hidden_states"],
+                                                    config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states, residual = layer(positions, hidden_states, residual)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual,
+            })
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class Step3TextForCausalLM(nn.Module, SupportsPP):
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.vllm_config = vllm_config
+
+        self.model = Step3TextModel(vllm_config=vllm_config, prefix=prefix)
+
+        if get_pp_group().is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            if lora_config:
+                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE
+                if not lora_config else lora_config.lora_vocab_padding_size,
+            )
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                    config.vocab_size)
+            self.sampler = get_sampler()
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None):
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: Optional[torch.Tensor],
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        qkv_params_mapping = [
+            # (param_name, shard_name, relative_start_idx, relative_end_idx)
+            (".qkv_proj", ".q_proj", 0, self.config.share_q_dim /
+             (self.config.share_q_dim + self.config.head_dim * 2)),
+            (".qkv_proj", ".k_proj", self.config.share_q_dim /
+             (self.config.share_q_dim + self.config.head_dim * 2),
+             (self.config.share_q_dim + self.config.head_dim) /
+             (self.config.share_q_dim + self.config.head_dim * 2)),
+            (".qkv_proj", ".v_proj",
+             (self.config.share_q_dim + self.config.head_dim) /
+             (self.config.share_q_dim + self.config.head_dim * 2),
+             (self.config.share_q_dim + self.config.head_dim * 2) /
+             (self.config.share_q_dim + self.config.head_dim * 2)),
+        ]
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        expert_params_mapping = [
+            (".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
+            (".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
+            (".moe.experts.w2_weight", ".moe.down_proj.weight", "w2")
+        ]
+
+        disable_moe_stacked_params = [
+            data[1] for data in expert_params_mapping
+        ]
+
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if any(disable_moe_stacked_param in name
+                       for disable_moe_stacked_param in
+                       disable_moe_stacked_params):
+                    continue
+                name = name.replace(weight_name, param_name)
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    # Skip layers on other devices.
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    # Skip loading extra bias for GPTQ models.
+                    if ((name.endswith(".bias") or name.endswith("_bias"))
+                            and name not in params_dict):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    for expert_id in range(loaded_weight.shape[0]):
+                        loaded_weight_expert = loaded_weight[expert_id]
+                        weight_loader(param,
+                                      loaded_weight_expert,
+                                      name,
+                                      shard_id=shard_id,
+                                      expert_id=expert_id)
+                    loaded_params.add(name)
+                    break
+                else:
+                    for (param_name, weight_name, start_idx,
+                         end_idx) in qkv_params_mapping:
+                        if weight_name not in name:
+                            continue
+                        name = name.replace(weight_name, param_name)
+                        if is_pp_missing_parameter(name, self):
+                            continue
+                        param = params_dict[name]
+                        dim = param.shape[param.output_dim]
+                        begin_idx = int(start_idx * dim)
+                        end_idx = int(end_idx * dim)
+                        param_slice = param.narrow(param.output_dim, begin_idx,
+                                                   end_idx - begin_idx)
+                        param_slice.copy_(loaded_weight)
+                        loaded_params.add(name)
+                        break
+                    else:
+                        if is_pp_missing_parameter(name, self):
+                            continue
+                        param = params_dict[name]
+                        weight_loader = getattr(param, "weight_loader",
+                                                default_weight_loader)
+                        weight_loader(param, loaded_weight)
+                        loaded_params.add(name)
+        return loaded_params
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/whisper.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/whisper.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca02ecd828ba3f8e731694b0bf55ea8cceda9f47
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/models/whisper.py
@@ -0,0 +1,929 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from contextlib import nullcontext
+from typing import Optional, TypedDict, Union, cast
+
+import numpy as np
+import torch
+from torch import nn
+from transformers import (BatchFeature, WhisperConfig, WhisperFeatureExtractor,
+                          WhisperProcessor)
+from transformers.models.whisper.modeling_whisper import sinusoids
+
+from vllm.attention import Attention, AttentionType
+from vllm.attention.layer import MultiHeadAttention
+from vllm.config import (CacheConfig, ModelConfig, SpeechToTextConfig,
+                         VllmConfig)
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.inputs.data import PromptType
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY, NestedTensors
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import MultiModalDataItems, MultiModalDataParser
+from vllm.multimodal.processing import (BaseProcessingInfo,
+                                        EncDecMultiModalProcessor,
+                                        PromptReplacement, PromptUpdate)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.transformers_utils.processor import cached_get_processor
+
+from .interfaces import (MultiModalEmbeddings, SupportsMultiModal,
+                         SupportsTranscription, SupportsV0Only)
+from .utils import (AutoWeightsLoader, WeightsMapper, cast_overflow_tensors,
+                    make_layers)
+
+logger = init_logger(__name__)
+
+# From https://platform.openai.com/docs/guides/speech-to-text/supported-languages
+
+ISO639_1_SUPPORTED_LANGS = {
+    "af": "Afrikaans",
+    "ar": "Arabic",
+    "hy": "Armenian",
+    "az": "Azerbaijani",
+    "be": "Belarusian",
+    "bs": "Bosnian",
+    "bg": "Bulgarian",
+    "ca": "Catalan",
+    "zh": "Chinese",
+    "hr": "Croatian",
+    "cs": "Czech",
+    "da": "Danish",
+    "nl": "Dutch",
+    "en": "English",
+    "et": "Estonian",
+    "fi": "Finnish",
+    "fr": "French",
+    "gl": "Galician",
+    "de": "German",
+    "el": "Greek",
+    "he": "Hebrew",
+    "hi": "Hindi",
+    "hu": "Hungarian",
+    "is": "Icelandic",
+    "id": "Indonesian",
+    "it": "Italian",
+    "ja": "Japanese",
+    "kn": "Kannada",
+    "kk": "Kazakh",
+    "ko": "Korean",
+    "lv": "Latvian",
+    "lt": "Lithuanian",
+    "mk": "Macedonian",
+    "ms": "Malay",
+    "mr": "Marathi",
+    "mi": "Maori",
+    "ne": "Nepali",
+    "no": "Norwegian",
+    "fa": "Persian",
+    "pl": "Polish",
+    "pt": "Portuguese",
+    "ro": "Romanian",
+    "ru": "Russian",
+    "sr": "Serbian",
+    "sk": "Slovak",
+    "sl": "Slovenian",
+    "es": "Spanish",
+    "sw": "Swahili",
+    "sv": "Swedish",
+    "tl": "Tagalog",
+    "ta": "Tamil",
+    "th": "Thai",
+    "tr": "Turkish",
+    "uk": "Ukrainian",
+    "ur": "Urdu",
+    "vi": "Vietnamese",
+    "cy": "Welsh"
+}
+
+
+class WhisperAudioInputs(TypedDict):
+    input_features: NestedTensors
+    """Shape: `(batch_size, 128, M)`"""
+
+
+class WhisperPositionalEmbedding(nn.Embedding):
+
+    def __init__(self, num_positions: int, embedding_dim: int):
+        super().__init__(num_positions, embedding_dim)
+
+    def forward(self, position_ids):
+        return self.weight[position_ids]
+
+
+class WhisperAttention(nn.Module):
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        bias: bool = True,
+        attn_type: AttentionType = AttentionType.DECODER,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        standalone_encoder: bool = False,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        if self.total_num_heads >= tp_size:
+            # Number of heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_heads % tp_size == 0
+        else:
+            # Number of heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_heads == 0
+        self.num_kv_heads = max(1, self.total_num_heads // tp_size)
+        self.head_dim = self.embed_dim // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.attn_type = attn_type
+
+        if (self.head_dim * num_heads) != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: "
+                f"{self.embed_dim} and `num_heads`: {num_heads}).")
+        self.scaling = self.head_dim**-0.5
+
+        self._init_qkv(embed_dim, bias, quant_config, prefix=prefix)
+        self.out_proj = RowParallelLinear(
+            input_size=embed_dim,
+            output_size=embed_dim,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+        if standalone_encoder:
+            self.attn = MultiHeadAttention(
+                self.num_heads,
+                self.head_dim,
+                self.scaling,
+                num_kv_heads=self.num_kv_heads,
+            )
+        else:
+            self.attn = Attention(
+                self.num_heads,
+                self.head_dim,
+                self.scaling,
+                num_kv_heads=self.num_kv_heads,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.attn",
+                attn_type=self.attn_type,
+            )
+
+    def _init_qkv(
+        self,
+        embed_dim: int,
+        bias: bool = True,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=embed_dim,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ):
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        attn_output = self.attn(q, k, v)
+
+        output, _ = self.out_proj(attn_output)
+
+        return output
+
+
+class WhisperCrossAttention(WhisperAttention):
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        bias: bool = True,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__(
+            embed_dim=embed_dim,
+            num_heads=num_heads,
+            bias=bias,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=prefix,
+            attn_type=AttentionType.ENCODER_DECODER,
+        )
+
+    def _init_qkv(
+        self,
+        embed_dim: int,
+        bias: bool = True,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        self.q_proj = ColumnParallelLinear(
+            input_size=embed_dim,
+            output_size=embed_dim,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.q_proj",
+        )
+        self.kv_proj = QKVParallelLinear(
+            hidden_size=embed_dim,
+            head_size=self.head_dim,
+            total_num_heads=0,
+            total_num_kv_heads=self.total_num_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.kv_proj",
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor],
+    ):
+        q, _ = self.q_proj(hidden_states)
+
+        # Encoder hidden states are only computed once during prefill phase.
+        # Afterwards, the keys and values should be available in the kv-cache.
+        if encoder_hidden_states is not None:
+            kv, _ = self.kv_proj(encoder_hidden_states)
+            k, v = kv.split([self.kv_size, self.kv_size], dim=-1)
+        else:
+            k = v = None
+
+        attn_output = self.attn(q, k, v)
+
+        output, _ = self.out_proj(attn_output)
+
+        return output
+
+
+class WhisperMLP(nn.Module):
+
+    def __init__(
+        self,
+        embed_dim: int,
+        ffn_dim: int,
+        act_fn: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.activation_fn = get_act_fn(act_fn)
+        self.fc1 = ColumnParallelLinear(
+            input_size=embed_dim,
+            output_size=ffn_dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+        )
+        self.fc2 = RowParallelLinear(
+            input_size=ffn_dim,
+            output_size=embed_dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+        )
+
+    def forward(self, hidden_states: torch.Tensor):
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        return hidden_states
+
+
+class WhisperEncoderLayer(nn.Module):
+
+    def __init__(self,
+                 *,
+                 vllm_config: VllmConfig,
+                 prefix: str = "",
+                 is_standalone_encoder: bool = False):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.embed_dim = config.d_model
+        self.self_attn = WhisperAttention(
+            embed_dim=self.embed_dim,
+            num_heads=config.encoder_attention_heads,
+            attn_type=AttentionType.ENCODER,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+            standalone_encoder=is_standalone_encoder,
+        )
+        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.mlp = WhisperMLP(
+            embed_dim=config.d_model,
+            ffn_dim=config.encoder_ffn_dim,
+            act_fn=config.activation_function,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ):
+        residual = hidden_states
+        hidden_states = self.self_attn_layer_norm(hidden_states)
+        hidden_states = self.self_attn(hidden_states=hidden_states)
+        hidden_states = residual + hidden_states
+        residual = hidden_states
+        hidden_states = self.final_layer_norm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        hidden_states = cast_overflow_tensors(hidden_states)
+
+        return hidden_states
+
+
+class WhisperDecoderLayer(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.self_attn = WhisperAttention(
+            embed_dim=config.d_model,
+            num_heads=config.decoder_attention_heads,
+            attn_type=AttentionType.DECODER,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.self_attn_layer_norm = nn.LayerNorm(config.d_model)
+        self.encoder_attn = WhisperCrossAttention(
+            embed_dim=config.d_model,
+            num_heads=config.decoder_attention_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.encoder_attn",
+        )
+        self.encoder_attn_layer_norm = nn.LayerNorm(config.d_model)
+        self.mlp = WhisperMLP(
+            embed_dim=config.d_model,
+            ffn_dim=config.decoder_ffn_dim,
+            act_fn=config.activation_function,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.final_layer_norm = nn.LayerNorm(config.d_model)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor],
+    ):
+        residual = hidden_states
+        hidden_states = self.self_attn_layer_norm(hidden_states)
+        hidden_states = self.self_attn(hidden_states=hidden_states)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.encoder_attn_layer_norm(hidden_states)
+        hidden_states = self.encoder_attn(
+            hidden_states=hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.final_layer_norm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class WhisperEncoder(nn.Module):
+
+    def __init__(self,
+                 *,
+                 vllm_config: VllmConfig,
+                 prefix: str = "",
+                 is_standalone_encoder: bool = False,
+                 init_in_fp32: bool = False):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        embed_dim = config.d_model
+        self.is_standalone_encoder = is_standalone_encoder
+        self.num_mel_bins = config.num_mel_bins
+        self.max_source_positions = config.max_source_positions
+        self.embed_scale = (math.sqrt(embed_dim)
+                            if config.scale_embedding else 1.0)
+
+        self.conv1 = nn.Conv1d(self.num_mel_bins,
+                               embed_dim,
+                               kernel_size=3,
+                               padding=1)
+        self.conv2 = nn.Conv1d(embed_dim,
+                               embed_dim,
+                               kernel_size=3,
+                               stride=2,
+                               padding=1)
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.encoder_layers,
+            lambda prefix: WhisperEncoderLayer(vllm_config=vllm_config,
+                                               prefix=f"{prefix}.layers",
+                                               is_standalone_encoder=
+                                               is_standalone_encoder),
+            prefix=f"{prefix}.layers",
+        )
+        self.layer_norm = nn.LayerNorm(config.d_model)
+
+        maybe_fp32_init_ctx = set_default_torch_dtype(
+            torch.float32) if init_in_fp32 else nullcontext()
+
+        with (
+                torch.no_grad(),
+                maybe_fp32_init_ctx,
+        ):
+            self.embed_positions = nn.Embedding(self.max_source_positions,
+                                                embed_dim)
+            self.embed_positions.weight.copy_(
+                sinusoids(*self.embed_positions.weight.shape))
+
+    def forward(self, input_features: Union[torch.Tensor, list[torch.Tensor]]):
+        hidden_states = []
+        for features in input_features:
+            embeds = nn.functional.gelu(self.conv1(features))
+            embeds = nn.functional.gelu(self.conv2(embeds))
+            embeds = embeds.transpose(-1, -2)
+            embeds = (embeds +
+                      self.embed_positions.weight[:embeds.size(-2), :]).to(
+                          embeds.dtype)
+            hidden_states.append(embeds)
+        hidden_states = torch.cat(hidden_states)
+
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(hidden_states)
+
+        hidden_states = self.layer_norm(hidden_states)
+        return hidden_states
+
+
+class WhisperDecoder(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.layerdrop = config.decoder_layerdrop
+        self.padding_idx = config.pad_token_id
+        self.max_target_positions = config.max_target_positions
+        self.max_source_positions = config.max_source_positions
+        self.embed_scale = (math.sqrt(config.d_model)
+                            if config.scale_embedding else 1.0)
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model,
+                                         self.padding_idx)
+        self.embed_positions = WhisperPositionalEmbedding(
+            self.max_target_positions, config.d_model)
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.decoder_layers,
+            lambda prefix: WhisperDecoderLayer(vllm_config=vllm_config,
+                                               prefix=f"{prefix}.layers"),
+            prefix=f"{prefix}.layers",
+        )
+        self.layer_norm = nn.LayerNorm(config.d_model)
+
+    def forward(
+        self,
+        input_ids,
+        positions: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor],
+    ):
+        inputs_embeds = self.get_input_embeddings(input_ids)
+        positions = self.embed_positions(positions)
+        hidden_states = inputs_embeds + positions
+
+        for decoder_layer in self.layers:
+            hidden_states = decoder_layer(
+                hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+            )
+
+        hidden_states = self.layer_norm(hidden_states)
+        return hidden_states
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+
+class WhisperModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        self.encoder = WhisperEncoder(vllm_config=vllm_config,
+                                      prefix=f"{prefix}.encoder")
+        self.decoder = WhisperDecoder(vllm_config=vllm_config,
+                                      prefix=f"{prefix}.decoder")
+
+    def forward(
+        self,
+        input_features: Optional[Union[torch.Tensor, list[torch.Tensor]]],
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+    ) -> torch.Tensor:
+        encoder_outputs = self.get_encoder_outputs(input_features)
+        decoder_outputs = self.decoder(
+            input_ids=input_ids,
+            positions=positions,
+            encoder_hidden_states=encoder_outputs,
+        )
+        return decoder_outputs
+
+    def get_encoder_outputs(
+        self,
+        input_features: Optional[Union[torch.Tensor, list[torch.Tensor]]],
+    ) -> Optional[torch.Tensor]:
+        if input_features is None:
+            return None
+        return self.encoder(input_features)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".self_attn.qkv_proj", ".self_attn.q_proj", "q"),
+            (".self_attn.qkv_proj", ".self_attn.k_proj", "k"),
+            (".self_attn.qkv_proj", ".self_attn.v_proj", "v"),
+            (".encoder_attn.kv_proj", ".encoder_attn.k_proj", "k"),
+            (".encoder_attn.kv_proj", ".encoder_attn.v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class WhisperProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self) -> WhisperConfig:
+        return self.ctx.get_hf_config(WhisperConfig)
+
+    def get_hf_processor(self, **kwargs: object) -> WhisperProcessor:
+        # HACK: Transformers 4.53.2 has issue with whisper tokenizer to
+        # initialize processor. We use a monkeypatch to fix it here.
+        # See: https://github.com/vllm-project/vllm/issues/20224
+        processor_class = WhisperProcessor
+        tokenizer_class = ("WhisperTokenizer", "WhisperTokenizerFast")
+        if processor_class.tokenizer_class != tokenizer_class:
+            processor_class.tokenizer_class = tokenizer_class
+        return self.ctx.get_hf_processor(processor_class, **kwargs)
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"audio": 1}
+
+    def get_feature_extractor(self,
+                              **kwargs: object) -> WhisperFeatureExtractor:
+        hf_processor = self.get_hf_processor(**kwargs)
+        feature_extractor = hf_processor.feature_extractor  # type: ignore
+        assert isinstance(feature_extractor, WhisperFeatureExtractor)
+        return feature_extractor
+
+    def get_num_audio_tokens(self) -> int:
+        return self.get_hf_config().max_source_positions
+
+
+class WhisperDummyInputsBuilder(BaseDummyInputsBuilder[WhisperProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_audios = mm_counts.get("audio", 0)
+
+        return "<|startoftranscript|>" * num_audios
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        feature_extractor = self.info.get_feature_extractor()
+
+        sampling_rate = feature_extractor.sampling_rate
+        audio_len = feature_extractor.chunk_length * sampling_rate
+        num_audios = mm_counts.get("audio", 0)
+
+        return {
+            "audio":
+            self._get_dummy_audios(length=audio_len, num_audios=num_audios)
+        }
+
+
+class WhisperMultiModalProcessor(
+        EncDecMultiModalProcessor[WhisperProcessingInfo]):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        feature_extractor = self.info.get_feature_extractor()
+        return MultiModalDataParser(target_sr=feature_extractor.sampling_rate)
+
+    @property
+    def pad_dummy_encoder_prompt(self) -> bool:
+        return True
+
+    def create_encoder_prompt(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data: MultiModalDataDict,
+    ) -> Union[str, list[int]]:
+        # Strictly speaking, whisper encoder only accept audio features.
+        # We create a dummy encoder prompt here which will be padded to
+        # num_audio_tokens. So that we can create dummy data from this
+        # for encoder profiling.
+        return [0]
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        if mm_data:
+            feature_extractor = self.info.get_feature_extractor(**mm_kwargs)
+            mm_data = dict(audio=mm_data.pop("audios"))
+            mm_kwargs = dict(
+                **mm_kwargs,
+                sampling_rate=feature_extractor.sampling_rate,
+            )
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+            tok_kwargs=tok_kwargs,
+        )
+        if "labels" in processed_outputs:
+            processed_outputs["input_ids"] = processed_outputs.pop("labels")
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(input_features=MultiModalFieldConfig.batched("audio"))
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        num_tokens = self.info.get_num_audio_tokens()
+        return [
+            PromptReplacement(
+                modality="audio",
+                target=[0],
+                replacement=[0] * num_tokens,
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(WhisperMultiModalProcessor,
+                                        info=WhisperProcessingInfo,
+                                        dummy_inputs=WhisperDummyInputsBuilder)
+class WhisperForConditionalGeneration(nn.Module, SupportsTranscription,
+                                      SupportsMultiModal, SupportsV0Only):
+    packed_modules_mapping = {
+        "self_attn.qkv_proj": [
+            "self_attn.q_proj",
+            "self_attn.k_proj",
+            "self_attn.v_proj",
+        ],
+        "encoder_attn.kv_proj": ["encoder_attn.k_proj", "encoder_attn.v_proj"],
+    }
+
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_substr={
+        ".fc1.": ".mlp.fc1.",
+        ".fc2.": ".mlp.fc2."
+    })
+
+    # Whisper only supports audio-conditioned generation.
+    supports_transcription_only = True
+    supported_languages = ISO639_1_SUPPORTED_LANGS
+
+    @classmethod
+    def validate_language(cls, language: Optional[str]) -> Optional[str]:
+        if language is None:
+            # TODO language should be optional and can be guessed.
+            # For now we default to en. See
+            # https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/generation_whisper.py#L1520
+            logger.warning(
+                "Defaulting to language='en'. If you wish to transcribe "
+                "audio in a different language, pass the `language` field "
+                "in the TranscriptionRequest.")
+            language = "en"
+        return super().validate_language(language)
+
+    @classmethod
+    def get_generation_prompt(
+            cls,
+            audio: np.ndarray,
+            model_config: ModelConfig,  # not needed here
+            stt_config: SpeechToTextConfig,
+            language: Optional[str],
+            task_type: str,
+            request_prompt: str) -> PromptType:
+        if language is None:
+            raise ValueError(
+                "Language must be specified when creating the Whisper prompt")
+        prompt = {
+            "encoder_prompt": {
+                # Whisper does not support encoder prompt.
+                "prompt": "",
+                "multi_modal_data": {
+                    "audio": (audio, stt_config.sample_rate),
+                },
+            },
+            "decoder_prompt":
+            ((f"<|prev|>{request_prompt}" if request_prompt else "") +
+             f"<|startoftranscript|><|{language}|>" +
+             f"<|{task_type}|><|notimestamps|>")
+        }
+        return cast(PromptType, prompt)
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
+        if modality.startswith("audio"):
+            return None
+
+        raise ValueError("Only audio modality is supported")
+
+    @classmethod
+    def get_speech_to_text_config(cls, model_config: ModelConfig,
+                                  task_type: str) -> SpeechToTextConfig:
+        processor = cached_get_processor(model_config.model)
+
+        return SpeechToTextConfig(
+            max_audio_clip_s=processor.feature_extractor.chunk_length,
+            sample_rate=processor.feature_extractor.sampling_rate,
+        )
+
+    @classmethod
+    def get_num_audio_tokens(cls, audio_duration_s: float,
+                             stt_config: SpeechToTextConfig,
+                             model_config: ModelConfig) -> Optional[int]:
+        processor = cached_get_processor(model_config.model)
+        hop_length = processor.feature_extractor.hop_length
+        assert hop_length is not None
+        # NOTE(NickLucche) user can't pass encoder
+        # prompts directly at least not to Whisper.
+        # One indicator of the encoder amount of processing
+        # is the log-mel spectogram length.
+        return math.ceil(audio_duration_s * stt_config.sample_rate /
+                         hop_length)
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.dtype = vllm_config.model_config.dtype
+
+        self.model = WhisperModel(vllm_config=vllm_config, prefix=prefix)
+        self.unpadded_vocab_size = config.vocab_size
+        self.proj_out = ParallelLMHead(config.vocab_size,
+                                       config.d_model,
+                                       quant_config=quant_config)
+        self.proj_out = self.proj_out.tie_weights(
+            self.model.decoder.embed_tokens)
+        logit_scale = getattr(config, "logit_scale", 1.0)
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size, logit_scale)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        audio_input = self._parse_and_validate_audio_input(**kwargs)
+        decoder_outputs = self.model(
+            input_features=audio_input["input_features"],
+            input_ids=input_ids,
+            positions=positions,
+        )
+        return decoder_outputs
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.model.decoder
+
+    def get_multimodal_embeddings(self,
+                                  **kwargs: object) -> MultiModalEmbeddings:
+        # TODO: This method does not obey the interface for SupportsMultiModal.
+        # Refactor this once encoder/decoder support is implemented in V1.
+        audio_input = self._parse_and_validate_audio_input(**kwargs)
+        return self.model.get_encoder_outputs(audio_input["input_features"])
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+        # TODO: This method just returns the decoder sequence embeddings since
+        # Whisper does not have encoder text tokens. Refactor this once
+        # encoder/decoder support is implemented in V1.
+        return self.model.decoder.get_input_embeddings(input_ids)
+
+    def _parse_and_validate_audio_input(
+            self, **kwargs: object) -> WhisperAudioInputs:
+        input_features = kwargs.pop("input_features", None)
+
+        if input_features is not None:
+            if not isinstance(input_features, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of audio features. "
+                                 f"Got type: {type(input_features)}")
+            input_features = torch.cat(
+                [feat.to(self.dtype) for feat in input_features])
+
+        return WhisperAudioInputs(input_features=input_features)
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.proj_out, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self, skip_prefixes=["proj_out."])
+
+        # add fake zeros bias for k_proj to state_dict
+        weights = _create_fake_bias_for_k_proj(weights)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+
+def _create_fake_bias_for_k_proj(
+    weights: Iterable[tuple[str, torch.Tensor]]
+) -> Iterable[tuple[str, torch.Tensor]]:
+    """
+    Create full zeros bias for k_proj weight in self-attn and x-attn layers.
+    So that the bias for k_proj in qkv_proj can be initialized with zeros.
+    """
+    for name, weight in weights:
+        if name.endswith(".k_proj.weight"):
+            bias = torch.zeros(weight.size(0))
+            bias_name = name.replace("weight", "bias")
+            yield from [(name, weight), (bias_name, bias)]
+        yield name, weight
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/warmup/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/warmup/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/warmup/deep_gemm_warmup.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/warmup/deep_gemm_warmup.py
new file mode 100644
index 0000000000000000000000000000000000000000..74599fa44c88c0b06569641db9a6508e3efaa115
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/warmup/deep_gemm_warmup.py
@@ -0,0 +1,219 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Warmup deep_gemm kernels.
+DeepGEMM JIT's the kernels. The warmup aims to JIT all the kernels that would
+be used during model execution beforehand.
+"""
+
+import torch
+from tqdm import tqdm
+
+import vllm.envs as envs
+from vllm.model_executor.layers.fused_moe.deep_gemm_moe import DeepGemmExperts
+from vllm.model_executor.layers.fused_moe.deep_gemm_utils import (
+    compute_aligned_M, deep_gemm_block_shape)
+from vllm.model_executor.layers.fused_moe.layer import FusedMoE
+from vllm.model_executor.layers.fused_moe.modular_kernel import (
+    FusedMoEModularKernel)
+from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
+    TritonOrDeepGemmExperts)
+from vllm.model_executor.layers.linear import LinearBase
+from vllm.model_executor.layers.quantization.fp8 import Fp8LinearMethod
+from vllm.utils.deep_gemm import fp8_gemm_nt, m_grouped_fp8_gemm_nt_contiguous
+
+
+def _extract_data_from_linear_base_module(
+        m: torch.nn.Module) -> tuple[torch.Tensor, torch.Tensor, list[int]]:
+    """
+    Extract weights, weight scales and quantization block sizes from the given
+    LinearBase module.
+    """
+    assert isinstance(m, LinearBase)
+    assert isinstance(m.quant_method, Fp8LinearMethod)
+    assert m.quant_method.block_quant
+    assert m.quant_method.quant_config is not None
+
+    w = m.weight
+    ws = m.weight_scale_inv
+    quant_block_size = m.quant_method.quant_config.weight_block_size
+
+    assert isinstance(w, torch.Tensor)
+    assert isinstance(ws, torch.Tensor)
+    assert quant_block_size is not None
+    return (w, ws, quant_block_size)
+
+
+def _extract_data_from_fused_moe_module(
+    m: torch.nn.Module
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int]:
+    """
+    Extract weights, weight scales and num_topk from FusedMoE module.
+    """
+    assert isinstance(m, FusedMoE)
+    w13 = m.w13_weight
+    w13_s = m.w13_weight_scale_inv
+    w2 = m.w2_weight
+    w2_s = m.w2_weight_scale_inv
+    num_topk = m.top_k
+
+    assert isinstance(w13, torch.Tensor)
+    assert isinstance(w13_s, torch.Tensor)
+    assert isinstance(w2, torch.Tensor)
+    assert isinstance(w2_s, torch.Tensor)
+    return w13, w13_s, w2, w2_s, num_topk
+
+
+def _fp8_linear_may_use_deep_gemm(module: torch.nn.Module) -> bool:
+    """
+    Return True if the input module/layer could be processed with DeepGEMM.
+    """
+    block_size = deep_gemm_block_shape()[0]
+    if not (isinstance(module, LinearBase)
+            and isinstance(module.quant_method, Fp8LinearMethod)
+            and module.quant_method.block_quant):
+        return False
+
+    w, _, block_sizes = _extract_data_from_linear_base_module(module)
+    return (block_sizes == deep_gemm_block_shape() and w.ndim == 2
+            and w.shape[0] % block_size == 0 and w.shape[1] % block_size == 0)
+
+
+def _fused_moe_grouped_gemm_may_use_deep_gemm(module: torch.nn.Module) -> bool:
+    if not (isinstance(module, FusedMoE)
+            and module.moe_config.quant_dtype == torch.float8_e4m3fn
+            and module.moe_config.block_shape == deep_gemm_block_shape()):
+        return False
+
+    if not isinstance(module.quant_method.fused_experts,
+                      FusedMoEModularKernel):
+        # fused_experts could invoke deep_gemm_moe_fp8
+        return True
+
+    mk: FusedMoEModularKernel = module.quant_method.fused_experts
+    # Further check if the ModularKernel implementation uses the DeepGemmExperts
+    return isinstance(mk.fused_experts,
+                      (DeepGemmExperts, TritonOrDeepGemmExperts))
+
+
+FP8_GEMM_NT_WARMUP_CACHE: set[torch.Size] = set()
+
+
+def _deepgemm_fp8_gemm_nt_warmup(w: torch.Tensor, ws: torch.Tensor,
+                                 max_tokens: int):
+    if w.size() in FP8_GEMM_NT_WARMUP_CACHE:
+        return
+
+    n, k = w.size()
+    block_m = deep_gemm_block_shape()[0]
+
+    device = w.device
+    a1q = torch.empty((max_tokens, k),
+                      device=device,
+                      dtype=torch.float8_e4m3fn)
+    a1q_scales = torch.empty((max_tokens, k // block_m),
+                             device=device,
+                             dtype=torch.float32)
+    out = torch.empty((max_tokens, n), device=device, dtype=torch.bfloat16)
+
+    pbar = tqdm(total=max_tokens,
+                desc=f"DeepGemm(fp8_gemm_nt) warmup (W={w.size()})")
+    num_tokens = max_tokens
+    while num_tokens > 0:
+        fp8_gemm_nt((a1q[:num_tokens], a1q_scales[:num_tokens]), (w, ws),
+                    out[:num_tokens])
+        pbar.update(1)
+        num_tokens -= 1
+
+    FP8_GEMM_NT_WARMUP_CACHE.add(w.size())
+
+
+GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE: set[torch.Size] = set()
+
+
+def _deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(w1: torch.Tensor,
+                                                    w2: torch.Tensor,
+                                                    w1_scale: torch.Tensor,
+                                                    w2_scale: torch.Tensor,
+                                                    num_topk: int):
+    if (w1.size() in GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE
+            and w2.size() in GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE):
+        return
+
+    assert w1.size(0) == w2.size(0), (
+        "w1 and w2 must have the same number of experts")
+
+    block_m = deep_gemm_block_shape()[0]
+    num_experts = w1.size(0)
+    device = w1.device
+
+    # This is the maximum GroupedGemm M size that we expect to run
+    # the grouped_gemm with.
+    MAX_M = compute_aligned_M(envs.VLLM_FUSED_MOE_CHUNK_SIZE,
+                              num_topk,
+                              num_experts,
+                              block_m,
+                              expert_tokens_meta=None)
+    # Distribute expert-ids evenly.
+    MAX_BLOCKS = MAX_M // block_m
+    expert_ids_block = torch.randint(low=0,
+                                     high=num_experts,
+                                     size=(MAX_BLOCKS, ),
+                                     device=device,
+                                     dtype=torch.int32)
+    expert_ids = torch.repeat_interleave(expert_ids_block, block_m, dim=0)
+
+    def _warmup(w: torch.Tensor, w_scale: torch.Tensor):
+
+        _, n, k = w.size()
+        a1q = torch.empty((MAX_M, k), device=device, dtype=torch.float8_e4m3fn)
+        a1q_scales = torch.empty((MAX_M, k // block_m),
+                                 device=device,
+                                 dtype=torch.float32)
+        out = torch.empty((MAX_M, n), device=device, dtype=torch.bfloat16)
+
+        pbar = tqdm(
+            total=MAX_BLOCKS,
+            desc=
+            f"DeepGemm(m_grouped_fp8_gemm_nt_contiguous) warmup (W={w.size()})"
+        )
+        num_tokens = MAX_M
+        while num_tokens > 0:
+            m_grouped_fp8_gemm_nt_contiguous(
+                (a1q[:num_tokens], a1q_scales[:num_tokens]), (w, w_scale),
+                out[:num_tokens], expert_ids[:num_tokens])
+            pbar.update(1)
+            num_tokens = num_tokens - block_m
+
+    for w, ws in [(w1, w1_scale), (w2, w2_scale)]:
+        if w.size() not in GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE:
+            _warmup(w, ws)
+            GROUPED_FP8_GEMM_NT_CONTIGUOUS_WARMUP_CACHE.add(w.size())
+
+
+def deepgemm_fp8_gemm_nt_warmup(model: torch.nn.Module, max_tokens: int):
+    dg_modules = [
+        m for m in model.modules() if _fp8_linear_may_use_deep_gemm(m)
+    ]
+
+    for dgm in dg_modules:
+        w, ws, _ = _extract_data_from_linear_base_module(dgm)
+        _deepgemm_fp8_gemm_nt_warmup(w=w, ws=ws, max_tokens=max_tokens)
+
+
+def deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(model: torch.nn.Module):
+    dg_modules = [
+        m for m in model.modules()
+        if _fused_moe_grouped_gemm_may_use_deep_gemm(m)
+    ]
+
+    for dgm in dg_modules:
+        w13, w13_scale, w2, w2_scale, num_topk = (
+            _extract_data_from_fused_moe_module(dgm))
+        _deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(
+            w13, w2, w13_scale, w2_scale, num_topk)
+
+
+def deep_gemm_warmup(model: torch.nn.Module, max_tokens: int):
+    deepgemm_fp8_gemm_nt_warmup(model, max_tokens)
+    deepgemm_grouped_fp8_gemm_nt_contiguous_warmup(model)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/warmup/kernel_warmup.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/warmup/kernel_warmup.py
new file mode 100644
index 0000000000000000000000000000000000000000..761172e4d361664733a66eb059335435886becd0
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/model_executor/warmup/kernel_warmup.py
@@ -0,0 +1,57 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Warmup kernels used during model execution.
+This is useful specifically for JIT'ed kernels as we don't want JIT'ing to
+happen during model execution.
+"""
+from typing import TYPE_CHECKING
+
+import torch
+
+import vllm.envs as envs
+from vllm.model_executor.warmup.deep_gemm_warmup import deep_gemm_warmup
+from vllm.platforms import current_platform
+from vllm.utils.deep_gemm import is_deep_gemm_supported
+from vllm.utils.flashinfer import has_flashinfer
+
+if TYPE_CHECKING:
+    from vllm.v1.worker.gpu_model_runner import GPUModelRunner
+    from vllm.v1.worker.gpu_worker import Worker
+
+
+def kernel_warmup(worker: "Worker"):
+    # Deep GEMM warmup
+    do_deep_gemm_warmup = (envs.VLLM_USE_DEEP_GEMM
+                           and is_deep_gemm_supported()
+                           and not envs.VLLM_SKIP_DEEP_GEMM_WARMUP)
+    if do_deep_gemm_warmup:
+        model = worker.get_model()
+        max_tokens = worker.scheduler_config.max_num_batched_tokens
+        deep_gemm_warmup(model, max_tokens)
+
+    # FlashInfer autotune for Blackwell (SM 10.0) GPUs
+    if has_flashinfer() and current_platform.is_device_capability(100):
+        flashinfer_autotune(worker.model_runner)
+
+
+def flashinfer_autotune(runner: "GPUModelRunner") -> None:
+    """
+    Autotune FlashInfer operations.
+    FlashInfer have many implementations for the same operation,
+    autotuning runs benchmarks for each implementation and stores
+    the results. The results are cached transparently and
+    future calls to FlashInfer will use the best implementation.
+    Without autotuning, FlashInfer will rely on heuristics, which may
+    be significantly slower.
+    """
+    from vllm.utils.flashinfer import autotune
+
+    with torch.inference_mode(), autotune():
+        # We skip EPLB here since we don't want to record dummy metrics
+        # When autotuning with number of tokens m, flashinfer will autotune
+        # operations for all number of tokens up to m.
+        # So we only need to run with the max number of tokens.
+        runner._dummy_run(runner.scheduler_config.max_num_batched_tokens,
+                          skip_eplb=True,
+                          is_profile=True)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/multimodal/base.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/multimodal/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef8f1b2e17b47a6cee114ea95902144a58d27299
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/multimodal/base.py
@@ -0,0 +1,219 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from abc import ABC, abstractmethod
+from collections.abc import Sequence
+from pathlib import Path
+from typing import TYPE_CHECKING, Generic, NamedTuple, TypeVar
+
+if TYPE_CHECKING:
+    from vllm.sequence import SequenceGroupMetadata
+
+from .inputs import MultiModalKwargs, PlaceholderRange
+
+_T = TypeVar("_T")
+
+
+class MultiModalPlaceholderMap:
+    """
+    Relates multi-modal embeddings to their corresponding placeholders.
+
+    Note: This is only used in V0.
+    """
+
+    class IndexMap(NamedTuple):
+        src: list[int]
+        dest: list[int]
+
+    src_ranges: list[range]
+    """
+    The indices of the multi-modal embeddings that will replace the
+    corresponding placeholder embeddings pointed to by ``dest_ranges``.
+    """
+
+    src_len: int
+    """
+    The total number of flattened multi-modal embeddings.
+    """
+
+    dest_ranges: list[range]
+    """
+    The indices of the placeholder embeddings that will be replaced by the
+    multimodal embeddings.
+    """
+
+    dest_len: int
+    """
+    The total number of embeddings in the destination tensor.
+    """
+
+    def __init__(self):
+        self.src_ranges = []
+        self.src_len = 0
+        self.dest_ranges = []
+        self.dest_len = 0
+
+    @classmethod
+    def from_seq_group(
+        cls, seq_group: "SequenceGroupMetadata", positions: range
+    ) -> tuple[MultiModalKwargs, dict[str, "MultiModalPlaceholderMap"]]:
+        """
+        Returns the multi-modal items that intersect with the portion of a
+        prompt (``seq_group``) represented by ``positions``, as well as a
+        ``MultiModalPlaceholderMap`` that relates the multi-modal embedding
+        vectors to their corresponding placeholders.
+
+        Examples:
+
+        ```
+        Prompt:    |AAAA BBBB What's in these images?|
+        Positions: |.................................|
+
+            images      = [A, B]
+            src_ranges  = [(0, 4), (4, 8)]
+            dest_ranges = [(0, 4), (5, 9)]
+
+        Prompt:    |AAAA BBBB What's in these images?|
+        Positions: |  .....                          |
+
+            images      = [A, B]
+            src_ranges  = [(2, 4), (4, 6)]
+            dest_ranges = [(0, 2), (3, 5)]
+
+        Prompt:    |AAAA BBBB What's in these images?|
+        Positions: |     .........                   |
+
+            images      = [B]
+            src_ranges  = [(0, 4)]
+            dest_ranges = [(0, 4)]
+
+        Prompt:    |AAAA BBBB What's in these images?|
+        Positions: |          .......................|
+
+            images      = []
+            src_ranges  = []
+            dest_ranges = []
+        ```
+        """
+        seq_mm_data = seq_group.multi_modal_data
+        seq_mm_placeholders = seq_group.multi_modal_placeholders
+
+        if not seq_mm_data or not seq_mm_placeholders:
+            return MultiModalKwargs(), {}
+
+        placeholder_maps = dict[str, MultiModalPlaceholderMap]()
+
+        for modality, placeholders in seq_mm_placeholders.items():
+            placeholder_map = MultiModalPlaceholderMap()
+
+            if positions:
+                placeholder_map.append_items_from_seq_group(
+                    positions,
+                    # Dummy, since we don't care about intersecting items
+                    [None] * len(placeholders),
+                    placeholders,
+                )
+
+            placeholder_maps[modality] = placeholder_map
+
+        return seq_mm_data, placeholder_maps
+
+    def append_items_from_seq_group(
+        self,
+        positions: range,
+        multi_modal_items: list[_T],
+        multi_modal_placeholders: Sequence[PlaceholderRange],
+    ) -> list[_T]:
+        """
+        Adds the multi-modal items that intersect ```positions`` to this
+        placeholder map and returns the intersecting items.
+        """
+        intersecting_items = []
+
+        if len(multi_modal_items) != len(multi_modal_placeholders):
+            raise ValueError(
+                "Multi-modal placeholders and items must have the same length."
+            )
+        for placeholder_dict, mm_item in zip(multi_modal_placeholders,
+                                             multi_modal_items):
+            placeholder = range(
+                placeholder_dict.offset,
+                placeholder_dict.offset + placeholder_dict.length,
+            )
+            intersection = range(
+                max(positions.start, placeholder.start),
+                min(positions.stop, placeholder.stop),
+            )
+
+            if not intersection:
+                # Skip this multi-modal item.
+                continue
+
+            token_embedding_range = range(
+                intersection.start - positions.start,
+                intersection.stop - positions.start,
+            )
+
+            multimodal_embedding_range = range(
+                intersection.start - placeholder.start + self.src_len,
+                intersection.stop - placeholder.start + self.src_len,
+            )
+
+            intersecting_items.append(mm_item)
+            self.dest_ranges.append(token_embedding_range)
+            self.src_ranges.append(multimodal_embedding_range)
+            self.src_len += len(placeholder)
+
+        self.dest_len += len(positions)
+        return intersecting_items
+
+    def extend(self, other: "MultiModalPlaceholderMap"):
+        """
+        Adds the placeholders from another ``MultiModalPlaceholderMap`` to this
+        instance based on the source and destination tensors being
+        concatenated.
+        """
+
+        self.src_ranges.extend(
+            range(self.src_len + r.start, self.src_len + r.stop)
+            for r in other.src_ranges)
+        self.src_len += other.src_len
+        self.dest_ranges.extend(
+            range(self.dest_len + r.start, self.dest_len + r.stop)
+            for r in other.dest_ranges)
+        self.dest_len += other.dest_len
+
+    def index_map(self) -> "IndexMap":
+        """
+        Finalizes the placeholder map into lists of indices that can be used to
+        index the source and destination tensors.
+        """
+
+        src_indices = [i for r in self.src_ranges for i in r]
+        dest_indices = [i for r in self.dest_ranges for i in r]
+
+        if len(src_indices) != len(dest_indices):
+            raise ValueError(
+                f"The number of source ({len(src_indices)}) and destination "
+                f"indices ({len(dest_indices)}) must be the same.")
+
+        return self.IndexMap(src=src_indices, dest=dest_indices)
+
+
+class MediaIO(ABC, Generic[_T]):
+
+    @abstractmethod
+    def load_bytes(self, data: bytes) -> _T:
+        raise NotImplementedError
+
+    @abstractmethod
+    def load_base64(self, media_type: str, data: str) -> _T:
+        """
+        List of media types:
+        https://www.iana.org/assignments/media-types/media-types.xhtml
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def load_file(self, filepath: Path) -> _T:
+        raise NotImplementedError
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/platforms/cuda.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/platforms/cuda.py
new file mode 100644
index 0000000000000000000000000000000000000000..321db8287c0f82fda27fe240d63aebb869b1cf94
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/platforms/cuda.py
@@ -0,0 +1,654 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Code inside this file can safely assume cuda platform, e.g. importing
+pynvml. However, it should not initialize cuda context.
+"""
+
+import os
+from datetime import timedelta
+from functools import cache, wraps
+from typing import TYPE_CHECKING, Callable, Optional, TypeVar, Union
+
+import torch
+from torch.distributed import PrefixStore, ProcessGroup
+from torch.distributed.distributed_c10d import is_nccl_available
+from typing_extensions import ParamSpec
+
+# import custom ops, trigger op registration
+import vllm._C  # noqa
+import vllm.envs as envs
+from vllm.logger import init_logger
+from vllm.utils import cuda_device_count_stateless, import_pynvml
+
+from .interface import DeviceCapability, Platform, PlatformEnum, _Backend
+
+if TYPE_CHECKING:
+    from vllm.config import ModelConfig, VllmConfig
+
+logger = init_logger(__name__)
+
+_P = ParamSpec("_P")
+_R = TypeVar("_R")
+
+pynvml = import_pynvml()
+
+# pytorch 2.5 uses cudnn sdpa by default, which will cause crash on some models
+# see https://github.com/huggingface/diffusers/issues/9704 for details
+torch.backends.cuda.enable_cudnn_sdp(False)
+
+
+def with_nvml_context(fn: Callable[_P, _R]) -> Callable[_P, _R]:
+
+    @wraps(fn)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _R:
+        pynvml.nvmlInit()
+        try:
+            return fn(*args, **kwargs)
+        finally:
+            pynvml.nvmlShutdown()
+
+    return wrapper
+
+
+class CudaPlatformBase(Platform):
+    _enum = PlatformEnum.CUDA
+    device_name: str = "cuda"
+    device_type: str = "cuda"
+    dispatch_key: str = "CUDA"
+    ray_device_key: str = "GPU"
+    dist_backend: str = "nccl"
+    device_control_env_var: str = "CUDA_VISIBLE_DEVICES"
+
+    @property
+    def supported_dtypes(self) -> list[torch.dtype]:
+        if self.has_device_capability(80):
+            # Ampere and Hopper or later NVIDIA GPUs.
+            return [torch.bfloat16, torch.float16, torch.float32]
+        elif (not self.has_device_capability(80)
+              ) and self.has_device_capability(60):
+            # Pascal, Volta and Turing NVIDIA GPUs, BF16 is not supported
+            return [torch.float16, torch.float32]
+        # Kepler and Maxwell NVIDIA GPUs, only FP32 is supported,
+        # though vLLM doesn't support these GPUs.
+        return [torch.float32]
+
+    @classmethod
+    def set_device(cls, device: torch.device) -> None:
+        """
+        Set the device for the current platform.
+        """
+        torch.cuda.set_device(device)
+        # With this trick we can force the device to be set eagerly
+        # see https://github.com/pytorch/pytorch/issues/155668
+        # for why and when it is needed
+        _ = torch.zeros(1, device=device)
+
+    @classmethod
+    def get_device_capability(cls,
+                              device_id: int = 0
+                              ) -> Optional[DeviceCapability]:
+        raise NotImplementedError
+
+    @classmethod
+    def get_device_name(cls, device_id: int = 0) -> str:
+        raise NotImplementedError
+
+    @classmethod
+    def get_device_total_memory(cls, device_id: int = 0) -> int:
+        raise NotImplementedError
+
+    @classmethod
+    def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
+        if enforce_eager and not envs.VLLM_USE_V1:
+            logger.warning(
+                "To see benefits of async output processing, enable CUDA "
+                "graph. Since, enforce-eager is enabled, async output "
+                "processor cannot be used")
+            return False
+        return True
+
+    @classmethod
+    def is_fully_connected(cls, device_ids: list[int]) -> bool:
+        raise NotImplementedError
+
+    @classmethod
+    def log_warnings(cls):
+        pass
+
+    @classmethod
+    def check_and_update_config(cls, vllm_config: "VllmConfig") -> None:
+        parallel_config = vllm_config.parallel_config
+        model_config = vllm_config.model_config
+
+        if parallel_config.worker_cls == "auto":
+            if vllm_config.speculative_config:
+                if not envs.VLLM_USE_V1:
+                    raise NotImplementedError(
+                        "Speculative decoding is not supported on vLLM V0.")
+                parallel_config.worker_cls = "vllm.v1.worker.gpu_worker.Worker"
+            else:
+                if envs.VLLM_USE_V1:
+                    parallel_config.worker_cls = \
+                        "vllm.v1.worker.gpu_worker.Worker"
+                else:
+                    parallel_config.worker_cls = "vllm.worker.worker.Worker"
+
+        cache_config = vllm_config.cache_config
+        if cache_config and cache_config.block_size is None:
+            cache_config.block_size = 16
+
+        # TODO(lucas): handle this more gracefully
+        # Note: model_config may be None during testing
+        if model_config is not None and model_config.use_mla:
+            # If `VLLM_ATTENTION_BACKEND` is not set and we are using MLA,
+            # then we default to FlashMLA backend for non-blackwell GPUs,
+            # else we default to CutlassMLA. For each case, we force the
+            # required block_size.
+            use_flashmla = False
+            use_cutlass_mla = False
+
+            if envs.VLLM_ATTENTION_BACKEND is None:
+                # Default case
+                if cls.is_device_capability(100):
+                    # Blackwell => Force CutlassMLA.
+                    use_cutlass_mla = True
+                    # TODO: This does not work, because the
+                    # global_force_attn_backend_context_manager is not set.
+                    # See vllm/attention/selector.py:_cached_get_attn_backend
+                    envs.VLLM_ATTENTION_BACKEND = "CUTLASS_MLA"
+                else:
+                    # Not Blackwell
+                    use_flashmla = True
+            else:
+                # Forced case
+                use_flashmla = (envs.VLLM_ATTENTION_BACKEND == "FLASHMLA")
+                use_cutlass_mla = (
+                    envs.VLLM_ATTENTION_BACKEND == "CUTLASS_MLA")
+
+            from vllm.attention.ops.flashmla import is_flashmla_supported
+            if use_flashmla and is_flashmla_supported()[0] \
+                and cache_config.block_size != 64:
+                cache_config.block_size = 64
+                logger.info(
+                    "Forcing kv cache block size to 64 for FlashMLA backend.")
+
+            if use_cutlass_mla and cache_config.block_size != 128:
+                cache_config.block_size = 128
+                logger.info("Forcing kv cache block size to 128 for "
+                            "CUTLASS_MLA backend.")
+
+        # lazy import to avoid circular import
+        from vllm.config import CUDAGraphMode
+
+        compilation_config = vllm_config.compilation_config
+        if (envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput"
+                and parallel_config.data_parallel_size > 1
+                and compilation_config.cudagraph_mode != CUDAGraphMode.NONE):
+            logger.info(
+                "Data Parallel: disabling cudagraphs since DP "
+                "with DeepEP high-throughput kernels are not CUDA Graph "
+                "compatible. The DeepEP low-latency kernels are CUDA Graph "
+                "compatible. Set the all_to_all backend to deepep_low_latency "
+                "to use those kernels instead.")
+            compilation_config.cudagraph_mode = CUDAGraphMode.NONE
+            if model_config is not None:
+                model_config.enforce_eager = True
+
+    @classmethod
+    def get_current_memory_usage(cls,
+                                 device: Optional[torch.types.Device] = None
+                                 ) -> float:
+        torch.cuda.empty_cache()
+        torch.cuda.reset_peak_memory_stats(device)
+        return torch.cuda.max_memory_allocated(device)
+
+    @classmethod
+    def get_vit_attn_backend(cls, support_fa: bool = False) -> _Backend:
+        if cls.has_device_capability(80) and support_fa:
+            from transformers.utils import is_flash_attn_2_available
+            if is_flash_attn_2_available():
+                return _Backend.FLASH_ATTN
+            logger.warning_once(
+                "Current `vllm-flash-attn` has a bug inside vision "
+                "module, so we use xformers backend instead. You can "
+                "run `pip install flash-attn` to use flash-attention "
+                "backend.")
+        # Fallback for Volta/Turing GPUs or FA not supported
+        return _Backend.XFORMERS
+
+    @classmethod
+    def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
+                             kv_cache_dtype, block_size, use_v1, use_mla,
+                             has_sink) -> str:
+        if use_mla:
+            # TODO(lucas): refactor to be more concise
+            #  we should probably consider factoring out V1 here
+            if selected_backend == _Backend.CUTLASS_MLA or (
+                    cls.is_device_capability(100) and selected_backend is None
+                    and block_size == 128):
+                if use_v1:
+                    logger.info_once("Using Cutlass MLA backend on V1 engine.")
+                    return ("vllm.v1.attention.backends.mla."
+                            "cutlass_mla.CutlassMLABackend")
+                else:
+                    logger.warning(
+                        "Cutlass MLA backend is only supported on V1 engine")
+            if selected_backend == _Backend.TRITON_MLA or block_size != 64:
+                if use_v1:
+                    logger.info_once("Using Triton MLA backend on V1 engine.")
+                    return ("vllm.v1.attention.backends.mla."
+                            "triton_mla.TritonMLABackend")
+                else:
+                    logger.info("Using Triton MLA backend.")
+                    return "vllm.attention.backends.triton_mla.TritonMLABackend"
+            else:
+                from vllm.attention.backends.flashmla import (
+                    is_flashmla_supported)
+                if not is_flashmla_supported()[0]:
+                    logger.warning(
+                        "FlashMLA backend is not supported due to %s",
+                        is_flashmla_supported()[1])
+                elif block_size != 64:
+                    logger.warning(
+                        "FlashMLA backend is not supported for block size %d"
+                        " (currently only supports block size 64).",
+                        block_size)
+                else:
+                    if use_v1:
+                        logger.info_once(
+                            "Using FlashMLA backend on V1 engine.")
+                        return ("vllm.v1.attention.backends.mla."
+                                "flashmla.FlashMLABackend")
+                    else:
+                        logger.info("Using FlashMLA backend.")
+                        return ("vllm.attention.backends."
+                                "flashmla.FlashMLABackend")
+        if use_v1:
+            FLASHINFER_V1 = "vllm.v1.attention.backends.flashinfer.FlashInferBackend"  # noqa: E501
+            FLEX_ATTENTION_V1 = "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend"  # noqa: E501
+            TRITON_ATTN_VLLM_V1 = "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend"  # noqa: E501
+            FLASH_ATTN_V1 = "vllm.v1.attention.backends.flash_attn.FlashAttentionBackend"  # noqa: E501
+            TREE_ATTN_V1 = "vllm.v1.attention.backends.tree_attn.TreeAttentionBackend"  # noqa: E501
+            XFORMERS_V1 = "vllm.v1.attention.backends.xformers.XFormersAttentionBackend"  # noqa: E501
+
+            if selected_backend == _Backend.FLASHINFER:
+                logger.info_once("Using FlashInfer backend on V1 engine.")
+                if cls.has_device_capability(100):
+                    from vllm.v1.attention.backends.utils import (
+                        set_kv_cache_layout)
+                    set_kv_cache_layout("HND")
+                return FLASHINFER_V1
+            elif selected_backend == _Backend.FLEX_ATTENTION:
+                logger.info_once("Using FlexAttention backend on V1 engine.")
+                return FLEX_ATTENTION_V1
+            elif selected_backend == _Backend.TRITON_ATTN_VLLM_V1:
+                logger.info_once("Using Triton backend on V1 engine.")
+                return TRITON_ATTN_VLLM_V1
+            elif selected_backend == _Backend.FLASH_ATTN:
+                logger.info_once("Using Flash Attention backend on V1 engine.")
+                return FLASH_ATTN_V1
+            elif selected_backend == _Backend.TREE_ATTN:
+                logger.info_once("Using Tree Attention backend on V1 engine.")
+                return TREE_ATTN_V1
+            elif selected_backend == _Backend.XFORMERS_VLLM_V1:
+                logger.info_once("Using XFormers backend on V1 engine.")
+                return XFORMERS_V1
+
+            from vllm.attention.selector import is_attn_backend_supported
+
+            # Default backends for V1 engine
+            # Prefer FlashInfer for Blackwell GPUs if installed
+            if cls.is_device_capability(100):
+                if is_default_backend_supported := is_attn_backend_supported(
+                        FLASHINFER_V1, head_size, dtype):
+                    from vllm.v1.attention.backends.utils import (
+                        set_kv_cache_layout)
+
+                    logger.info_once(
+                        "Using FlashInfer backend with HND KV cache layout on "
+                        "V1 engine by default for Blackwell (SM 10.0) GPUs.")
+                    set_kv_cache_layout("HND")
+
+                    return FLASHINFER_V1
+
+                if not is_default_backend_supported.can_import:
+                    logger.warning_once(
+                        "FlashInfer failed to import for V1 engine on "
+                        "Blackwell (SM 10.0) GPUs; it is recommended to "
+                        "install FlashInfer for better performance.")
+
+            # FlashAttention is the default for SM 8.0+ GPUs
+            if cls.has_device_capability(80):
+                if has_sink and not cls.is_device_capability(90):
+                    logger.info_once("Using Triton backend on V1 engine.")
+                    return TRITON_ATTN_VLLM_V1
+                if is_default_backend_supported := is_attn_backend_supported(
+                        FLASH_ATTN_V1, head_size, dtype,
+                        allow_import_error=False):
+                    logger.info_once("Using Flash Attention backend on "
+                                     "V1 engine.")
+                    return FLASH_ATTN_V1
+
+            # FlexAttention is the default for older GPUs
+            else:
+                logger.info_once("Using FlexAttention backend on V1 engine.")
+                return FLEX_ATTENTION_V1
+
+            assert not is_default_backend_supported
+
+            use_flex_attention_reason = {}
+            if not is_default_backend_supported.head_size:
+                use_flex_attention_reason["head_size"] = head_size
+            if not is_default_backend_supported.dtype:
+                use_flex_attention_reason["dtype"] = dtype
+
+            logger.info_once(
+                "Using FlexAttention backend for %s on V1 engine.",
+                ", ".join(f"{k}={v}"
+                          for k, v in use_flex_attention_reason.items()),
+            )
+            return FLEX_ATTENTION_V1
+
+        # Backends for V0 engine
+        if selected_backend == _Backend.FLASHINFER:
+            logger.info("Using FlashInfer backend.")
+            if cls.has_device_capability(100):
+                from vllm.v1.attention.backends.utils import (
+                    set_kv_cache_layout)
+                logger.info_once(
+                    "Using HND KV cache layout on V1 engine by default for "
+                    "Blackwell (SM 10.0) GPUs.")
+                set_kv_cache_layout("HND")
+            return "vllm.attention.backends.flashinfer.FlashInferBackend"
+        elif selected_backend == _Backend.XFORMERS:
+            logger.info("Using XFormers backend.")
+            return "vllm.attention.backends.xformers.XFormersBackend"
+        elif selected_backend == _Backend.DUAL_CHUNK_FLASH_ATTN:
+            logger.info("Using DualChunkFlashAttention backend.")
+            return ("vllm.attention.backends.dual_chunk_flash_attn."
+                    "DualChunkFlashAttentionBackend")
+        elif selected_backend == _Backend.DIFFERENTIAL_FLASH_ATTN:
+            logger.info("Using DifferentialFlashAttention backend.")
+            return ("vllm.attention.backends.differential_flash_attn."
+                    "DifferentialFlashAttentionBackend")
+        elif selected_backend == _Backend.FLASH_ATTN:
+            pass
+        elif selected_backend:
+            raise ValueError(
+                f"Invalid attention backend for {cls.device_name}, "
+                f"with use_v1: {use_v1} use_mla: {use_mla}")
+
+        target_backend = _Backend.FLASH_ATTN
+        if not cls.has_device_capability(80):
+            # Volta and Turing NVIDIA GPUs.
+            logger.info(
+                "Cannot use FlashAttention-2 backend for Volta and Turing "
+                "GPUs.")
+            target_backend = _Backend.XFORMERS
+        elif dtype not in (torch.float16, torch.bfloat16):
+            logger.info(
+                "Cannot use FlashAttention-2 backend for dtype other than "
+                "torch.float16 or torch.bfloat16.")
+            target_backend = _Backend.XFORMERS
+        elif block_size % 16 != 0:
+            logger.info(
+                "Cannot use FlashAttention-2 backend for block size not "
+                "divisible by 16.")
+            target_backend = _Backend.XFORMERS
+
+        # FlashAttn is valid for the model, checking if the package is
+        # installed.
+        if target_backend == _Backend.FLASH_ATTN:
+            try:
+                import vllm.vllm_flash_attn  # noqa: F401
+                from vllm.attention.backends.flash_attn import (  # noqa: F401
+                    FlashAttentionBackend, flash_attn_supports_fp8)
+
+                supported_sizes = \
+                    FlashAttentionBackend.get_supported_head_sizes()
+                if head_size not in supported_sizes:
+                    logger.info(
+                        "Cannot use FlashAttention-2 backend for head size %d.",
+                        head_size)
+                    target_backend = _Backend.XFORMERS
+                fp8_kv_cache = (kv_cache_dtype is not None
+                                and kv_cache_dtype.startswith("fp8"))
+                if (fp8_kv_cache and not flash_attn_supports_fp8()):
+                    logger.info(
+                        "Cannot use FlashAttention backend for FP8 KV cache.")
+                    logger.warning(
+                        "Please use FlashInfer backend with FP8 KV Cache for "
+                        "better performance by setting environment variable "
+                        "VLLM_ATTENTION_BACKEND=FLASHINFER")
+                    target_backend = _Backend.XFORMERS
+            except ImportError:
+                logger.info(
+                    "Cannot use FlashAttention-2 backend because the "
+                    "vllm.vllm_flash_attn package is not found. "
+                    "Make sure that vllm_flash_attn was built and installed "
+                    "(on by default).")
+                target_backend = _Backend.XFORMERS
+
+        if target_backend == _Backend.XFORMERS:
+            logger.info("Using XFormers backend.")
+            return "vllm.attention.backends.xformers.XFormersBackend"
+
+        logger.info("Using Flash Attention backend.")
+        return "vllm.attention.backends.flash_attn.FlashAttentionBackend"
+
+    @classmethod
+    def get_punica_wrapper(cls) -> str:
+        return "vllm.lora.punica_wrapper.punica_gpu.PunicaWrapperGPU"
+
+    @classmethod
+    def get_device_communicator_cls(cls) -> str:
+        return "vllm.distributed.device_communicators.cuda_communicator.CudaCommunicator"  # noqa
+
+    @classmethod
+    def supports_fp8(cls) -> bool:
+        return cls.has_device_capability(89)
+
+    @classmethod
+    def supports_v1(cls, model_config: "ModelConfig") -> bool:
+        return True
+
+    @classmethod
+    def use_custom_allreduce(cls) -> bool:
+        return True
+
+    @classmethod
+    def get_static_graph_wrapper_cls(cls) -> str:
+        return "vllm.compilation.cuda_graph.CUDAGraphWrapper"
+
+    @classmethod
+    def stateless_init_device_torch_dist_pg(
+        cls,
+        backend: str,
+        prefix_store: PrefixStore,
+        group_rank: int,
+        group_size: int,
+        timeout: timedelta,
+    ) -> ProcessGroup:
+        assert is_nccl_available()
+        pg: ProcessGroup = ProcessGroup(
+            prefix_store,
+            group_rank,
+            group_size,
+        )
+        from torch.distributed.distributed_c10d import ProcessGroupNCCL
+
+        backend_options = ProcessGroupNCCL.Options()
+        backend_options._timeout = timeout
+
+        backend_class = ProcessGroupNCCL(prefix_store, group_rank, group_size,
+                                         backend_options)
+        backend_type = ProcessGroup.BackendType.NCCL
+        device = torch.device("cuda")
+        pg._set_default_backend(backend_type)
+        backend_class._set_sequence_number_for_group()
+
+        pg._register_backend(device, backend_type, backend_class)
+        return pg
+
+    @classmethod
+    def device_count(cls) -> int:
+        return cuda_device_count_stateless()
+
+    @classmethod
+    def is_kv_cache_dtype_supported(cls, kv_cache_dtype: str) -> bool:
+        fp8_attention = kv_cache_dtype.startswith("fp8")
+        will_use_fa = (not envs.is_set("VLLM_ATTENTION_BACKEND")
+                       ) or envs.VLLM_ATTENTION_BACKEND == "FLASH_ATTN_VLLM_V1"
+        supported = False
+        if cls.is_device_capability(100):
+            supported = True
+        elif fp8_attention and will_use_fa:
+            from vllm.attention.utils.fa_utils import flash_attn_supports_fp8
+            supported = flash_attn_supports_fp8()
+        return supported
+
+
+# NVML utils
+# Note that NVML is not affected by `CUDA_VISIBLE_DEVICES`,
+# all the related functions work on real physical device ids.
+# the major benefit of using NVML is that it will not initialize CUDA
+class NvmlCudaPlatform(CudaPlatformBase):
+
+    @classmethod
+    @cache
+    @with_nvml_context
+    def get_device_capability(cls,
+                              device_id: int = 0
+                              ) -> Optional[DeviceCapability]:
+        try:
+            physical_device_id = cls.device_id_to_physical_device_id(device_id)
+            handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
+            major, minor = pynvml.nvmlDeviceGetCudaComputeCapability(handle)
+            return DeviceCapability(major=major, minor=minor)
+        except RuntimeError:
+            return None
+
+    @classmethod
+    @with_nvml_context
+    def has_device_capability(
+        cls,
+        capability: Union[tuple[int, int], int],
+        device_id: int = 0,
+    ) -> bool:
+        try:
+            return super().has_device_capability(capability, device_id)
+        except RuntimeError:
+            return False
+
+    @classmethod
+    @with_nvml_context
+    def get_device_name(cls, device_id: int = 0) -> str:
+        physical_device_id = cls.device_id_to_physical_device_id(device_id)
+        return cls._get_physical_device_name(physical_device_id)
+
+    @classmethod
+    @with_nvml_context
+    def get_device_uuid(cls, device_id: int = 0) -> str:
+        physical_device_id = cls.device_id_to_physical_device_id(device_id)
+        handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
+        return pynvml.nvmlDeviceGetUUID(handle)
+
+    @classmethod
+    @with_nvml_context
+    def get_device_total_memory(cls, device_id: int = 0) -> int:
+        physical_device_id = cls.device_id_to_physical_device_id(device_id)
+        handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
+        return int(pynvml.nvmlDeviceGetMemoryInfo(handle).total)
+
+    @classmethod
+    @with_nvml_context
+    def is_fully_connected(cls, physical_device_ids: list[int]) -> bool:
+        """
+        query if the set of gpus are fully connected by nvlink (1 hop)
+        """
+        handles = [
+            pynvml.nvmlDeviceGetHandleByIndex(i) for i in physical_device_ids
+        ]
+        for i, handle in enumerate(handles):
+            for j, peer_handle in enumerate(handles):
+                if i < j:
+                    try:
+                        p2p_status = pynvml.nvmlDeviceGetP2PStatus(
+                            handle,
+                            peer_handle,
+                            pynvml.NVML_P2P_CAPS_INDEX_NVLINK,
+                        )
+                        if p2p_status != pynvml.NVML_P2P_STATUS_OK:
+                            return False
+                    except pynvml.NVMLError:
+                        logger.exception(
+                            "NVLink detection failed. This is normal if"
+                            " your machine has no NVLink equipped.")
+                        return False
+        return True
+
+    @classmethod
+    def _get_physical_device_name(cls, device_id: int = 0) -> str:
+        handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
+        return pynvml.nvmlDeviceGetName(handle)
+
+    @classmethod
+    @with_nvml_context
+    def log_warnings(cls):
+        device_ids: int = pynvml.nvmlDeviceGetCount()
+        if device_ids > 1:
+            device_names = [
+                cls._get_physical_device_name(i) for i in range(device_ids)
+            ]
+            if (len(set(device_names)) > 1
+                    and os.environ.get("CUDA_DEVICE_ORDER") != "PCI_BUS_ID"):
+                logger.warning(
+                    "Detected different devices in the system: %s. Please"
+                    " make sure to set `CUDA_DEVICE_ORDER=PCI_BUS_ID` to "
+                    "avoid unexpected behavior.",
+                    ", ".join(device_names),
+                )
+
+
+class NonNvmlCudaPlatform(CudaPlatformBase):
+
+    @classmethod
+    @cache
+    def get_device_capability(cls, device_id: int = 0) -> DeviceCapability:
+        major, minor = torch.cuda.get_device_capability(device_id)
+        return DeviceCapability(major=major, minor=minor)
+
+    @classmethod
+    def get_device_name(cls, device_id: int = 0) -> str:
+        return torch.cuda.get_device_name(device_id)
+
+    @classmethod
+    def get_device_total_memory(cls, device_id: int = 0) -> int:
+        device_props = torch.cuda.get_device_properties(device_id)
+        return device_props.total_memory
+
+    @classmethod
+    def is_fully_connected(cls, physical_device_ids: list[int]) -> bool:
+        logger.exception(
+            "NVLink detection not possible, as context support was"
+            " not found. Assuming no NVLink available.")
+        return False
+
+
+# Autodetect either NVML-enabled or non-NVML platform
+# based on whether NVML is available.
+nvml_available = False
+try:
+    try:
+        pynvml.nvmlInit()
+        nvml_available = True
+    except Exception:
+        # On Jetson, NVML is not supported.
+        nvml_available = False
+finally:
+    if nvml_available:
+        pynvml.nvmlShutdown()
+
+CudaPlatform = NvmlCudaPlatform if nvml_available else NonNvmlCudaPlatform
+
+CudaPlatform.log_warnings()
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/transformers_utils/tokenizer.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/transformers_utils/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2be2ceeeae6db018f67f20247f9c8ce114c5b86
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/transformers_utils/tokenizer.py
@@ -0,0 +1,321 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import contextlib
+import copy
+import os
+import warnings
+from functools import lru_cache
+from pathlib import Path
+from types import MethodType
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+import huggingface_hub
+from transformers import (AutoTokenizer, PreTrainedTokenizer,
+                          PreTrainedTokenizerFast)
+
+from vllm import envs
+from vllm.logger import init_logger
+from vllm.transformers_utils.config import (
+    get_sentence_transformer_tokenizer_config)
+from vllm.transformers_utils.tokenizers import MistralTokenizer
+from vllm.transformers_utils.utils import check_gguf_file
+from vllm.utils import make_async
+
+if TYPE_CHECKING:
+    from vllm.config import ModelConfig
+    from vllm.lora.request import LoRARequest
+    from vllm.transformers_utils.tokenizer_base import TokenizerBase
+else:
+    ModelConfig = Any
+    LoRARequest = Any
+    TokenizerBase = Any
+
+logger = init_logger(__name__)
+
+AnyTokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast,
+                     TokenizerBase]
+
+
+def decode_tokens(
+    tokenizer: AnyTokenizer,
+    token_ids: list[int],
+    *,
+    skip_special_tokens: Optional[bool] = None,
+) -> str:
+    """
+    Backend-agnostic equivalent of HF's
+    `tokenizer.decode(token_ids, ...)`.
+
+    `skip_special_tokens=None` means to use the backend's default
+    settings.
+    """
+    decode_method = getattr(tokenizer, "_decode", tokenizer.decode)
+    if skip_special_tokens is not None:
+        return decode_method(token_ids,
+                             skip_special_tokens=skip_special_tokens)
+
+    return decode_method(token_ids)
+
+
+def encode_tokens(
+    tokenizer: AnyTokenizer,
+    text: str,
+    *,
+    truncation: Optional[bool] = None,
+    max_length: Optional[int] = None,
+    add_special_tokens: Optional[bool] = None,
+) -> list[int]:
+    """
+    Backend-agnostic equivalent of HF's
+    `tokenizer.encode(text, ...)`.
+
+    `add_special_tokens=None` means to use the backend's default
+    settings.
+    """
+
+    kw_args: dict[str, Any] = {}
+    if max_length is not None:
+        kw_args["max_length"] = max_length
+
+    if truncation is not None:
+        kw_args["truncation"] = truncation
+
+    if add_special_tokens is not None:
+        kw_args["add_special_tokens"] = add_special_tokens
+
+    return tokenizer.encode(text, **kw_args)
+
+
+def get_cached_tokenizer(tokenizer: AnyTokenizer) -> AnyTokenizer:
+    """
+    By default, transformers will recompute multiple tokenizer properties
+    each time they are called, leading to a significant slowdown.
+    This proxy caches these properties for faster access.
+    """
+    cached_tokenizer = copy.copy(tokenizer)
+
+    tokenizer_all_special_ids = tokenizer.all_special_ids
+    tokenizer_all_special_tokens = tokenizer.all_special_tokens
+    tokenizer_all_special_tokens_extended = (
+        tokenizer.all_special_tokens_extended)
+    tokenizer_vocab = tokenizer.get_vocab()
+    tokenizer_len = len(tokenizer)
+
+    max_token_id = max(tokenizer_vocab.values())
+    # Some tokenizers (e.g., QwenTokenizer) have special tokens that
+    # are added and included in the implementation of the vocab_size
+    # property, but not in get_vocab(); if there is an implementation
+    # of vocab size, we should take the greater value.
+    if hasattr(tokenizer, "vocab_size"):
+        with contextlib.suppress(NotImplementedError):
+            max_token_id = max(max_token_id, tokenizer.vocab_size)
+
+    class CachedTokenizer(tokenizer.__class__):  # type: ignore
+
+        @property
+        def all_special_ids(self) -> list[int]:
+            return tokenizer_all_special_ids
+
+        @property
+        def all_special_tokens(self) -> list[str]:
+            return tokenizer_all_special_tokens
+
+        @property
+        def all_special_tokens_extended(self) -> list[str]:
+            return tokenizer_all_special_tokens_extended
+
+        @property
+        def max_token_id(self) -> int:
+            return max_token_id
+
+        def get_vocab(self) -> dict[str, int]:
+            return tokenizer_vocab
+
+        def __len__(self) -> int:
+            return tokenizer_len
+
+        def __reduce__(self):
+            return get_cached_tokenizer, (tokenizer, )
+
+    CachedTokenizer.__name__ = f"Cached{tokenizer.__class__.__name__}"
+
+    cached_tokenizer.__class__ = CachedTokenizer
+    return cached_tokenizer
+
+
+def patch_padding_side(tokenizer: PreTrainedTokenizer) -> None:
+    """Patch _pad method to accept `padding_side` for older tokenizers."""
+    orig_pad = tokenizer._pad
+
+    def _pad(
+        self: PreTrainedTokenizer,
+        *args,
+        padding_side: Optional[str] = None,
+        **kwargs,
+    ):
+        if padding_side is not None and padding_side != self.padding_side:
+            msg = ("`padding_side` argument is not supported by "
+                   f"{type(tokenizer).__name__} and will be ignored.")
+            warnings.warn(msg, stacklevel=2)
+
+        return orig_pad(*args, **kwargs)
+
+    tokenizer._pad = MethodType(_pad, tokenizer)
+
+
+def get_tokenizer(
+    tokenizer_name: Union[str, Path],
+    *args,
+    tokenizer_mode: str = "auto",
+    trust_remote_code: bool = False,
+    revision: Optional[str] = None,
+    download_dir: Optional[str] = None,
+    **kwargs,
+) -> AnyTokenizer:
+    """Gets a tokenizer for the given model name via HuggingFace or ModelScope.
+    """
+    if envs.VLLM_USE_MODELSCOPE:
+        # download model from ModelScope hub,
+        # lazy import so that modelscope is not required for normal use.
+        # pylint: disable=C.
+        from modelscope.hub.snapshot_download import snapshot_download
+
+        # avoid circuit import
+        from vllm.model_executor.model_loader.weight_utils import get_lock
+
+        # Only set the tokenizer here, model will be downloaded on the workers.
+        if not os.path.exists(tokenizer_name):
+            # Use file lock to prevent multiple processes from
+            # downloading the same file at the same time.
+            with get_lock(tokenizer_name, download_dir):
+                tokenizer_path = snapshot_download(
+                    model_id=tokenizer_name,
+                    cache_dir=download_dir,
+                    revision=revision,
+                    local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
+                    # Ignore weights - we only need the tokenizer.
+                    ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
+                tokenizer_name = tokenizer_path
+
+    if tokenizer_mode == "slow":
+        if kwargs.get("use_fast", False):
+            raise ValueError(
+                "Cannot use the fast tokenizer in slow tokenizer mode.")
+        kwargs["use_fast"] = False
+
+    if "truncation_side" not in kwargs:
+        kwargs["truncation_side"] = "left"
+
+    # Separate model folder from file path for GGUF models
+    is_gguf = check_gguf_file(tokenizer_name)
+    if is_gguf:
+        kwargs["gguf_file"] = Path(tokenizer_name).name
+        tokenizer_name = Path(tokenizer_name).parent
+
+    # if tokenizer is from official mistral org
+    is_from_mistral_org = str(tokenizer_name).split("/")[0] == "mistralai"
+    if is_from_mistral_org and tokenizer_mode != "mistral":
+        warnings.warn(
+            'It is strongly recommended to run mistral models with '
+            '`--tokenizer-mode "mistral"` to ensure correct '
+            'encoding and decoding.',
+            FutureWarning,
+            stacklevel=2)
+
+    tokenizer: AnyTokenizer
+    if tokenizer_mode == "mistral":
+        tokenizer = MistralTokenizer.from_pretrained(str(tokenizer_name),
+                                                     revision=revision)
+    elif tokenizer_mode == "custom":
+        from vllm.transformers_utils.tokenizer_base import TokenizerRegistry
+        tokenizer = TokenizerRegistry.get_tokenizer(str(tokenizer_name),
+                                                    *args,
+                                                    revision=revision,
+                                                    download_dir=download_dir,
+                                                    **kwargs)
+    else:
+        try:
+            tokenizer = AutoTokenizer.from_pretrained(
+                tokenizer_name,
+                *args,
+                trust_remote_code=trust_remote_code,
+                revision=revision,
+                **kwargs,
+            )
+        except ValueError as e:
+            # If the error pertains to the tokenizer class not existing or not
+            # currently being imported,
+            # suggest using the --trust-remote-code flag.
+            if not trust_remote_code and (
+                    "does not exist or is not currently imported." in str(e)
+                    or "requires you to execute the tokenizer file" in str(e)):
+                err_msg = ("Failed to load the tokenizer. If the tokenizer "
+                           "is a custom tokenizer not yet available in the "
+                           "HuggingFace transformers library, consider "
+                           "setting `trust_remote_code=True` in LLM or using "
+                           "the `--trust-remote-code` flag in the CLI.")
+                raise RuntimeError(err_msg) from e
+            else:
+                raise e
+
+        # The special_tokens in tokenizer should also be
+        # controlled by do_lower_case in encoder_config
+        encoder_config = get_sentence_transformer_tokenizer_config(
+            tokenizer_name, revision)
+        if isinstance(encoder_config, dict) and encoder_config.get(
+                "do_lower_case", False):
+            special_tokens_map = {
+                k: v.lower()
+                for k, v in tokenizer.special_tokens_map.items()
+            }
+            tokenizer.add_special_tokens(special_tokens_map)
+
+        # NOTE: We can remove this after https://github.com/zai-org/ChatGLM3/issues/1324
+        if type(tokenizer).__name__ in ("ChatGLMTokenizer",
+                                        "ChatGLM4Tokenizer"):
+            assert isinstance(tokenizer, PreTrainedTokenizer)
+            patch_padding_side(tokenizer)
+
+        if not isinstance(tokenizer, PreTrainedTokenizerFast):
+            logger.warning(
+                "Using a slow tokenizer. This might cause a significant "
+                "slowdown. Consider using a fast tokenizer instead.")
+        tokenizer = get_cached_tokenizer(tokenizer)
+
+    return tokenizer
+
+
+cached_get_tokenizer = lru_cache(get_tokenizer)
+
+
+def cached_tokenizer_from_config(
+    model_config: ModelConfig,
+    **kwargs: Any,
+):
+    return cached_get_tokenizer(
+        model_config.tokenizer,
+        tokenizer_mode=model_config.tokenizer_mode,
+        revision=model_config.tokenizer_revision,
+        trust_remote_code=model_config.trust_remote_code,
+        **kwargs,
+    )
+
+
+def get_lora_tokenizer(lora_request: LoRARequest, *args,
+                       **kwargs) -> Optional[AnyTokenizer]:
+    if lora_request is None:
+        return None
+    try:
+        tokenizer = get_tokenizer(lora_request.lora_path, *args, **kwargs)
+    except Exception as e:
+        # No tokenizer was found in the LoRA folder,
+        # use base model tokenizer
+        logger.warning(
+            "No tokenizer found in %s, using base model tokenizer instead. "
+            "(Exception: %s)", lora_request.lora_path, e)
+        tokenizer = None
+    return tokenizer
+
+
+get_lora_tokenizer_async = make_async(get_lora_tokenizer)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/transformers_utils/tokenizer_base.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/transformers_utils/tokenizer_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..20e5fea714e7045d68b4a329cfd2ba881b18fa8d
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/transformers_utils/tokenizer_base.py
@@ -0,0 +1,149 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import importlib
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+if TYPE_CHECKING:
+    from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+
+
+class TokenizerBase(ABC):
+
+    @property
+    @abstractmethod
+    def all_special_tokens_extended(self) -> list[str]:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def all_special_tokens(self) -> list[str]:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def all_special_ids(self) -> list[int]:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def bos_token_id(self) -> int:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def eos_token_id(self) -> int:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def sep_token(self) -> str:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def pad_token(self) -> str:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def is_fast(self) -> bool:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def vocab_size(self) -> int:
+        raise NotImplementedError()
+
+    @property
+    @abstractmethod
+    def max_token_id(self) -> int:
+        raise NotImplementedError()
+
+    def __len__(self) -> int:
+        return self.vocab_size
+
+    @abstractmethod
+    def __call__(
+        self,
+        text: Union[str, list[str], list[int]],
+        text_pair: Optional[str] = None,
+        add_special_tokens: bool = False,
+        truncation: bool = False,
+        max_length: Optional[int] = None,
+    ):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def get_vocab(self) -> dict[str, int]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def get_added_vocab(self) -> dict[str, int]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def encode_one(
+        self,
+        text: str,
+        truncation: bool = False,
+        max_length: Optional[int] = None,
+    ) -> list[int]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def encode(self,
+               text: str,
+               truncation: Optional[bool] = None,
+               max_length: Optional[int] = None,
+               add_special_tokens: Optional[bool] = None) -> list[int]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def apply_chat_template(self,
+                            messages: list["ChatCompletionMessageParam"],
+                            tools: Optional[list[dict[str, Any]]] = None,
+                            **kwargs) -> list[int]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def convert_tokens_to_string(self, tokens: list[str]) -> str:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def decode(self,
+               ids: Union[list[int], int],
+               skip_special_tokens: bool = True) -> str:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def convert_ids_to_tokens(
+        self,
+        ids: list[int],
+        skip_special_tokens: bool = True,
+    ) -> list[str]:
+        raise NotImplementedError()
+
+
+class TokenizerRegistry:
+    # Tokenizer name -> (tokenizer module, tokenizer class)
+    REGISTRY: dict[str, tuple[str, str]] = {}
+
+    @staticmethod
+    def register(name: str, module: str, class_name: str) -> None:
+        TokenizerRegistry.REGISTRY[name] = (module, class_name)
+
+    @staticmethod
+    def get_tokenizer(
+        tokenizer_name: str,
+        *args,
+        **kwargs,
+    ) -> TokenizerBase:
+        tokenizer_cls = TokenizerRegistry.REGISTRY.get(tokenizer_name)
+        if tokenizer_cls is None:
+            raise ValueError(f"Tokenizer {tokenizer_name} not found.")
+
+        tokenizer_module = importlib.import_module(tokenizer_cls[0])
+        class_ = getattr(tokenizer_module, tokenizer_cls[1])
+        return class_.from_pretrained(*args, **kwargs)
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/transformers_utils/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/transformers_utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..66c8fb797adcde0a1672c9d99f1e1b04e209d2de
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/transformers_utils/utils.py
@@ -0,0 +1,99 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+from functools import cache
+from os import PathLike
+from pathlib import Path
+from typing import Optional, Union
+
+from vllm.envs import VLLM_MODEL_REDIRECT_PATH
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+def is_s3(model_or_path: str) -> bool:
+    return model_or_path.lower().startswith('s3://')
+
+
+def check_gguf_file(model: Union[str, PathLike]) -> bool:
+    """Check if the file is a GGUF model."""
+    model = Path(model)
+    if not model.is_file():
+        return False
+    elif model.suffix == ".gguf":
+        return True
+
+    try:
+        with model.open("rb") as f:
+            header = f.read(4)
+
+        return header == b"GGUF"
+    except Exception as e:
+        logger.debug("Error reading file %s: %s", model, e)
+        return False
+
+
+def modelscope_list_repo_files(
+    repo_id: str,
+    revision: Optional[str] = None,
+    token: Union[str, bool, None] = None,
+) -> list[str]:
+    """List files in a modelscope repo."""
+    from modelscope.hub.api import HubApi
+    api = HubApi()
+    api.login(token)
+    # same as huggingface_hub.list_repo_files
+    files = [
+        file['Path'] for file in api.get_model_files(
+            model_id=repo_id, revision=revision, recursive=True)
+        if file['Type'] == 'blob'
+    ]
+    return files
+
+
+def _maybe_json_dict(path: Union[str, PathLike]) -> dict[str, str]:
+    with open(path) as f:
+        try:
+            return json.loads(f.read())
+        except Exception:
+            return dict[str, str]()
+
+
+def _maybe_space_split_dict(path: Union[str, PathLike]) -> dict[str, str]:
+    parsed_dict = dict[str, str]()
+    with open(path) as f:
+        for line in f.readlines():
+            try:
+                model_name, redirect_name = line.strip().split()
+                parsed_dict[model_name] = redirect_name
+            except Exception:
+                pass
+    return parsed_dict
+
+
+@cache
+def maybe_model_redirect(model: str) -> str:
+    """
+    Use model_redirect to redirect the model name to a local folder.
+
+    :param model: hf model name
+    :return: maybe redirect to a local folder
+    """
+
+    model_redirect_path = VLLM_MODEL_REDIRECT_PATH
+
+    if not model_redirect_path:
+        return model
+
+    if not Path(model_redirect_path).exists():
+        return model
+
+    redirect_dict = (_maybe_json_dict(model_redirect_path)
+                     or _maybe_space_split_dict(model_redirect_path))
+    if (redirect_model := redirect_dict.get(model)):
+        logger.info("model redirect: [ %s ] -> [ %s ]", model, redirect_model)
+        return redirect_model
+
+    return model
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/v1/utils.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/v1/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5750c82db023e5ad0efee33007abfe45b85cca6
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/v1/utils.py
@@ -0,0 +1,328 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import argparse
+import multiprocessing
+import time
+import weakref
+from collections.abc import Sequence
+from multiprocessing import connection
+from multiprocessing.process import BaseProcess
+from typing import (TYPE_CHECKING, Any, Callable, Generic, Optional, TypeVar,
+                    Union, overload)
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled,
+                                  usage_message)
+from vllm.utils import (get_open_port, get_open_zmq_ipc_path, get_tcp_uri,
+                        kill_process_tree)
+
+if TYPE_CHECKING:
+    from vllm.v1.engine.coordinator import DPCoordinator
+    from vllm.v1.engine.utils import (CoreEngineActorManager,
+                                      CoreEngineProcManager)
+
+logger = init_logger(__name__)
+
+T = TypeVar("T")
+
+
+class ConstantList(Generic[T], Sequence):
+
+    def __init__(self, x: list[T]) -> None:
+        self._x = x
+
+    def append(self, item):
+        raise TypeError("Cannot append to a constant list")
+
+    def extend(self, item):
+        raise TypeError("Cannot extend a constant list")
+
+    def insert(self, item):
+        raise TypeError("Cannot insert into a constant list")
+
+    def pop(self, item):
+        raise TypeError("Cannot pop from a constant list")
+
+    def remove(self, item):
+        raise TypeError("Cannot remove from a constant list")
+
+    def clear(self):
+        raise TypeError("Cannot clear a constant list")
+
+    def index(self,
+              item: T,
+              start: int = 0,
+              stop: Optional[int] = None) -> int:
+        return self._x.index(item, start,
+                             stop if stop is not None else len(self._x))
+
+    @overload
+    def __getitem__(self, item: int) -> T:
+        ...
+
+    @overload
+    def __getitem__(self, s: slice, /) -> list[T]:
+        ...
+
+    def __getitem__(self, item: Union[int, slice]) -> Union[T, list[T]]:
+        return self._x[item]
+
+    @overload
+    def __setitem__(self, item: int, value: T):
+        ...
+
+    @overload
+    def __setitem__(self, s: slice, value: T, /):
+        ...
+
+    def __setitem__(self, item: Union[int, slice], value: Union[T, list[T]]):
+        raise TypeError("Cannot set item in a constant list")
+
+    def __delitem__(self, item):
+        raise TypeError("Cannot delete item from a constant list")
+
+    def __iter__(self):
+        return iter(self._x)
+
+    def __contains__(self, item):
+        return item in self._x
+
+    def __len__(self):
+        return len(self._x)
+
+    def __repr__(self):
+        return f"ConstantList({self._x})"
+
+
+def get_engine_client_zmq_addr(local_only: bool,
+                               host: str,
+                               port: int = 0) -> str:
+    """Assign a new ZMQ socket address.
+
+    If local_only is True, participants are colocated and so a unique IPC
+    address will be returned.
+
+    Otherwise, the provided host and port will be used to construct a TCP
+    address (port == 0 means assign an available port)."""
+
+    return get_open_zmq_ipc_path() if local_only else (get_tcp_uri(
+        host, port or get_open_port()))
+
+
+class APIServerProcessManager:
+    """Manages a group of API server processes.
+    
+    Handles creation, monitoring, and termination of API server worker
+    processes. Also monitors extra processes to check if they are healthy.
+    """
+
+    def __init__(
+        self,
+        target_server_fn: Callable,
+        listen_address: str,
+        sock: Any,
+        args: argparse.Namespace,
+        num_servers: int,
+        input_addresses: list[str],
+        output_addresses: list[str],
+        stats_update_address: Optional[str] = None,
+    ):
+        """Initialize and start API server worker processes.
+        
+        Args:
+            target_server_fn: Function to call for each API server process
+            listen_address: Address to listen for client connections
+            sock: Socket for client connections
+            args: Command line arguments
+            num_servers: Number of API server processes to start
+            input_addresses: Input addresses for each API server
+            output_addresses: Output addresses for each API server
+            stats_update_address: Optional stats update address 
+        """
+        self.listen_address = listen_address
+        self.sock = sock
+        self.args = args
+
+        # Start API servers
+        spawn_context = multiprocessing.get_context("spawn")
+        self.processes: list[BaseProcess] = []
+
+        for i, in_addr, out_addr in zip(range(num_servers), input_addresses,
+                                        output_addresses):
+            client_config = {
+                "input_address": in_addr,
+                "output_address": out_addr,
+                "client_count": num_servers,
+                "client_index": i
+            }
+            if stats_update_address is not None:
+                client_config["stats_update_address"] = stats_update_address
+
+            proc = spawn_context.Process(target=target_server_fn,
+                                         name=f"ApiServer_{i}",
+                                         args=(listen_address, sock, args,
+                                               client_config))
+            self.processes.append(proc)
+            proc.start()
+
+        logger.info("Started %d API server processes", len(self.processes))
+
+        # Shutdown only the API server processes on garbage collection
+        # The extra processes are managed by their owners
+        self._finalizer = weakref.finalize(self, shutdown, self.processes)
+
+    def close(self) -> None:
+        self._finalizer()
+
+
+def wait_for_completion_or_failure(
+        api_server_manager: APIServerProcessManager,
+        engine_manager: Optional[Union["CoreEngineProcManager",
+                                       "CoreEngineActorManager"]] = None,
+        coordinator: Optional["DPCoordinator"] = None) -> None:
+    """Wait for all processes to complete or detect if any fail.
+    
+    Raises an exception if any process exits with a non-zero status.
+
+    Args:
+        api_server_manager: The manager for API servers.
+        engine_manager: The manager for engine processes.
+            If CoreEngineProcManager, it manages local engines;
+            if CoreEngineActorManager, it manages all engines.
+        coordinator: The coordinator for data parallel.
+    """
+
+    from vllm.v1.engine.utils import (CoreEngineActorManager,
+                                      CoreEngineProcManager)
+
+    try:
+        logger.info("Waiting for API servers to complete ...")
+        # Create a mapping of sentinels to their corresponding processes
+        # for efficient lookup
+        sentinel_to_proc: dict[Any, BaseProcess] = {
+            proc.sentinel: proc
+            for proc in api_server_manager.processes
+        }
+
+        if coordinator:
+            sentinel_to_proc[coordinator.proc.sentinel] = coordinator.proc
+
+        actor_run_refs = []
+        if isinstance(engine_manager, CoreEngineProcManager):
+            for proc in engine_manager.processes:
+                sentinel_to_proc[proc.sentinel] = proc
+        elif isinstance(engine_manager, CoreEngineActorManager):
+            actor_run_refs = engine_manager.get_run_refs()
+
+        # Check if any process terminates
+        while sentinel_to_proc or actor_run_refs:
+            # Wait for any process to terminate
+            ready_sentinels: list[Any] = connection.wait(sentinel_to_proc,
+                                                         timeout=5)
+
+            # Process any terminated processes
+            for sentinel in ready_sentinels:
+                proc = sentinel_to_proc.pop(sentinel)
+
+                # Check if process exited with error
+                if proc.exitcode != 0:
+                    raise RuntimeError(
+                        f"Process {proc.name} (PID: {proc.pid}) "
+                        f"died with exit code {proc.exitcode}")
+
+            if actor_run_refs:
+                import ray
+                _, actor_run_refs = ray.wait(actor_run_refs, timeout=5)
+
+    except KeyboardInterrupt:
+        logger.info("Received KeyboardInterrupt, shutting down API servers...")
+    except Exception as e:
+        logger.exception("Exception occurred while running API servers: %s",
+                         str(e))
+        raise
+    finally:
+        logger.info("Terminating remaining processes ...")
+        api_server_manager.close()
+        if coordinator:
+            coordinator.close()
+        if engine_manager:
+            engine_manager.close()
+
+
+# Note(rob): shutdown function cannot be a bound method,
+# else the gc cannot collect the object.
+def shutdown(procs: list[BaseProcess]):
+    # Shutdown the process.
+    for proc in procs:
+        if proc.is_alive():
+            proc.terminate()
+
+    # Allow 5 seconds for remaining procs to terminate.
+    deadline = time.monotonic() + 5
+    for proc in procs:
+        remaining = deadline - time.monotonic()
+        if remaining <= 0:
+            break
+        if proc.is_alive():
+            proc.join(remaining)
+
+    for proc in procs:
+        if proc.is_alive() and (pid := proc.pid) is not None:
+            kill_process_tree(pid)
+
+
+def copy_slice(from_tensor: torch.Tensor, to_tensor: torch.Tensor,
+               length: int) -> torch.Tensor:
+    """
+    Copy the first length elements of a tensor into another tensor in a
+    non-blocking manner.
+
+    Used to copy pinned CPU tensor data to pre-allocated GPU tensors.
+
+    Returns the sliced target tensor.
+    """
+    return to_tensor[:length].copy_(from_tensor[:length], non_blocking=True)
+
+
+def report_usage_stats(
+        vllm_config,
+        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT) -> None:
+    """Report usage statistics if enabled."""
+
+    if not is_usage_stats_enabled():
+        return
+
+    from vllm.model_executor.model_loader import get_architecture_class_name
+
+    usage_message.report_usage(
+        get_architecture_class_name(vllm_config.model_config),
+        usage_context,
+        extra_kvs={
+            # Common configuration
+            "dtype":
+            str(vllm_config.model_config.dtype),
+            "tensor_parallel_size":
+            vllm_config.parallel_config.tensor_parallel_size,
+            "block_size":
+            vllm_config.cache_config.block_size,
+            "gpu_memory_utilization":
+            vllm_config.cache_config.gpu_memory_utilization,
+
+            # Quantization
+            "quantization":
+            vllm_config.model_config.quantization,
+            "kv_cache_dtype":
+            str(vllm_config.cache_config.cache_dtype),
+
+            # Feature flags
+            "enable_lora":
+            bool(vllm_config.lora_config),
+            "enable_prefix_caching":
+            vllm_config.cache_config.enable_prefix_caching,
+            "enforce_eager":
+            vllm_config.model_config.enforce_eager,
+            "disable_custom_all_reduce":
+            vllm_config.parallel_config.disable_custom_all_reduce,
+        })
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/worker/__init__.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/worker/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/worker/cache_engine.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/worker/cache_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..530907012f7049da1196ae0f279bc80dcef9eab3
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/worker/cache_engine.py
@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""CacheEngine class for managing the KV cache."""
+from typing import List
+
+import torch
+
+from vllm.attention import get_attn_backend
+from vllm.config import CacheConfig, DeviceConfig, ModelConfig, ParallelConfig
+from vllm.logger import init_logger
+from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, LayerBlockType,
+                        get_dtype_size, is_pin_memory_available)
+
+logger = init_logger(__name__)
+
+
+class CacheEngine:
+    """Manages the KV cache.
+
+    This class is responsible for initializing and managing the GPU and CPU KV
+    caches. It also provides methods for performing KV cache operations, such
+    as swapping and copying.
+    """
+
+    def __init__(
+        self,
+        cache_config: CacheConfig,
+        model_config: ModelConfig,
+        parallel_config: ParallelConfig,
+        device_config: DeviceConfig,
+    ) -> None:
+        self.cache_config = cache_config
+        self.model_config = model_config
+        self.parallel_config = parallel_config
+        self.device_config = device_config
+
+        self.head_size = model_config.get_head_size()
+        # Models like Jamba, have mixed typed layers, E.g Mamba
+        self.num_attention_layers = model_config.get_num_layers_by_block_type(
+            parallel_config, LayerBlockType.attention)
+        self.num_kv_heads = model_config.get_num_kv_heads(parallel_config)
+
+        self.block_size = cache_config.block_size
+        self.num_gpu_blocks = cache_config.num_gpu_blocks
+        if self.num_gpu_blocks:
+            self.num_gpu_blocks //= parallel_config.pipeline_parallel_size
+        self.num_cpu_blocks = cache_config.num_cpu_blocks
+        if self.num_cpu_blocks:
+            self.num_cpu_blocks //= parallel_config.pipeline_parallel_size
+
+        if cache_config.cache_dtype == "auto":
+            self.dtype = model_config.dtype
+        else:
+            self.dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
+
+        # Get attention backend.
+        self.attn_backend = get_attn_backend(self.head_size,
+                                             model_config.dtype,
+                                             cache_config.cache_dtype,
+                                             self.block_size,
+                                             model_config.is_attention_free,
+                                             use_mla=model_config.use_mla)
+
+        # Initialize the cache.
+        self.gpu_cache = self._allocate_kv_cache(
+            self.num_gpu_blocks, self.device_config.device_type)
+        self.cpu_cache = self._allocate_kv_cache(self.num_cpu_blocks, "cpu")
+
+    def _allocate_kv_cache(
+        self,
+        num_blocks: int,
+        device: str,
+    ) -> List[torch.Tensor]:
+        """Allocates KV cache on the specified device."""
+        kv_cache_generic_shape = self.attn_backend.get_kv_cache_shape(
+            num_blocks, self.block_size, self.num_kv_heads, self.head_size)
+        pin_memory = is_pin_memory_available() if device == "cpu" else False
+        kv_cache: List[torch.Tensor] = []
+        try:
+            kv_cache_stride_order = self.attn_backend.get_kv_cache_stride_order(
+            )
+        except (AttributeError, NotImplementedError):
+            kv_cache_stride_order = tuple(range(len(kv_cache_generic_shape)))
+
+        # The allocation respects the backend-defined stride order to ensure
+        # the semantic remains consistent for each backend. We first obtain the
+        # generic kv cache shape and then permute it according to the stride
+        # order which could result in a non-contiguous tensor.
+        kv_cache_allocation_shape = tuple(kv_cache_generic_shape[i]
+                                          for i in kv_cache_stride_order)
+
+        for _ in range(self.num_attention_layers):
+            # null block in CpuGpuBlockAllocator requires at least that
+            # block to be zeroed-out.
+            # We zero-out everything for simplicity.
+            layer_kv_cache = torch.zeros(
+                kv_cache_allocation_shape,
+                dtype=self.dtype,
+                pin_memory=pin_memory,
+                device=device).permute(*kv_cache_stride_order)
+
+            # view back to (TOTAL_PAGES, PAGE_SIZE, entry_shape...) for cases
+            # when entry_shape is higher than 1D
+            kv_cache.append(layer_kv_cache)
+        return kv_cache
+
+    def swap_in(self, src_to_dst: torch.Tensor) -> None:
+        for i in range(self.num_attention_layers):
+            self.attn_backend.swap_blocks(self.cpu_cache[i], self.gpu_cache[i],
+                                          src_to_dst)
+
+    def swap_out(self, src_to_dst: torch.Tensor) -> None:
+        for i in range(self.num_attention_layers):
+            self.attn_backend.swap_blocks(self.gpu_cache[i], self.cpu_cache[i],
+                                          src_to_dst)
+
+    def copy(self, src_to_dsts: torch.Tensor) -> None:
+        self.attn_backend.copy_blocks(self.gpu_cache, src_to_dsts)
+
+    @staticmethod
+    def get_cache_block_size(
+        cache_config: CacheConfig,
+        model_config: ModelConfig,
+        parallel_config: ParallelConfig,
+    ) -> int:
+        head_size = model_config.get_head_size()
+        num_heads = model_config.get_num_kv_heads(parallel_config)
+        num_attention_layers = model_config.get_num_layers_by_block_type(
+            parallel_config, LayerBlockType.attention)
+
+        if cache_config.cache_dtype == "auto":
+            dtype = model_config.dtype
+        else:
+            dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
+
+        key_cache_entry = num_heads * head_size
+
+        # For MLA there is no value cache, since the latent vector
+        # is joint keys and values.
+        value_cache_entry = key_cache_entry if not model_config.use_mla else 0
+        total = num_attention_layers * cache_config.block_size * \
+            (key_cache_entry + value_cache_entry)
+
+        dtype_size = get_dtype_size(dtype)
+        return dtype_size * total
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/worker/pooling_model_runner.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/worker/pooling_model_runner.py
new file mode 100644
index 0000000000000000000000000000000000000000..e49783ad9b244cb024a8ca5ceb5bbdc1e3233a0b
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/worker/pooling_model_runner.py
@@ -0,0 +1,214 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import dataclasses
+from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast
+
+import torch
+
+from vllm.config import VllmConfig
+from vllm.distributed import get_pp_group
+from vllm.forward_context import set_forward_context
+from vllm.logger import init_logger
+from vllm.model_executor.models.interfaces_base import VllmModelForPooling
+from vllm.model_executor.pooling_metadata import PoolingMetadata
+from vllm.multimodal import MultiModalKwargs
+from vllm.pooling_params import PoolingParams
+from vllm.sequence import (IntermediateTensors, PoolerOutput, SequenceData,
+                           SequenceGroupMetadata)
+from vllm.worker.model_runner import (GPUModelRunnerBase, ModelInputForGPU,
+                                      ModelInputForGPUBuilder)
+
+logger = init_logger(__name__)
+
+
+@dataclasses.dataclass(frozen=True)
+class ModelInputForGPUWithPoolingMetadata(ModelInputForGPU):
+    """
+    Used by the PoolingModelRunner.
+    """
+    pooling_metadata: Optional["PoolingMetadata"] = None
+
+
+class PoolingModelRunner(
+        GPUModelRunnerBase[ModelInputForGPUWithPoolingMetadata]):
+    _model_input_cls: Type[ModelInputForGPUWithPoolingMetadata] = (
+        ModelInputForGPUWithPoolingMetadata)
+    _builder_cls: Type[ModelInputForGPUBuilder] = ModelInputForGPUBuilder
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        kv_cache_dtype: Optional[str] = "auto",
+        is_driver_worker: bool = False,
+    ):
+        super().__init__(vllm_config=vllm_config,
+                         kv_cache_dtype=kv_cache_dtype,
+                         is_driver_worker=is_driver_worker)
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        model_input: ModelInputForGPUWithPoolingMetadata,
+        kv_caches: List[torch.Tensor],
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        num_steps: int = 1,
+    ) -> Optional[Union[List[PoolerOutput], IntermediateTensors]]:
+        if num_steps > 1:
+            raise ValueError(
+                "PoolingModelRunner does not support multi-step execution.")
+
+        if self.lora_config:
+            assert model_input.lora_requests is not None
+            assert model_input.lora_mapping is not None
+            self.set_active_loras(model_input.lora_requests,
+                                  model_input.lora_mapping)
+
+        # Currently cuda graph is only supported by the decode phase.
+        assert model_input.attn_metadata is not None
+        prefill_meta = model_input.attn_metadata.prefill_metadata
+        decode_meta = model_input.attn_metadata.decode_metadata
+        virtual_engine = model_input.virtual_engine
+        # Pooling models are (ab-)used also to integrate non text models that
+        # are not autoregressive (PrithviGeosaptialMAE).
+        # These model might not use attention and do not really have a prefill
+        # and decode phase. The model input is processed in one shot and both
+        # decode_metadata and prefill_metadata would be None for such models.
+        # See the PlaceholderAttentionMetadata class.
+        # TODO: Figure out if cuda_graph is of any use for these models and
+        #  explore how to leverage it.
+        if (prefill_meta is None and decode_meta is not None
+                and decode_meta.use_cuda_graph):
+            if model_input.inputs_embeds is None:
+                assert model_input.input_tokens is not None
+                graph_batch_size = model_input.input_tokens.shape[0]
+                model_executable = (
+                    self.graph_runners[model_input.virtual_engine][(
+                        graph_batch_size, False)])
+            else:
+                graph_batch_size = model_input.inputs_embeds.shape[0]
+                model_executable = (
+                    self.graph_runners[model_input.virtual_engine][(
+                        graph_batch_size, True)])
+        else:
+            model_executable = self.model
+
+        multi_modal_kwargs = model_input.multi_modal_kwargs or {}
+        seqlen_agnostic_kwargs = {
+            "finished_requests_ids": model_input.finished_requests_ids,
+            "request_ids_to_seq_ids": model_input.request_ids_to_seq_ids,
+        } if self.has_inner_state else {}
+        if (self.observability_config is not None
+                and self.observability_config.collect_model_forward_time):
+            model_forward_start = torch.cuda.Event(enable_timing=True)
+            model_forward_end = torch.cuda.Event(enable_timing=True)
+            model_forward_start.record()
+
+        cross_enc_kwargs = {}
+        if model_input.token_types is not None:
+            cross_enc_kwargs["token_type_ids"] = model_input.token_types
+
+        with set_forward_context(model_input.attn_metadata, self.vllm_config,
+                                 virtual_engine):
+            hidden_or_intermediate_states = model_executable(
+                input_ids=model_input.input_tokens,
+                positions=model_input.input_positions,
+                intermediate_tensors=intermediate_tensors,
+                **MultiModalKwargs.as_kwargs(
+                    multi_modal_kwargs,
+                    device=self.device,
+                ),
+                **cross_enc_kwargs,
+                **seqlen_agnostic_kwargs,
+            )
+
+        if (self.observability_config is not None
+                and self.observability_config.collect_model_forward_time):
+            model_forward_end.record()
+
+        # Only perform pooling in the last pipeline stage.
+        if not get_pp_group().is_last_rank:
+            if (self.is_driver_worker
+                    and hidden_or_intermediate_states is not None
+                    and isinstance(hidden_or_intermediate_states,
+                                   IntermediateTensors)
+                    and self.observability_config is not None
+                    and self.observability_config.collect_model_forward_time):
+                model_forward_end.synchronize()
+                model_forward_time = model_forward_start.elapsed_time(
+                    model_forward_end)
+                orig_model_forward_time = 0.0
+                if intermediate_tensors is not None:
+                    orig_model_forward_time = intermediate_tensors.tensors.get(
+                        "model_forward_time", torch.tensor(0.0)).item()
+                hidden_or_intermediate_states.tensors["model_forward_time"] = (
+                    torch.tensor(model_forward_time + orig_model_forward_time))
+            return hidden_or_intermediate_states
+
+        # Only perform pooling in the driver worker.
+        if not self.is_driver_worker:
+            return []
+
+        return [
+            self.model.pooler(hidden_states=hidden_or_intermediate_states,
+                              pooling_metadata=model_input.pooling_metadata)
+        ]
+
+    def make_model_input_from_broadcasted_tensor_dict(
+            self,
+            tensor_dict: Dict[str,
+                              Any]) -> ModelInputForGPUWithPoolingMetadata:
+        return ModelInputForGPUWithPoolingMetadata.from_broadcasted_tensor_dict(
+            tensor_dict,
+            attn_backend=self.attn_backend,
+        )
+
+    def prepare_model_input(
+        self,
+        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
+        virtual_engine: int = 0,
+        finished_requests_ids: Optional[List[str]] = None
+    ) -> ModelInputForGPUWithPoolingMetadata:
+        assert seq_group_metadata_list is not None
+        model_input = self._prepare_model_input_tensors(
+            seq_group_metadata_list, finished_requests_ids)
+        # Prepare PoolingMetadata.
+        assert model_input.seq_lens is not None
+        pooling_metadata = self._prepare_pooling(seq_group_metadata_list,
+                                                 model_input.seq_lens)
+
+        return dataclasses.replace(model_input,
+                                   pooling_metadata=pooling_metadata)
+
+    def _prepare_pooling(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+        prompt_lens: List[int],
+    ) -> PoolingMetadata:
+        """Prepare PoolingMetadata for the sequence group metadata list."""
+        seq_groups: List[Tuple[List[int], PoolingParams]] = []
+        for i, seq_group_metadata in enumerate(seq_group_metadata_list):
+            seq_ids = list(seq_group_metadata.seq_data.keys())
+
+            pooling_params = seq_group_metadata.pooling_params
+            assert pooling_params is not None
+            assert (task := pooling_params.task) is not None, (
+                "You did not set `task` in the API")
+
+            model = cast(VllmModelForPooling, self.model)
+            to_update = model.pooler.get_pooling_updates(task)
+            to_update.apply(pooling_params)
+
+            seq_groups.append((seq_ids, pooling_params))
+
+        seq_data: Dict[int, SequenceData] = {}
+        for seq_group_metadata in seq_group_metadata_list:
+            seq_data.update(seq_group_metadata.seq_data)
+
+        pooling_metadata = PoolingMetadata(
+            seq_groups=seq_groups,
+            seq_data=seq_data,
+            prompt_lens=prompt_lens,
+        )
+
+        return pooling_metadata
diff --git a/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/worker/worker.py b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/worker/worker.py
new file mode 100644
index 0000000000000000000000000000000000000000..9dfea947568d47836b9bcbef1a7a3e934b80362d
--- /dev/null
+++ b/platform/dbops/archive/databases_old/data/home/x/.local/lib/python3.12/site-packages/vllm/worker/worker.py
@@ -0,0 +1,587 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""A GPU worker class."""
+import gc
+import os
+from typing import Dict, List, Optional, Set, Tuple, Type, Union
+
+import torch
+import torch.distributed
+
+import vllm.envs as envs
+from vllm.attention.layer import Attention
+from vllm.config import VllmConfig, get_layers_from_vllm_config
+from vllm.device_allocator.cumem import CuMemAllocator
+from vllm.distributed import (ensure_model_parallel_initialized,
+                              init_distributed_environment,
+                              set_custom_all_reduce)
+from vllm.distributed.kv_transfer import ensure_kv_transfer_initialized
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.model_executor import set_random_seed
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
+from vllm.platforms import current_platform
+from vllm.sequence import (ExecuteModelRequest, IntermediateTensors,
+                           SequenceGroupMetadata, SequenceGroupMetadataDelta)
+from vllm.utils import (GiB_bytes, MemorySnapshot, bind_kv_cache,
+                        memory_profiling)
+from vllm.worker.cache_engine import CacheEngine
+from vllm.worker.enc_dec_model_runner import EncoderDecoderModelRunner
+from vllm.worker.model_runner import GPUModelRunnerBase, ModelRunner
+from vllm.worker.pooling_model_runner import PoolingModelRunner
+from vllm.worker.worker_base import (LocalOrDistributedWorkerBase, WorkerBase,
+                                     WorkerInput)
+
+logger = init_logger(__name__)
+
+
+class Worker(LocalOrDistributedWorkerBase):
+    """A worker class that executes (a partition of) the model on a GPU.
+
+    Each worker is associated with a single GPU. The worker is responsible for
+    maintaining the KV cache and executing the model on the GPU. In case of
+    distributed inference, each worker is assigned a partition of the model.
+    """
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+        is_driver_worker: bool = False,
+        model_runner_cls: Optional[Type[GPUModelRunnerBase]] = None,
+    ) -> None:
+        WorkerBase.__init__(self, vllm_config)
+        self.parallel_config.rank = rank
+        self.local_rank = local_rank
+        self.rank = rank
+        self.distributed_init_method = distributed_init_method
+        self.is_driver_worker = is_driver_worker
+        if self.model_config.trust_remote_code:
+            # note: lazy import to avoid importing torch before initializing
+            from vllm.utils import init_cached_hf_modules
+            init_cached_hf_modules()
+
+        # Return hidden states from target model if the draft model is an
+        # mlp_speculator
+        speculative_config = self.speculative_config
+        model_config = self.model_config
+        speculative_args = {} if speculative_config is None \
+            or (speculative_config.draft_model_config.hf_config.model_type ==
+                model_config.hf_config.model_type) \
+            or (speculative_config.draft_model_config.hf_config.model_type
+                not in ("medusa",
+                        "mlp_speculator",
+                        "eagle",
+                        "deepseek_mtp",
+                        "glm4_moe_mtp",
+                        "mimo_mtp")) \
+                    else {"return_hidden_states": True}
+
+        ModelRunnerClass: Type[GPUModelRunnerBase] = ModelRunner
+        if model_config.runner_type == "pooling":
+            ModelRunnerClass = PoolingModelRunner
+        elif self.model_config.is_encoder_decoder:
+            ModelRunnerClass = EncoderDecoderModelRunner
+        self.model_runner: GPUModelRunnerBase = ModelRunnerClass(
+            vllm_config=self.vllm_config,
+            kv_cache_dtype=self.cache_config.cache_dtype,
+            is_driver_worker=is_driver_worker,
+            **speculative_args,
+        )
+        if model_runner_cls is not None:
+            self.model_runner = model_runner_cls(self.model_runner)
+
+        # Uninitialized cache engine. Will be initialized by
+        # initialize_cache.
+        self.cache_engine: List[CacheEngine]
+        # Initialize gpu_cache as pooling models don't initialize kv_caches
+        self.gpu_cache: Optional[List[List[torch.Tensor]]] = None
+        self._seq_group_metadata_cache: Dict[str, SequenceGroupMetadata] = {}
+
+        # Buffers saved before sleep
+        self._sleep_saved_buffers: Dict[str, torch.Tensor] = {}
+
+        # Torch profiler. Enabled and configured through env vars:
+        # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
+        if envs.VLLM_TORCH_PROFILER_DIR:
+            torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
+            logger.info("Profiling enabled. Traces will be saved to: %s",
+                        torch_profiler_trace_dir)
+            self.profiler = torch.profiler.profile(
+                activities=[
+                    torch.profiler.ProfilerActivity.CPU,
+                    torch.profiler.ProfilerActivity.CUDA,
+                ],
+                with_stack=True,
+                on_trace_ready=torch.profiler.tensorboard_trace_handler(
+                    torch_profiler_trace_dir, use_gzip=True))
+        else:
+            self.profiler = None
+
+    def start_profile(self):
+        if self.profiler is None:
+            raise RuntimeError("Profiler is not enabled.")
+        self.profiler.start()
+
+    def stop_profile(self):
+        if self.profiler is None:
+            raise RuntimeError("Profiler is not enabled.")
+        self.profiler.stop()
+        print(
+            self.profiler.key_averages().table(sort_by="self_cuda_time_total"))
+
+    def sleep(self, level: int = 1) -> None:
+        free_bytes_before_sleep = torch.cuda.mem_get_info()[0]
+
+        # Save the buffers before level 2 sleep
+        if level == 2:
+            model = self.model_runner.model
+            self._sleep_saved_buffers = {
+                name: buffer.cpu().clone()
+                for name, buffer in model.named_buffers()
+            }
+
+        allocator = CuMemAllocator.get_instance()
+        allocator.sleep(offload_tags=("weights", ) if level == 1 else tuple())
+        free_bytes_after_sleep, total = torch.cuda.mem_get_info()
+        freed_bytes = free_bytes_after_sleep - free_bytes_before_sleep
+        used_bytes = total - free_bytes_after_sleep
+        assert freed_bytes >= 0, "Memory usage increased after sleeping."
+        logger.info(
+            "Sleep mode freed %.2f GiB memory, "
+            "%.2f GiB memory is still in use.", freed_bytes / GiB_bytes,
+            used_bytes / GiB_bytes)
+
+    def wake_up(self, tags: Optional[list[str]] = None) -> None:
+        allocator = CuMemAllocator.get_instance()
+        allocator.wake_up(tags=tags)
+
+        # Restore the buffers after level 2 sleep
+        if len(self._sleep_saved_buffers):
+            model = self.model_runner.model
+            for name, buffer in model.named_buffers():
+                if name in self._sleep_saved_buffers:
+                    buffer.data.copy_(self._sleep_saved_buffers[name].data)
+            self._sleep_saved_buffers = {}
+
+    def init_device(self) -> None:
+        if self.device_config.device.type == "cuda":
+            # torch.distributed.all_reduce does not free the input tensor until
+            # the synchronization point. This causes the memory usage to grow
+            # as the number of all_reduce calls increases. This env var disables
+            # this behavior.
+            # Related issue:
+            # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573
+            os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+
+            # This env var set by Ray causes exceptions with graph building.
+            os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
+            self.device = torch.device(f"cuda:{self.local_rank}")
+            torch.cuda.set_device(self.device)
+
+            _check_if_gpu_supports_dtype(self.model_config.dtype)
+            gc.collect()
+            torch.cuda.empty_cache()
+            torch.cuda.reset_peak_memory_stats()
+            self.baseline_snapshot = MemorySnapshot()
+        else:
+            raise RuntimeError(
+                f"Not support device type: {self.device_config.device}")
+        # Initialize the distributed environment.
+        init_worker_distributed_environment(self.vllm_config, self.rank,
+                                            self.distributed_init_method,
+                                            self.local_rank)
+        # Set random seed.
+        set_random_seed(self.model_config.seed)
+
+    def load_model(self):
+        if self.vllm_config.model_config.enable_sleep_mode:
+            allocator = CuMemAllocator.get_instance()
+            assert allocator.get_current_usage() == 0, (
+                "Sleep mode can only be "
+                "used for one instance per process.")
+            context = allocator.use_memory_pool(tag="weights")
+        else:
+            from contextlib import nullcontext
+            context = nullcontext()
+        with context:
+            self.model_runner.load_model()
+
+    def save_sharded_state(
+        self,
+        path: str,
+        pattern: Optional[str] = None,
+        max_size: Optional[int] = None,
+    ) -> None:
+        self.model_runner.save_sharded_state(
+            path,
+            pattern=pattern,
+            max_size=max_size,
+        )
+
+    def save_tensorized_model(
+        self,
+        tensorizer_config: TensorizerConfig,
+    ) -> None:
+        self.model_runner.save_tensorized_model(
+            tensorizer_config=tensorizer_config, )
+
+    @torch.inference_mode()
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
+        """Profiles the peak memory usage of the model to determine how many
+        KV blocks may be allocated without OOMs.
+
+        The engine will first conduct a profiling of the existing memory usage.
+        Then, it calculate the maximum possible number of GPU and CPU blocks
+        that can be allocated with the remaining free memory.
+
+        Tip:
+            You may limit the usage of GPU memory
+            by adjusting the `gpu_memory_utilization` parameter.
+        """
+        # Profile the memory usage of the model and get the maximum number of
+        # cache blocks that can be allocated with the remaining free memory.
+        torch.cuda.empty_cache()
+        torch.cuda.reset_peak_memory_stats()
+
+        free_memory_pre_profile, total_gpu_memory = torch.cuda.mem_get_info()
+
+        # Execute a forward pass with dummy inputs to profile the memory usage
+        # of the model.
+        with memory_profiling(
+                self.baseline_snapshot,
+                weights_memory=self.model_runner.model_memory_usage) as result:
+            self.model_runner.profile_run()
+
+        self._assert_memory_footprint_increased_during_profiling()
+
+        memory_for_current_instance = total_gpu_memory * \
+            self.cache_config.gpu_memory_utilization
+        available_kv_cache_memory = (memory_for_current_instance -
+                                     result.non_kv_cache_memory)
+
+        # Calculate the number of blocks that can be allocated with the
+        # profiled peak memory.
+        cache_block_size = self.get_cache_block_size_bytes()
+        if cache_block_size == 0:
+            num_gpu_blocks = 0
+            num_cpu_blocks = 0
+        else:
+            num_gpu_blocks = int(available_kv_cache_memory // cache_block_size)
+            num_cpu_blocks = int(self.cache_config.swap_space_bytes //
+                                 cache_block_size)
+        num_gpu_blocks = max(num_gpu_blocks, 0)
+        num_cpu_blocks = max(num_cpu_blocks, 0)
+
+        msg = (f"Memory profiling takes {result.profile_time:.2f} seconds\n"
+               "the current vLLM instance can use "
+               "total_gpu_memory "
+               f"({(total_gpu_memory / GiB_bytes):.2f}GiB)"
+               " x gpu_memory_utilization "
+               f"({self.cache_config.gpu_memory_utilization:.2f})"
+               f" = {(memory_for_current_instance / GiB_bytes):.2f}GiB\n"
+               "model weights take "
+               f"{(result.weights_memory / GiB_bytes):.2f}GiB;"
+               " non_torch_memory takes "
+               f"{(result.non_torch_increase / GiB_bytes):.2f}GiB;"
+               " PyTorch activation peak memory takes "
+               f"{(result.torch_peak_increase / GiB_bytes):.2f}GiB;"
+               " the rest of the memory reserved for KV Cache is "
+               f"{(available_kv_cache_memory / GiB_bytes):.2f}GiB.")
+
+        logger.info(msg)
+        # Final cleanup
+        gc.collect()
+
+        return num_gpu_blocks, num_cpu_blocks
+
+    def _assert_memory_footprint_increased_during_profiling(self):
+        # NOTE(woosuk): Here we assume that the other processes using the same
+        # GPU did not change their memory usage during the profiling.
+        free_gpu_memory, total = torch.cuda.mem_get_info()
+        cuda_memory = total - free_gpu_memory
+        assert self.baseline_snapshot.cuda_memory < cuda_memory, (
+            "Error in memory profiling. "
+            f"Initial used memory {self.baseline_snapshot.cuda_memory}, "
+            f"currently used memory {cuda_memory}. "
+            f"This happens when the GPU memory was "
+            "not properly cleaned up before initializing the vLLM instance.")
+
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Allocate GPU and CPU KV cache with the specified number of blocks.
+
+        This also warms up the model, which may record CUDA graphs.
+        """
+        raise_if_cache_size_invalid(
+            num_gpu_blocks, self.cache_config.block_size,
+            self.cache_config.is_attention_free,
+            self.model_config.max_model_len,
+            self.parallel_config.pipeline_parallel_size)
+
+        self.cache_config.num_gpu_blocks = num_gpu_blocks
+        self.cache_config.num_cpu_blocks = num_cpu_blocks
+
+        if self.vllm_config.model_config.enable_sleep_mode:
+            allocator = CuMemAllocator.get_instance()
+            context = allocator.use_memory_pool(tag="kv_cache")
+        else:
+            from contextlib import nullcontext
+            context = nullcontext()
+        with context:
+            self._init_cache_engine()
+        self._warm_up_model()
+
+    def _init_cache_engine(self):
+        assert self.cache_config.num_gpu_blocks is not None
+        self.cache_engine = [
+            CacheEngine(self.cache_config, self.model_config,
+                        self.parallel_config, self.device_config)
+            for _ in range(self.parallel_config.pipeline_parallel_size)
+        ]
+        self.gpu_cache = [
+            self.cache_engine[ve].gpu_cache
+            for ve in range(self.parallel_config.pipeline_parallel_size)
+        ]
+
+        # Layer pairings for cross-layer KV sharing.
+        # If an Attention layer `layer_name` is in the keys of this dict, it
+        # means this layer will perform attention using the keys and values
+        # from the KV cache of `shared_kv_cache_layers[layer_name]`.
+        shared_kv_cache_layers: dict[str, str] = {}
+
+        attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
+
+        for layer_name, attn_module in attn_layers.items():
+            if (kv_tgt_layer :=
+                    attn_module.kv_sharing_target_layer_name) is not None:
+                # The layer doesn't need its own KV cache and will use that of
+                # the target layer. We skip creating a KVCacheSpec for it, so
+                # that KV cache management logic will act as this layer does
+                # not exist, and doesn't allocate KV cache for the layer. This
+                # enables the memory saving of cross-layer kv sharing, allowing
+                # a given amount of memory to accommodate longer context lengths
+                # or enable more requests to be processed simultaneously.
+                shared_kv_cache_layers[layer_name] = kv_tgt_layer
+
+        bind_kv_cache(self.compilation_config.static_forward_context,
+                      self.gpu_cache, shared_kv_cache_layers)
+
+    def _warm_up_model(self) -> None:
+        # warm up sizes that are not in cudagraph capture sizes,
+        # but users still want to compile for better performance,
+        # e.g. for the max-num-batched token size in chunked prefill.
+        warmup_sizes = self.vllm_config.compilation_config.compile_sizes.copy()
+        if not self.model_config.enforce_eager:
+            warmup_sizes = [
+                x for x in warmup_sizes if x not in
+                self.vllm_config.compilation_config.cudagraph_capture_sizes
+            ]
+        for size in sorted(warmup_sizes, reverse=True):
+            logger.info("Compile and warming up model for size %d", size)
+            self.model_runner._dummy_run(size)
+        if not self.model_config.enforce_eager:
+            self.model_runner.capture_model(self.gpu_cache)
+        # Reset the seed to ensure that the random state is not affected by
+        # the model initialization and profiling.
+        set_random_seed(self.model_config.seed)
+
+    @property
+    def do_metadata_broadcast(self) -> bool:
+        return self.parallel_config.tensor_parallel_size > 1
+
+    @property
+    def kv_cache(self) -> Optional[List[List[torch.Tensor]]]:
+        return self.gpu_cache
+
+    @torch.inference_mode()
+    def prepare_worker_input(
+            self, execute_model_req: ExecuteModelRequest) -> WorkerInput:
+        virtual_engine = execute_model_req.virtual_engine
+        num_steps = execute_model_req.num_steps
+        num_seq_groups = len(execute_model_req.seq_group_metadata_list)
+        # `blocks_to_swap_in` and `blocks_to_swap_out` are cpu tensors.
+        # they contain parameters to launch cudamemcpyasync.
+        blocks_to_swap_in = torch.tensor(execute_model_req.blocks_to_swap_in,
+                                         device="cpu",
+                                         dtype=torch.int64).view(-1, 2)
+        blocks_to_swap_out = torch.tensor(execute_model_req.blocks_to_swap_out,
+                                          device="cpu",
+                                          dtype=torch.int64).view(-1, 2)
+        # `blocks_to_copy` is a gpu tensor. The src and tgt of
+        # blocks to copy are in the same device, and `blocks_to_copy`
+        # can be used directly within cuda kernels.
+        blocks_to_copy = torch.tensor(execute_model_req.blocks_to_copy,
+                                      device=self.device,
+                                      dtype=torch.int64).view(-1, 2)
+
+        return WorkerInput(
+            num_seq_groups=num_seq_groups,
+            blocks_to_swap_in=blocks_to_swap_in,
+            blocks_to_swap_out=blocks_to_swap_out,
+            blocks_to_copy=blocks_to_copy,
+            virtual_engine=virtual_engine,
+            num_steps=num_steps,
+        )
+
+    @torch.inference_mode()
+    def execute_worker(self, worker_input: WorkerInput) -> None:
+        virtual_engine = worker_input.virtual_engine
+        # Issue cache operations.
+        if (worker_input.blocks_to_swap_in is not None
+                and worker_input.blocks_to_swap_in.numel() > 0):
+            self.cache_engine[virtual_engine].swap_in(
+                worker_input.blocks_to_swap_in)
+        if (worker_input.blocks_to_swap_out is not None
+                and worker_input.blocks_to_swap_out.numel() > 0):
+            self.cache_engine[virtual_engine].swap_out(
+                worker_input.blocks_to_swap_out)
+        if (worker_input.blocks_to_copy is not None
+                and worker_input.blocks_to_copy.numel() > 0):
+            self.cache_engine[virtual_engine].copy(worker_input.blocks_to_copy)
+
+    def _get_cached_seq_group_metadata(
+            self,
+            seq_group_metadata_list: List[Union[SequenceGroupMetadata,
+                                                SequenceGroupMetadataDelta]],
+            finished_request_ids: List[str]) -> List[SequenceGroupMetadata]:
+        """Return a list of cached Sequence Group Metadata after updating its
+        state.
+
+        It is used because scheduler only sends delta to workers to reduce
+        the data payload size. The function also cleans up cache based on
+        a given `finished_request_ids`.
+        """
+        new_seq_group_metadata_list = []
+        for metadata_or_delta in seq_group_metadata_list:
+            request_id = metadata_or_delta.request_id
+            if request_id not in self._seq_group_metadata_cache:
+                # The first prefill.
+                assert isinstance(metadata_or_delta, SequenceGroupMetadata)
+                self._seq_group_metadata_cache[request_id] = metadata_or_delta
+            else:
+                # The first prefill is already cached.
+                if isinstance(metadata_or_delta, SequenceGroupMetadataDelta):
+                    self._seq_group_metadata_cache[request_id].apply_delta(
+                        metadata_or_delta)
+                else:
+                    # If metadata snapshot is sent again, it is
+                    # preempted. Reset the cache because we need to start
+                    # from scratch.
+                    assert isinstance(metadata_or_delta, SequenceGroupMetadata)
+                    self._seq_group_metadata_cache[
+                        request_id] = metadata_or_delta
+
+            new_seq_group_metadata_list.append(
+                self._seq_group_metadata_cache[request_id])
+
+        # Clean up finished ids
+        for finished_id in finished_request_ids:
+            del self._seq_group_metadata_cache[finished_id]
+
+        return new_seq_group_metadata_list
+
+    def _execute_model_spmd(
+        self,
+        execute_model_req: ExecuteModelRequest,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+    ) -> Optional[List[SamplerOutput]]:
+        if execute_model_req is not None:
+            new_seq_group_metadata_list = self._get_cached_seq_group_metadata(
+                execute_model_req.seq_group_metadata_list,
+                execute_model_req.finished_requests_ids)
+
+            execute_model_req.seq_group_metadata_list = (
+                new_seq_group_metadata_list)
+        output = super()._execute_model_spmd(execute_model_req,
+                                             intermediate_tensors)
+        return output
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        return self.model_runner.add_lora(lora_request)
+
+    def remove_lora(self, lora_id: int) -> bool:
+        return self.model_runner.remove_lora(lora_id)
+
+    def pin_lora(self, lora_id: int) -> bool:
+        return self.model_runner.pin_lora(lora_id)
+
+    def list_loras(self) -> Set[int]:
+        return self.model_runner.list_loras()
+
+    @property
+    def max_model_len(self) -> int:
+        return self.model_config.max_model_len
+
+    @property
+    def vocab_size(self) -> int:
+        return self.model_runner.vocab_size
+
+    def get_cache_block_size_bytes(self) -> int:
+        """Get the size of the KV cache block size in bytes.
+        """
+        return CacheEngine.get_cache_block_size(self.cache_config,
+                                                self.model_config,
+                                                self.parallel_config)
+
+
+def init_worker_distributed_environment(
+    vllm_config: VllmConfig,
+    rank: int,
+    distributed_init_method: Optional[str] = None,
+    local_rank: int = -1,
+) -> None:
+    """Initialize the distributed environment."""
+    parallel_config = vllm_config.parallel_config
+    set_custom_all_reduce(not parallel_config.disable_custom_all_reduce)
+
+    init_distributed_environment(parallel_config.world_size, rank,
+                                 distributed_init_method, local_rank,
+                                 current_platform.dist_backend)
+    ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
+                                      parallel_config.pipeline_parallel_size)
+
+    ensure_kv_transfer_initialized(vllm_config)
+
+
+def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
+    # Check if the GPU supports the dtype.
+    if torch_dtype == torch.bfloat16:  # noqa: SIM102
+        if not current_platform.has_device_capability(80):
+            capability = current_platform.get_device_capability()
+            gpu_name = current_platform.get_device_name()
+
+            if capability is None:
+                compute_str = "does not have a compute capability"
+            else:
+                version_str = capability.as_version_str()
+                compute_str = f"has compute capability {version_str}"
+
+            raise ValueError(
+                "Bfloat16 is only supported on GPUs with compute capability "
+                f"of at least 8.0. Your {gpu_name} GPU {compute_str}. "
+                "You can use float16 instead by explicitly setting the "
+                "`dtype` flag in CLI, for example: --dtype=half.")
+
+
+def raise_if_cache_size_invalid(num_gpu_blocks, block_size, is_attention_free,
+                                max_model_len, pipeline_parallel_size) -> None:
+    if is_attention_free and num_gpu_blocks != 0:
+        raise ValueError("No memory should be allocated for the cache blocks "
+                         f"for an attention-free model, but {num_gpu_blocks} "
+                         "blocks are allocated.")
+    if not is_attention_free and num_gpu_blocks <= 0:
+        raise ValueError("No available memory for the cache blocks. "
+                         "Try increasing `gpu_memory_utilization` when "
+                         "initializing the engine.")
+    max_seq_len = block_size * (num_gpu_blocks // pipeline_parallel_size)
+    if not is_attention_free and max_model_len > max_seq_len:
+        raise ValueError(
+            f"The model's max seq len ({max_model_len}) "
+            "is larger than the maximum number of tokens that can be "
+            f"stored in KV cache ({max_seq_len}). Try increasing "
+            "`gpu_memory_utilization` or decreasing `max_model_len` when "
+            "initializing the engine.")