File size: 56,067 Bytes
b025706 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 | # SPDX-FileCopyrightText: © 2024 Tenstorrent USA, Inc.
# SPDX-License-Identifier: Apache-2.0
from types import SimpleNamespace
from typing import List, Mapping, Union
import torch
from loguru import logger
from PIL.Image import Image
from tqdm import tqdm
from vllm.model_executor.models.gemma3_mm import (
Gemma3DummyInputsBuilder,
Gemma3MultiModalProcessor,
Gemma3ProcessingInfo,
)
from vllm.model_executor.models.interfaces import SupportsMultiModal
from vllm.model_executor.models.mistral3 import (
Mistral3DummyInputsBuilder,
Mistral3MultiModalProcessor,
Mistral3ProcessingInfo,
)
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.processing import BaseDummyInputsBuilder
try:
# vLLM >= 0.24.0 exposes MultiModalDataDict from vllm.inputs; older
# versions export it from vllm.multimodal.inputs.
from vllm.inputs import MultiModalDataDict
except ImportError:
from vllm.multimodal.inputs import MultiModalDataDict
import ttnn
from models.common.llama_models import create_vision_mask
from models.common.utility_functions import is_wormhole_b0, nearest_32
from models.tt_transformers.tt.generator import Generator, create_submeshes
from models.tt_transformers.tt.model import Transformer
from models.tt_transformers.tt.model_config import DecodersPrecision, ModelArgs, TensorGroup
def allocate_vllm_kv_cache_per_layer(per_layer_specs, dp_model: List[Transformer], tt_cache_path):
"""Allocate KV cache tensors with optional cross-layer DRAM sharing.
Args:
per_layer_specs: list of ``(kv_cache_shape, dtype, tensor_idx)``
triples, one per layer in model layer-index order. Layers with
the same ``tensor_idx`` share one underlying TT tensor — this
is upstream's HMA tensor-sharing layout (e.g. for Gemma3 5:1,
one full-attention layer and several sliding-window layers
collapse to a single DRAM buffer; per-group block tables keep
their slot accesses disjoint at runtime). Layers with unique
``tensor_idx`` get their own buffer.
dp_model: list of replicated TT model handles, one per data-parallel
submesh.
tt_cache_path: path used for on-disk weight cache file naming.
Returns:
``list[submesh][layer_idx][k_or_v]`` of TT tensors. Multiple
``layer_idx`` entries may refer to the same underlying tensor
objects when they share a ``tensor_idx``.
"""
submesh_devices = [model.mesh_device for model in dp_model]
kv_cache = []
for mesh_idx, submesh in enumerate(submesh_devices):
# tensor_idx -> [k, v] ttnn handles; reused across all layers that
# share a buffer.
unique_buffers: dict[int, list] = {}
kv_tt = []
for layer_num, (kv_cache_shape, dtype, tensor_idx) in enumerate(
tqdm(per_layer_specs, desc=f"Allocating TT kv caches for each layer (submesh {mesh_idx+1})")
):
existing = unique_buffers.get(tensor_idx)
if existing is not None:
kv_tt.append(existing)
continue
cache_kv = torch.zeros(kv_cache_shape, dtype=dtype)
# Get the dtype for the kv cache based on the configured optimizations in the model
if dp_model[mesh_idx].args.optimizations is not None:
kv_cache_dtype = dp_model[mesh_idx].args.optimizations.get_tensor_dtype(
decoder_id=layer_num, tensor=TensorGroup.KV_CACHE
)
else:
logger.info("No dtype specified for the model KV cache - defaulting to ttnn.bfloat8_b.")
kv_cache_dtype = None
# Set default to bfloat8_b when no optimizations are configured
kv_cache_dtype = ttnn.bfloat8_b if kv_cache_dtype is None else kv_cache_dtype
kv_tt_i = [
ttnn.as_tensor(
cache_kv,
device=submesh,
# TODO: this could be ShardTensorToMesh, removing the need for vLLM to know about TP for num_kv_heads.
# Could affect other calculations which use TTCacheEngine.num_kv_heads, though.
mesh_mapper=ttnn.ReplicateTensorToMesh(submesh),
layout=ttnn.TILE_LAYOUT,
memory_config=ttnn.DRAM_MEMORY_CONFIG,
dtype=kv_cache_dtype,
# Separate cache files for K and V to avoid collision.
# ``tensor_idx`` distinguishes shared buffers that have the
# same shape but back different layer subsets.
cache_file_name=tt_cache_path / f"empty_{kv}cache_paged_attention{kv_cache_shape}_t{tensor_idx}",
)
for kv in ["k", "v"]
]
unique_buffers[tensor_idx] = kv_tt_i
kv_tt.append(kv_tt_i)
kv_cache.append(kv_tt)
return kv_cache
def allocate_vllm_kv_cache(kv_cache_shape, dtype, num_layers, dp_model: List[Transformer], tt_cache_path):
"""Uniform-shape KV cache allocator for non-hybrid models.
Hybrid attention models should use :func:`allocate_vllm_kv_cache_per_layer`,
which takes a per-layer ``(shape, dtype, tensor_idx)`` list so layers
can share DRAM buffers per upstream's HMA tensor-sharing model.
"""
return allocate_vllm_kv_cache_per_layer(
[(kv_cache_shape, dtype, i) for i in range(num_layers)],
dp_model=dp_model,
tt_cache_path=tt_cache_path,
)
class HybridAttentionForCausalLM(Generator):
"""vLLM wrapper base for hybrid attention models.
Models with mixed sliding-window + full-attention layers (Gemma3,
Gemma4, GPT-OSS, ...) inherit from this class instead of plain
:class:`Generator` so they can opt in to upstream's hybrid kv cache
manager. The shared ``get_kv_cache_spec`` classmethod here builds
the per-layer KV cache spec from ``hf_config.text_config.layer_types``
— the standard HF convention used by all of these models — emitting
``SlidingWindowSpec`` for sliding layers and ``FullAttentionSpec``
for full-attention layers.
Subclasses are responsible for the model-specific pieces:
* ``initialize_vllm_model``: load the underlying TT model.
* ``prefill_forward`` / ``decode_forward``: consume the
``page_tables_per_layer`` list (one tensor per decoder layer, layer-
aligned with the model's ``self.layers``) and pass each entry to its
corresponding attention layer. The plugin pre-expands
``block_tables_per_group`` into this per-layer view at submission
time so bridges don't have to re-derive vLLM's group construction
order — see ``TTModelRunner._block_tables_per_layer``.
* ``allocate_kv_cache_per_layer``: typically just delegates to
:func:`allocate_vllm_kv_cache_per_layer` with the model handles.
Until a subclass overrides them, ``prefill_forward`` and
``decode_forward`` raise :class:`NotImplementedError` to make the
contract explicit. Legacy (non-hybrid) models never see the
``page_tables_per_layer`` kwarg — vLLM's plugin opts in via the
presence of ``get_kv_cache_spec`` on the model class.
"""
# Keep this in sync with get_kv_cache_spec below and with the TT vLLM
# worker's token-budget calculation. While SlidingWindowSpec is disabled,
# vLLM produces one full-attention KV group and the legacy single page_table
# path is sufficient. When SlidingWindowSpec is restored, flip this back so
# warmup also exercises the per-layer persistent page-table path.
_HYBRID_KV_CACHE_GROUPS_ENABLED = False
@classmethod
def get_kv_cache_spec(cls, vllm_config):
"""Build per-layer KVCacheSpec from HF config ``layer_types``.
Returns a dict keyed by ``model.layers.<idx>.self_attn`` (the
upstream attention-layer naming convention vLLM's KVCacheGroup
machinery understands) so :func:`_parse_layer_index` on the
runner side can map each spec back to its model layer index.
"""
from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
# SlidingWindowSpec import intentionally dropped; restore alongside the
# branch below when re-enabling kv cache groups.
from vllm.v1.kv_cache_interface import FullAttentionSpec
model_config = vllm_config.model_config
cache_config = vllm_config.cache_config
hf_config = model_config.hf_config
text_config = getattr(hf_config, "text_config", hf_config)
layer_types = getattr(text_config, "layer_types", None)
if layer_types is None:
raise ValueError(
f"{cls.__name__}.get_kv_cache_spec requires "
"hf_config.text_config.layer_types (one of 'full_attention' / "
"'sliding_attention' per layer); none found on this model"
)
num_kv_heads = model_config.get_num_kv_heads(vllm_config.parallel_config)
head_size = model_config.get_head_size()
dtype = (
model_config.dtype
if cache_config.cache_dtype == "auto"
else STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
)
block_size = cache_config.block_size
common = dict(
block_size=block_size,
num_kv_heads=num_kv_heads,
head_size=head_size,
dtype=dtype,
)
# SlidingWindowSpec is temporarily disabled: TT-side decode passes the
# absolute position to paged_update_cache / paged_sdpa_decode, but vLLM
# zero-pads the sliding group's page_table past sliding_window/block_size
# entries, so positions beyond the sliding window collapse onto physical
# block 0 and silently corrupt the cache. Emit FullAttentionSpec for every
# layer so vLLM allocates a max_model_len cache per layer; the SDPA op's
# own sliding_window_size kwarg still trims attention correctly on the
# read side.
spec_per_layer = {}
for i, lt in enumerate(layer_types):
name = f"model.layers.{i}.self_attn"
if lt not in ("sliding_attention", "full_attention"):
raise ValueError(
f"Unsupported layer_type {lt!r} at layer {i} on "
f"{cls.__name__}; expected 'full_attention' or "
"'sliding_attention'"
)
spec_per_layer[name] = FullAttentionSpec(**common)
return spec_per_layer
def prefill_forward(self, *args, **kwargs):
raise NotImplementedError(
f"{type(self).__name__} must override prefill_forward to consume "
"`page_tables_per_layer` and pass each entry to the matching "
"attention layer."
)
def decode_forward(self, *args, **kwargs):
raise NotImplementedError(
f"{type(self).__name__} must override decode_forward to consume "
"`page_tables_per_layer` and pass each entry to the matching "
"attention layer."
)
def allocate_kv_cache_per_layer(self, per_layer_specs):
return allocate_vllm_kv_cache_per_layer(per_layer_specs, dp_model=self.model, tt_cache_path=self.cache_path)
@staticmethod
def _reload_per_layer_page_tables(kwargs) -> bool:
"""Whether the explicit contract requests per-layer page-table upload."""
return bool(kwargs["reload_inputs"] or kwargs["reload_page_table"])
def _ensure_page_tables_per_layer(self, page_tables_per_layer, page_table):
"""When invoked outside the vLLM hybrid plugin (e.g. by warmup
which only knows about the legacy single ``page_table``), optionally
broadcast the single page table to a per-layer list.
Broadcasting is only correct while hybrid KV cache groups are enabled:
trace capture then needs to exercise the per-layer code path inside
``Transformer.forward`` so replay reads the persistent per-layer device
tensors updated before each call. While hybrid groups are temporarily
disabled, all layers use one full-attention KV group, so we intentionally
keep warmup/runtime on the legacy single-page-table path.
"""
if page_tables_per_layer is not None or page_table is None or not self._HYBRID_KV_CACHE_GROUPS_ENABLED:
return page_tables_per_layer
# Broadcast the same torch tensor across every layer in every
# submesh — content is identical, persistent allocation gives each
# layer its own device tensor at a stable address.
num_layers = len(self.model[0].layers)
return [page_table] * num_layers
def _chunk_page_tables_per_dp(self, page_tables_per_layer):
"""Split a global per-layer list along DP into one per-layer list
per submesh.
The plugin pads each per-layer table to the global
``(max_num_seqs * data_parallel, max_num_blocks_per_req)`` shape
(see ``TTModelRunner._block_tables_per_layer``); warmup likewise
builds a global-batch tensor. ``Generator.decode_forward`` already
does ``torch.chunk(page_table, self.data_parallel, 0)`` for the
legacy single-page-table path before the per-submesh
``prepare_inputs_decode``; the hybrid bridge has to do the
equivalent so each submesh's ``_page_tables_to_ttnn`` receives a
per-DP slice whose batch dim matches the submesh's K/V tensors.
Without this, ``paged_update_cache`` asserts a batch-size mismatch
on multi-DP runs.
"""
if page_tables_per_layer is None:
return None
dp = self.data_parallel
if dp <= 1:
return [page_tables_per_layer]
per_submesh = [list() for _ in range(dp)]
for pt in page_tables_per_layer:
if pt is None or isinstance(pt, ttnn.Tensor):
# Already-resolved or absent entries pass through unchanged
# to every submesh — chunking only applies to torch tensors
# carrying global batch.
for s in per_submesh:
s.append(pt)
continue
chunks = torch.chunk(pt, dp, dim=0)
for s, c in zip(per_submesh, chunks):
s.append(c)
return per_submesh
def _route_per_layer_page_tables(self, per_submesh_page_tables):
"""Stash each submesh's per-layer page-table list on its model
handle for the duration of a forward call.
``Generator``'s prefill/decode paths invoke
``model[i].ttnn_prefill_forward`` / ``ttnn_decode_forward`` from
many sites (warmup, trace capture, traced replay, etc.) without
forwarding an arbitrary kwarg. Threading the per-layer list through
every site would be a wide change for a feature only this hybrid
bridge consumes, so we use a localised attribute injection: each
model reads ``getattr(self, "_active_page_tables_per_layer", None)``
when its own kwarg is None. ``per_submesh_page_tables[i]`` is the
per-layer slice that submesh ``i`` should see; ``None`` clears the
stash entirely (legacy fallback).
"""
class _Stash:
def __init__(self, models, per_submesh):
self._models = models
self._per_submesh = per_submesh
def __enter__(self):
if self._per_submesh is None:
return
for m, value in zip(self._models, self._per_submesh):
m._active_page_tables_per_layer = value
def __exit__(self, *_):
if self._per_submesh is None:
return
for m in self._models:
if hasattr(m, "_active_page_tables_per_layer"):
del m._active_page_tables_per_layer
return _Stash(self.model, per_submesh_page_tables)
def initialize_vllm_text_transformer(
hf_config,
tt_data_parallel,
mesh_device,
max_batch_size,
max_seq_len,
n_layers=None,
dtype=ttnn.bfloat8_b,
optimizations=DecodersPrecision.performance,
):
submesh_devices = create_submeshes(mesh_device, tt_data_parallel)
# Load model args, weights
model_args = []
for submesh in submesh_devices:
model_args_i = ModelArgs(
submesh,
instruct=(
"Instruct" in hf_config._name_or_path or "DeepSeek-R1-Distill-Llama-70B" in hf_config._name_or_path
),
max_batch_size=max_batch_size // tt_data_parallel,
optimizations=lambda model_args: optimizations(model_args.n_layers, model_args.model_name),
max_seq_len=max_seq_len,
)
assert model_args_i.model_name.replace("-", "") in hf_config._name_or_path.replace(
"-", ""
), f"The model specified in vLLM ({hf_config._name_or_path}) does not match the model name ({model_args_i.model_name}) with model weights ({model_args_i.CKPT_DIR})."
if n_layers is not None:
model_args_i.n_layers = n_layers
model_args.append(model_args_i)
state_dict = model_args[0].load_state_dict()
tt_model = []
for i, submesh in enumerate(submesh_devices):
tt_model_i = Transformer(
args=model_args[i],
mesh_device=submesh,
dtype=dtype,
state_dict=state_dict,
weight_cache_path=model_args[i].weight_cache_path(dtype),
use_paged_kv_cache=True,
)
tt_model.append(tt_model_i)
return tt_model, model_args
class DummyInputsBuilder(BaseDummyInputsBuilder):
"""
We don't need to implement a dummy input builder since we don't do profiling in vLLM.
Create callable class just for processor registration.
"""
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
raise NotImplementedError
def get_dummy_mm_data(
self,
seq_len: int,
mm_counts: Mapping[str, int],
) -> MultiModalDataDict:
raise NotImplementedError
class CustomNamespace(SimpleNamespace):
def __contains__(self, key):
return key in self.__dict__
@MULTIMODAL_REGISTRY.register_processor(
Mistral3MultiModalProcessor,
info=Mistral3ProcessingInfo,
dummy_inputs=Mistral3DummyInputsBuilder,
)
class Mistral3ForConditionalGeneration(Generator, SupportsMultiModal):
decode_input_update_contract = 1
model_capabilities = {
"supports_prefix_caching": False,
"supports_sample_on_device": True,
"max_device_top_k": 32,
}
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.MISTRAL_IMAGE_TOKEN_ID = 151655
self.max_gen_len = self.model_args[0].max_seq_len - 1
@classmethod
def initialize_vllm_model(
cls,
hf_config,
mesh_device,
max_batch_size,
max_seq_len=131072,
tt_data_parallel=1,
optimizations: str = None,
):
assert optimizations is None, "Custom optimizations are not supported for this model"
from models.tt_transformers.demo.simple_vision_demo import create_multimodal_model
max_seq_len = 1024 * 128
submesh_devices = create_submeshes(mesh_device, tt_data_parallel)
model_args = []
model = []
state_dict = None
for submesh in submesh_devices:
model_args_i, model_i, state_dict = create_multimodal_model(
mesh_device=submesh,
max_batch_size=max_batch_size // tt_data_parallel,
max_seq_len=max_seq_len,
use_paged_kv_cache=True,
checkpoint=state_dict,
)
model_args.append(model_args_i)
model.append(model_i)
return cls(model, model_args, mesh_device)
@property
def cache_path(self):
return self.model_args[0].model_cache_path
def prefill_forward(self, *args, **kwargs):
self.tokenizer = self.model_args[0].tokenizer
pad_token_id = self.tokenizer.pad_token_id
tokens = kwargs["tokens"]
prompt_lens = kwargs["prompt_lens"]
inputs = CustomNamespace()
inputs.input_ids = tokens
data = kwargs.get("images", None)
for i in range(tokens.shape[0]):
tokens[i][prompt_lens[i] :] = pad_token_id
pixel_values, image_sizes = None, None
if data and hasattr(data[0], "pixel_values"):
pixel_values = [im.pixel_values for im in data if hasattr(im, "pixel_values")]
image_sizes = [im.image_sizes for im in data if hasattr(im, "image_sizes")]
page_table = kwargs.get("page_table", None)
kv_cache = kwargs.get("kv_cache", None)
return super().prefill_forward_text(
tokens=inputs.input_ids,
page_table=page_table,
kv_cache=kv_cache,
prompt_lens=prompt_lens,
pixel_values=pixel_values if pixel_values else None,
image_sizes=image_sizes if image_sizes else None,
)
def decode_forward(self, *args, **kwargs):
return super().decode_forward(*args, **kwargs)
def allocate_kv_cache(self, *args, **kwargs):
return allocate_vllm_kv_cache(*args, **kwargs, dp_model=self.model, tt_cache_path=self.cache_path)
# Mllama is currently not supported in vLLM V1.
# TODO: Remove or re-enable when Mllama is supported in vLLM V1.
# @MULTIMODAL_REGISTRY.register_processor(
# MllamaMultiModalProcessor, info=TT_MllamaProcessingInfo, dummy_inputs=DummyInputsBuilder
# )
class MllamaForConditionalGeneration(Generator, SupportsMultiModal):
decode_input_update_contract = 1
# Class-level capabilities
# Note: Mllama doesn't support prefix caching (it's V0 only)
# decode_forward calls decode_forward_llama_vision and discards anything
# but logits, so sampling_params never reach a sampler — explicitly
# declare on-device sampling unsupported.
model_capabilities = {
"supports_prefix_caching": False,
"supports_async_decode": False,
"supports_sample_on_device": False,
}
@classmethod
def get_max_tokens_all_users(
cls,
model_name: str = "",
num_devices: int = 1,
tt_data_parallel: int = 1,
**kwargs,
) -> int:
"""Returns config-specific all-user KV-cache token capacity."""
devices_per_dp_cache = num_devices // tt_data_parallel
is_wormhole = is_wormhole_b0()
# Llama90B on WH T3K
if "Llama-3.2-90B" in model_name and devices_per_dp_cache == 8 and is_wormhole:
return 65_536
return super().get_max_tokens_all_users(
model_name=model_name,
num_devices=num_devices,
tt_data_parallel=tt_data_parallel,
**kwargs,
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.MLLAMA_IMAGE_TOKEN_ID = 128256
self.max_gen_len = self.model_args[0].max_seq_len - 1 # TODO: double check what this should be
@classmethod
def initialize_vllm_model(
cls, hf_config, mesh_device, max_batch_size, max_seq_len, tt_data_parallel=1, optimizations: str = None
):
assert optimizations is None, "Custom optimizations are not supported for this model"
from models.tt_transformers.demo.simple_vision_demo import create_multimodal_model
submesh_devices = create_submeshes(mesh_device, tt_data_parallel)
model_args = []
model = []
state_dict = None
for submesh in submesh_devices:
model_args_i, model_i, state_dict = create_multimodal_model(
mesh_device=submesh,
max_batch_size=max_batch_size // tt_data_parallel,
max_seq_len=max_seq_len,
use_paged_kv_cache=True,
checkpoint=state_dict,
)
model_args.append(model_args_i)
model.append(model_i)
return cls(model, model_args, mesh_device)
@property
def cache_path(self):
return self.model_args[0].model_cache_path
@property
def max_cross_attn_tokens(self):
return self.model_args[0].vision_max_num_chunks * nearest_32(self.model_args[0].vision_chunk_ntok)
def prefill_forward(
self,
tokens: torch.Tensor,
images: Union[List[Image], List[List[Image]]],
page_table: torch.Tensor,
kv_cache,
prompt_lens,
cross_page_table: torch.Tensor,
):
"""
Replaces prefill_forward from Generator with a version that supports mask creation.
"""
batch = tokens.shape[0]
vision_images = []
vision_masks = []
total_lens = []
for user_id in range(batch):
image = images[user_id]
if isinstance(image, list):
assert len(image) == 1, "Only one image is supported for each user in the batch"
image = image[0]
vision_images.append([image] if image else None)
prompt_tokens = [int(tokens[user_id, i]) for i in range(prompt_lens[user_id])]
vision_masks.append(create_vision_mask(prompt_tokens, self.MLLAMA_IMAGE_TOKEN_ID) if image else None)
total_lens.append(prompt_lens[user_id] + self.max_gen_len)
return super().prefill_forward(
vision_images,
vision_masks,
tokens,
None,
total_lens,
prompt_lens,
page_table=page_table,
kv_cache=kv_cache,
cross_page_table=cross_page_table,
)
def decode_forward(self, *args, **kwargs):
reload_inputs = kwargs.pop("reload_inputs")
reload_page_table = kwargs.pop("reload_page_table")
reload_sampling_params = kwargs.pop("reload_sampling_params")
reset_sampling_state = kwargs.pop("reset_sampling_state")
# Mllama has no persistent model state keyed by vLLM's condensed
# decode slots. Contract v1 nevertheless supplies slot_remap for every
# decode so adapters with recurrent state can consume it.
kwargs.pop("slot_remap", None)
if not reload_inputs or reload_page_table or reload_sampling_params or reset_sampling_state:
raise ValueError("Mllama requires a full host-input reload and has no " "device sampling state")
logits = super().decode_forward_llama_vision(*args, **kwargs)
if isinstance(logits, tuple):
return logits[0]
else:
return logits
def allocate_kv_cache(self, *args, **kwargs):
return allocate_vllm_kv_cache(*args, **kwargs, dp_model=self.model, tt_cache_path=self.cache_path)
class LlamaForCausalLM(Generator):
decode_input_update_contract = 1
# Class-level capabilities
model_capabilities = {
"supports_prefix_caching": True,
"supports_async_decode": True,
"supports_sample_on_device": True,
"max_device_top_k": 32,
# prefill_forward_single_user_text already routes a nonzero start_pos to
# the chunked SDPA, and the generator floors the offset to what that op
# needs, so a prompt split across engine steps needs no new prefill code.
"supports_chunked_prefill": True,
}
@classmethod
def get_max_tokens_all_users(
cls,
model_name: str = "",
num_devices: int = 1,
tt_data_parallel: int = 1,
**kwargs,
) -> int:
"""Returns config-specific all-user KV-cache token capacity."""
devices_per_dp_cache = num_devices // tt_data_parallel
is_wormhole = is_wormhole_b0()
# Llama8B on N150
if "Llama-3.1-8B" in model_name and devices_per_dp_cache == 1 and is_wormhole:
return 32_768
return super().get_max_tokens_all_users(
model_name=model_name,
num_devices=num_devices,
tt_data_parallel=tt_data_parallel,
**kwargs,
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@classmethod
def initialize_vllm_model(
cls,
hf_config,
mesh_device,
max_batch_size,
max_seq_len,
n_layers=None,
tt_data_parallel=1,
optimizations: str = "performance",
):
hf_model_name = hf_config._name_or_path
if (
("3.1-8B" in hf_model_name or "3.2-11B" in hf_model_name)
and mesh_device.get_num_devices() == 1
and is_wormhole_b0()
):
MAX_PROMPT_LEN = 32768
if max_seq_len > MAX_PROMPT_LEN:
raise ValueError(
f"TT-LLama8B and TT-Llama11B do not support max_model_len greater than {MAX_PROMPT_LEN} on N150 "
f"(received {max_seq_len}). Set --max_model_len to {MAX_PROMPT_LEN} or lower in vLLM."
)
tt_model, model_args = initialize_vllm_text_transformer(
hf_config,
tt_data_parallel,
mesh_device,
max_batch_size,
max_seq_len=max_seq_len,
n_layers=n_layers,
dtype=ttnn.bfloat8_b,
optimizations=(
DecodersPrecision.from_string(optimizations)
if optimizations is not None
else DecodersPrecision.performance
),
)
return cls(tt_model, model_args, mesh_device)
@property
def cache_path(self):
return self.model_args[0].model_cache_path
def prefill_forward(self, *args, **kwargs):
return super().prefill_forward_text(*args, **kwargs)
def decode_forward(self, *args, **kwargs):
return super().decode_forward(*args, **kwargs)
def allocate_kv_cache(self, *args, **kwargs):
return allocate_vllm_kv_cache(*args, **kwargs, dp_model=self.model, tt_cache_path=self.cache_path)
class QwenForCausalLM(Generator):
decode_input_update_contract = 1
# Class-level capabilities
model_capabilities = {
"supports_prefix_caching": True,
"supports_async_decode": True,
"supports_sample_on_device": True,
"max_device_top_k": 32,
# prefill_forward_single_user_text already routes a nonzero start_pos to
# the chunked SDPA, and the generator floors the offset to what that op
# needs, so a prompt split across engine steps needs no new prefill code.
"supports_chunked_prefill": True,
}
@classmethod
def get_max_tokens_all_users(
cls,
model_name: str = "",
num_devices: int = 1,
tt_data_parallel: int = 1,
**kwargs,
) -> int:
"""Returns config-specific all-user KV-cache token capacity."""
devices_per_dp_cache = num_devices // tt_data_parallel
is_wormhole = is_wormhole_b0()
# Qwen3-8B on N150 (same constraint as Llama8B-N150)
if "Qwen3-8B" in model_name and devices_per_dp_cache == 1 and is_wormhole:
return 32_768
# DeepSeek-R1-Distill-Qwen-14B / Qwen2.5-14B on N300
if (
("DeepSeek-R1-Distill-Qwen-14B" in model_name or "Qwen2.5-14B" in model_name)
and devices_per_dp_cache == 2
and is_wormhole
):
return 65_536
return super().get_max_tokens_all_users(
model_name=model_name,
num_devices=num_devices,
tt_data_parallel=tt_data_parallel,
**kwargs,
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@classmethod
def initialize_vllm_model(
cls,
hf_config,
mesh_device,
max_batch_size,
max_seq_len,
n_layers=None,
tt_data_parallel=1,
optimizations: str = "performance",
):
tt_model, model_args = initialize_vllm_text_transformer(
hf_config,
tt_data_parallel,
mesh_device,
max_batch_size,
max_seq_len=max_seq_len,
n_layers=n_layers,
dtype=ttnn.bfloat8_b,
optimizations=(
DecodersPrecision.from_string(optimizations)
if optimizations is not None
else DecodersPrecision.performance
),
)
return cls(tt_model, model_args, mesh_device)
@property
def cache_path(self):
return self.model_args[0].model_cache_path
def prefill_forward(self, *args, **kwargs):
return super().prefill_forward_text(*args, **kwargs)
def decode_forward(self, *args, **kwargs):
return super().decode_forward(*args, **kwargs)
def allocate_kv_cache(self, *args, **kwargs):
return allocate_vllm_kv_cache(*args, **kwargs, dp_model=self.model, tt_cache_path=self.cache_path)
class CohereForCausalLM(Generator):
"""Command-R / Command-A family (HF model_type "cohere").
Minimal text-only dense wrapper, same shape as QwenForCausalLM. All
Cohere-specific behaviour (parallel decoder block, LayerNorm instead of
RMSNorm, LM-head logit_scale 0.0625, tied embeddings) is handled by the
model_type=="cohere" family dispatch in model.py / model_config.py (P5a).
Canada Quant Labs (org-internal) - bounty tt-metal#49307 track.
"""
# Class-level capabilities
model_capabilities = {
"supports_prefix_caching": True,
"supports_async_decode": True,
"supports_sample_on_device": True,
}
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@classmethod
def initialize_vllm_model(
cls,
hf_config,
mesh_device,
max_batch_size,
max_seq_len,
n_layers=None,
tt_data_parallel=1,
optimizations: str = "performance",
):
tt_model, model_args = initialize_vllm_text_transformer(
hf_config,
tt_data_parallel,
mesh_device,
max_batch_size,
max_seq_len=max_seq_len,
n_layers=n_layers,
dtype=ttnn.bfloat8_b,
optimizations=DecodersPrecision.from_string(optimizations)
if optimizations is not None
else DecodersPrecision.performance,
)
return cls(tt_model, model_args, mesh_device)
@property
def cache_path(self):
return self.model_args[0].model_cache_path
def prefill_forward(self, *args, **kwargs):
return super().prefill_forward_text(*args, **kwargs)
def decode_forward(self, *args, **kwargs):
return super().decode_forward(*args, **kwargs)
def allocate_kv_cache(self, *args, **kwargs):
return allocate_vllm_kv_cache(*args, **kwargs, dp_model=self.model, tt_cache_path=self.cache_path)
class MistralForCausalLM(Generator):
decode_input_update_contract = 1
# Class-level capabilities
model_capabilities = {
"supports_prefix_caching": True,
"supports_async_decode": True,
"supports_sample_on_device": True,
"max_device_top_k": 32,
# prefill_forward_single_user_text already routes a nonzero start_pos to
# the chunked SDPA, and the generator floors the offset to what that op
# needs, so a prompt split across engine steps needs no new prefill code.
"supports_chunked_prefill": True,
}
@classmethod
def get_max_tokens_all_users(
cls,
model_name: str = "",
num_devices: int = 1,
tt_data_parallel: int = 1,
**kwargs,
) -> int:
"""Returns config-specific all-user KV-cache token capacity."""
devices_per_dp_cache = num_devices // tt_data_parallel
is_wormhole = is_wormhole_b0()
# Mistral-7B on N150
if "Mistral-7B" in model_name and devices_per_dp_cache == 1 and is_wormhole:
return 65_536
return super().get_max_tokens_all_users(
model_name=model_name,
num_devices=num_devices,
tt_data_parallel=tt_data_parallel,
**kwargs,
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@classmethod
def initialize_vllm_model(
cls,
hf_config,
mesh_device,
max_batch_size,
max_seq_len,
n_layers=None,
tt_data_parallel=1,
optimizations: str = "performance",
):
tt_model, model_args = initialize_vllm_text_transformer(
hf_config,
tt_data_parallel,
mesh_device,
max_batch_size,
max_seq_len=max_seq_len,
n_layers=n_layers,
dtype=ttnn.bfloat8_b,
optimizations=(
DecodersPrecision.from_string(optimizations)
if optimizations is not None
else DecodersPrecision.performance
),
)
return cls(tt_model, model_args, mesh_device)
@property
def cache_path(self):
return self.model_args[0].model_cache_path
def prefill_forward(self, *args, **kwargs):
return super().prefill_forward_text(*args, **kwargs)
def decode_forward(self, *args, **kwargs):
return super().decode_forward(*args, **kwargs)
def allocate_kv_cache(self, *args, **kwargs):
return allocate_vllm_kv_cache(*args, **kwargs, dp_model=self.model, tt_cache_path=self.cache_path)
@MULTIMODAL_REGISTRY.register_processor(
Gemma3MultiModalProcessor,
info=Gemma3ProcessingInfo,
dummy_inputs=Gemma3DummyInputsBuilder,
)
class Gemma3ForConditionalGeneration(HybridAttentionForCausalLM, SupportsMultiModal):
"""Gemma3 multimodal — hybrid attention (sliding-window + full).
Gemma3's text decoder alternates ``sliding_attention`` and
``full_attention`` per ``hf_config.text_config.layer_types`` (a 5:1
ratio in the 27B variant), so the bridge inherits from
:class:`HybridAttentionForCausalLM` to opt into vLLM's hybrid kv cache
manager. Sliding-window layers index a smaller paged pool than
full-attention layers — the per-layer KV cache shape difference is
where the asymmetric-hybrid memory savings live, and was the original
motivation for kv-cache-groups (it's what unblocks the 62-layer × 107
MB-per-layer DRAM OOM on T3K seen in run 25437459815).
Mirrors the ``GptOssForCausalLM`` plumbing: ``prefill_forward`` /
``decode_forward`` stash ``page_tables_per_layer`` on each
``self.model[i]`` for the duration of a single
``super().{prefill_forward_text,decode_forward}`` call. The underlying
``Transformer.ttnn_*_forward`` (which ``TtGemmaModel`` inherits)
picks up the stash via ``_active_page_tables_per_layer`` and routes
each layer's attention to its own page table.
"""
decode_input_update_contract = 1
# Class-level capabilities
model_capabilities = {
"supports_prefix_caching": False,
"supports_async_decode": True,
"supports_sample_on_device": True,
"max_device_top_k": 32,
}
@classmethod
def get_max_tokens_all_users(
cls,
model_name: str = "",
num_devices: int = 1,
tt_data_parallel: int = 1,
**kwargs,
) -> int:
"""Returns config-specific all-user KV-cache token capacity."""
devices_per_dp_cache = num_devices // tt_data_parallel
is_wormhole = is_wormhole_b0()
# gemma-3-4b on wormhole configurations with up to 2 devices per DP shard
if "gemma-3-4b" in model_name.lower() and devices_per_dp_cache in (1, 2) and is_wormhole:
return 65_536
return super().get_max_tokens_all_users(
model_name=model_name,
num_devices=num_devices,
tt_data_parallel=tt_data_parallel,
**kwargs,
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@classmethod
def initialize_vllm_model(
cls,
hf_config,
mesh_device,
max_batch_size,
max_seq_len=131072,
n_layers=None,
tt_data_parallel=1,
optimizations: str = "performance",
):
from models.demos.multimodal.gemma3.demo.vision_demo import create_multimodal_model
optimizations = (
DecodersPrecision.from_string(optimizations) if optimizations is not None else DecodersPrecision.performance
)
submesh_devices = create_submeshes(mesh_device, tt_data_parallel)
model_args = []
model = []
state_dict = None
for submesh in submesh_devices:
model_args_i, model_i, state_dict = create_multimodal_model(
mesh_device=submesh,
max_batch_size=max_batch_size // tt_data_parallel,
max_seq_len=max_seq_len,
use_paged_kv_cache=True,
checkpoint=state_dict,
optimizations=lambda model_args: optimizations(model_args.n_layers, model_args.model_name),
)
model_args.append(model_args_i)
model.append(model_i)
return cls(model, model_args, mesh_device)
@property
def cache_path(self):
return self.model_args[0].model_cache_path
def prefill_forward(self, *args, page_tables_per_layer=None, **kwargs):
# While hybrid KV cache groups are disabled (one full-attention group
# for every layer), the per-layer page-table routing inside this
# bridge is buggy for users_row_sharded models: it shards page tables
# naively by mesh row, which doesn't match the gpt-oss
# slot // max_local_batch_size → row mapping and produces null
# content on the rows whose page-table chunks point at the wrong
# KV blocks. Until a row-aware per-layer routing lands, skip the
# hybrid path entirely and let the legacy single page_table flow
# through Generator.prefill_forward_text reach the model untouched.
if not self._HYBRID_KV_CACHE_GROUPS_ENABLED:
return super().prefill_forward_text(*args, **kwargs)
page_tables_per_layer = self._ensure_page_tables_per_layer(page_tables_per_layer, kwargs.get("page_table"))
per_submesh = self._chunk_page_tables_per_dp(page_tables_per_layer)
# Push the per-layer block IDs into the persistent device buffers
# *before* entering ``Generator.prefill_forward_text`` — that path
# may execute a captured trace, which reads block IDs from the
# persistent addresses and forbids in-trace writes. Allocation
# itself happens lazily in ``Transformer._page_tables_to_ttnn``
# the first time the inner forward runs (warmup compile).
if per_submesh is not None:
for m, pt_for_submesh in zip(self.model, per_submesh):
m.update_persistent_per_layer_page_tables(pt_for_submesh)
with self._route_per_layer_page_tables(per_submesh):
return super().prefill_forward_text(**kwargs)
def decode_forward(self, *args, page_tables_per_layer=None, **kwargs):
# See prefill_forward note above. Skip the hybrid path while
# _HYBRID_KV_CACHE_GROUPS_ENABLED is False.
if not self._HYBRID_KV_CACHE_GROUPS_ENABLED:
return super(HybridAttentionForCausalLM, self).decode_forward(*args, **kwargs)
page_tables_per_layer = self._ensure_page_tables_per_layer(page_tables_per_layer, kwargs.get("page_table"))
per_submesh = self._chunk_page_tables_per_dp(page_tables_per_layer)
if per_submesh is not None and self._reload_per_layer_page_tables(kwargs):
for m, pt_for_submesh in zip(self.model, per_submesh):
m.update_persistent_per_layer_page_tables(pt_for_submesh)
with self._route_per_layer_page_tables(per_submesh):
# Skip ``HybridAttentionForCausalLM.decode_forward``, which is a
# NotImplementedError placeholder; route to ``Generator``'s
# actual decode implementation.
return super(HybridAttentionForCausalLM, self).decode_forward(*args, **kwargs)
def allocate_kv_cache(self, *args, **kwargs):
return allocate_vllm_kv_cache(*args, **kwargs, dp_model=self.model, tt_cache_path=self.cache_path)
class Exaone4_5_ForConditionalGeneration(HybridAttentionForCausalLM):
"""EXAONE-4.5 (text decoder) for vLLM integration.
Named after the HF architecture string. Note that the vLLM TT plugin
does NOT pick this class up automatically: the plugin prepends ``TT``
to the checkpoint architecture and its built-in registry has no EXAONE
entry, so serving requires registering
``TTExaone4_5_ForConditionalGeneration`` -> this class in the vLLM
``ModelRegistry`` of the engine-core process. tt-inference-server does
this via an ``EXTRA_MODELS_DIR`` plugin bundle (see
tenstorrent/tt-inference-server#5023); direct plugin users must supply
an equivalent registration and set ``--max-model-len 131072``: the
checkpoint advertises 262144 tokens, but prefill above 131072 needs
sliding-window chunked prefill, which is not supported yet.
The checkpoint is multimodal, but ModelArgs runs its text decoder only
(``force_text_only``): hybrid LLLG sliding/full attention (via
:class:`HybridAttentionForCausalLM`), post-norm decoder, NoPE
full-attention layers. Vision inputs are not yet wired through vLLM —
this serves text-only requests.
"""
# Class-level capabilities
model_capabilities = {
"supports_prefix_caching": False, # Sliding window => no prefix caching
"supports_async_decode": True,
"supports_sample_on_device": True,
}
# One prefill chunk on P150x8 (MAX_PREFILL_CHUNK_SIZES["EXAONE-4.5-33B"]).
MAX_SUPPORTED_SEQ_LEN = 128 * 1024
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@classmethod
def initialize_vllm_model(
cls,
hf_config,
mesh_device,
max_batch_size,
max_seq_len,
n_layers=None,
tt_data_parallel=1,
optimizations: str = "performance",
):
# Prompts longer than one prefill chunk take the chunked path, where
# the sliding-window attention layers raise NotImplementedError, so
# reject the checkpoint's 262144-token default up front.
if max_seq_len > cls.MAX_SUPPORTED_SEQ_LEN:
raise ValueError(
f"EXAONE-4.5 currently supports max_seq_len <= {cls.MAX_SUPPORTED_SEQ_LEN}; "
f"got {max_seq_len}. Sliding-window chunked prefill is not supported; "
"pass --max-model-len 131072 to vLLM."
)
tt_model, model_args = initialize_vllm_text_transformer(
hf_config,
tt_data_parallel,
mesh_device,
max_batch_size,
max_seq_len=max_seq_len,
n_layers=n_layers,
dtype=ttnn.bfloat8_b,
optimizations=DecodersPrecision.from_string(optimizations)
if optimizations is not None
else DecodersPrecision.performance,
)
return cls(tt_model, model_args, mesh_device)
@property
def cache_path(self):
return self.model_args[0].model_cache_path
def prefill_forward(self, *args, page_tables_per_layer=None, **kwargs):
# While hybrid KV cache groups are disabled upstream (every layer is
# one full-attention group), skip the per-layer page-table routing
# and let the legacy single page_table flow through
# ``Generator.prefill_forward_text`` — same rationale as
# ``GptOssForCausalLM.prefill_forward``.
if not self._HYBRID_KV_CACHE_GROUPS_ENABLED:
return super().prefill_forward_text(*args, **kwargs)
page_tables_per_layer = self._ensure_page_tables_per_layer(page_tables_per_layer, kwargs.get("page_table"))
per_submesh = self._chunk_page_tables_per_dp(page_tables_per_layer)
if per_submesh is not None:
for m, pt_for_submesh in zip(self.model, per_submesh):
m.update_persistent_per_layer_page_tables(pt_for_submesh)
with self._route_per_layer_page_tables(per_submesh):
return super().prefill_forward_text(*args, **kwargs)
def decode_forward(self, *args, page_tables_per_layer=None, **kwargs):
# See prefill_forward note above.
if not self._HYBRID_KV_CACHE_GROUPS_ENABLED:
return super(HybridAttentionForCausalLM, self).decode_forward(*args, **kwargs)
page_tables_per_layer = self._ensure_page_tables_per_layer(page_tables_per_layer, kwargs.get("page_table"))
per_submesh = self._chunk_page_tables_per_dp(page_tables_per_layer)
if per_submesh is not None:
for m, pt_for_submesh in zip(self.model, per_submesh):
m.update_persistent_per_layer_page_tables(pt_for_submesh)
with self._route_per_layer_page_tables(per_submesh):
# ``HybridAttentionForCausalLM.decode_forward`` is a placeholder;
# route to ``Generator``'s actual decode implementation.
return super(HybridAttentionForCausalLM, self).decode_forward(*args, **kwargs)
def allocate_kv_cache(self, *args, **kwargs):
return allocate_vllm_kv_cache(*args, **kwargs, dp_model=self.model, tt_cache_path=self.cache_path)
class GptOssForCausalLM(HybridAttentionForCausalLM):
"""GPT-OSS model for vLLM integration.
GPT-OSS is a hybrid attention model — its layers alternate between
full attention and sliding-window attention per ``hf_config.layer_types``.
Inheriting from :class:`HybridAttentionForCausalLM` opts into vLLM's
hybrid kv cache manager so sliding-window layers can index a smaller
paged pool than full-attention layers, recovering the asymmetric-hybrid
memory waste described in vLLM's hybrid kv cache manager design.
The bridge accepts ``page_tables_per_layer`` from the plugin (one tensor
per decoder layer, layer-aligned with the underlying TT model's
``self.layers``) and stashes it on each TT model handle as
``_active_page_tables_per_layer`` so the model's ``ttnn_prefill_forward``
/ ``ttnn_decode_forward`` pick it up without us having to thread the
kwarg through every call site in :class:`Generator`. The attribute is
cleared on the way out so a subsequent legacy single-page-table call
isn't accidentally affected.
"""
decode_input_update_contract = 1
# Class-level capabilities
model_capabilities = {
"supports_prefix_caching": False, # Sliding window => no prefix caching
"supports_async_decode": True,
"supports_sample_on_device": True,
"max_device_top_k": 32,
}
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def prefill_forward(self, *args, page_tables_per_layer=None, **kwargs):
# While hybrid KV cache groups are disabled (one full-attention group
# for every layer), the per-layer page-table routing inside this
# bridge is buggy for users_row_sharded models: it shards page tables
# naively by mesh row, which doesn't match the gpt-oss
# slot // max_local_batch_size → row mapping and produces null
# content on the rows whose page-table chunks point at the wrong
# KV blocks. Until a row-aware per-layer routing lands, skip the
# hybrid path entirely and let the legacy single page_table flow
# through Generator.prefill_forward_text reach the model untouched.
if not self._HYBRID_KV_CACHE_GROUPS_ENABLED:
return super().prefill_forward_text(*args, **kwargs)
page_tables_per_layer = self._ensure_page_tables_per_layer(page_tables_per_layer, kwargs.get("page_table"))
per_submesh = self._chunk_page_tables_per_dp(page_tables_per_layer)
# See ``Gemma3ForConditionalGeneration.prefill_forward`` for why
# the persistent-buffer update has to happen *before* the inner
# decode/prefill path that may run captured traces.
if per_submesh is not None:
for m, pt_for_submesh in zip(self.model, per_submesh):
m.update_persistent_per_layer_page_tables(pt_for_submesh)
with self._route_per_layer_page_tables(per_submesh):
return super().prefill_forward_text(*args, **kwargs)
def decode_forward(self, *args, page_tables_per_layer=None, **kwargs):
# See prefill_forward note above. Skip the hybrid path while
# _HYBRID_KV_CACHE_GROUPS_ENABLED is False.
if not self._HYBRID_KV_CACHE_GROUPS_ENABLED:
return super(HybridAttentionForCausalLM, self).decode_forward(*args, **kwargs)
page_tables_per_layer = self._ensure_page_tables_per_layer(page_tables_per_layer, kwargs.get("page_table"))
per_submesh = self._chunk_page_tables_per_dp(page_tables_per_layer)
if per_submesh is not None and self._reload_per_layer_page_tables(kwargs):
for m, pt_for_submesh in zip(self.model, per_submesh):
m.update_persistent_per_layer_page_tables(pt_for_submesh)
with self._route_per_layer_page_tables(per_submesh):
# Skip ``HybridAttentionForCausalLM.decode_forward``, which is a
# NotImplementedError placeholder; route to ``Generator``'s
# actual decode implementation.
return super(HybridAttentionForCausalLM, self).decode_forward(*args, **kwargs)
@classmethod
def initialize_vllm_model(
cls,
hf_config,
mesh_device,
max_batch_size,
max_seq_len,
n_layers=None,
tt_data_parallel=1,
optimizations: str = None,
):
assert optimizations is None, "Custom optimizations are not supported for this model"
from models.demos.gpt_oss.tt.common import create_tt_model
model_args = []
model = []
state_dict = None
# GPT-OSS throughput profile uses user-row sharding on
# multi-row meshes with large max batch sizes (e.g., 128 on 4x8).
# This must be selected at model init time to ensure correct sharding
# and input preparation.
users_row_sharded = bool(mesh_device.shape[0] > 1 and max_batch_size > 32)
if users_row_sharded:
# For users_row_sharded, we internally manage DP=4 in attention so we don't need to create submeshes
tt_data_parallel = 1
submesh_devices = create_submeshes(mesh_device, tt_data_parallel)
for submesh in submesh_devices:
# Use the existing create_tt_model function
model_args_i, model_i, _, state_dict = create_tt_model(
mesh_device=submesh,
max_batch_size=max_batch_size // tt_data_parallel,
max_seq_len=max_seq_len,
paged_attention_config=None,
dtype=ttnn.bfloat8_b,
state_dict=state_dict,
num_layers=n_layers,
mesh_config=None,
create_kv_cache=False,
users_row_sharded=users_row_sharded,
use_throughput_experts=submesh.shape[0] > 1 and (max_batch_size > 1),
)
model_args.append(model_args_i)
model.append(model_i)
return cls(model, model_args, mesh_device)
@property
def cache_path(self):
return self.model_args[0].weight_cache_path(ttnn.bfloat8_b)
# prefill_forward / decode_forward are defined above with the
# per-layer page-table stash; allocate_kv_cache_per_layer is inherited
# from HybridAttentionForCausalLM.
def allocate_kv_cache(self, *args, **kwargs):
return allocate_vllm_kv_cache(*args, **kwargs, dp_model=self.model, tt_cache_path=self.cache_path)
|