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danieldk HF Staff commited on Jun 30

Commit

65b4aab

1 Parent(s): 85033ba

Remove stable ABI 2.10 builds, since we have 2.9 now.

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Files changed (16) hide show

build/torch-stable-abi210-cu128-x86_64-linux/__init__.py +0 -17
build/torch-stable-abi210-cu128-x86_64-linux/_flash_attn3_cuda_477ab85.abi3.so +0 -3
build/torch-stable-abi210-cu128-x86_64-linux/_ops.py +0 -9
build/torch-stable-abi210-cu128-x86_64-linux/flash_attn3/__init__.py +0 -26
build/torch-stable-abi210-cu128-x86_64-linux/flash_attn_config.py +0 -7
build/torch-stable-abi210-cu128-x86_64-linux/flash_attn_interface.py +0 -1127
build/torch-stable-abi210-cu128-x86_64-linux/metadata.json +0 -25
build/torch-stable-abi210-cu128-x86_64-linux/metadata.json.sigstore +0 -1
build/torch-stable-abi210-cu130-x86_64-linux/__init__.py +0 -17
build/torch-stable-abi210-cu130-x86_64-linux/_flash_attn3_cuda_477ab85.abi3.so +0 -3
build/torch-stable-abi210-cu130-x86_64-linux/_ops.py +0 -9
build/torch-stable-abi210-cu130-x86_64-linux/flash_attn3/__init__.py +0 -26
build/torch-stable-abi210-cu130-x86_64-linux/flash_attn_config.py +0 -7
build/torch-stable-abi210-cu130-x86_64-linux/flash_attn_interface.py +0 -1127
build/torch-stable-abi210-cu130-x86_64-linux/metadata.json +0 -25
build/torch-stable-abi210-cu130-x86_64-linux/metadata.json.sigstore +0 -1

build/torch-stable-abi210-cu128-x86_64-linux/__init__.py DELETED Viewed

@@ -1,17 +0,0 @@
-from .flash_attn_interface import (
-    flash_attn_combine,
-    flash_attn_func,
-    flash_attn_qkvpacked_func,
-    flash_attn_varlen_func,
-    flash_attn_with_kvcache,
-    get_scheduler_metadata,
-)
-__all__ = [
-    "flash_attn_combine",
-    "flash_attn_func",
-    "flash_attn_qkvpacked_func",
-    "flash_attn_varlen_func",
-    "flash_attn_with_kvcache",
-    "get_scheduler_metadata",
-]

build/torch-stable-abi210-cu128-x86_64-linux/_flash_attn3_cuda_477ab85.abi3.so DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dc32f163c5e1fb6362e1a2ec165a3d2b6d622929c4a463fa23bc6e561e7e4680
-size 802035904

build/torch-stable-abi210-cu128-x86_64-linux/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _flash_attn3_cuda_477ab85
-ops = torch.ops._flash_attn3_cuda_477ab85
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_flash_attn3_cuda_477ab85::{op_name}"

build/torch-stable-abi210-cu128-x86_64-linux/flash_attn3/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import importlib.util
-import sys
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch-stable-abi210-cu128-x86_64-linux/flash_attn_config.py DELETED Viewed

@@ -1,7 +0,0 @@
-# Auto-generated by flash attention 3 setup.py
-CONFIG = {'build_flags': {'FLASHATTENTION_DISABLE_BACKWARD': False, 'FLASHATTENTION_DISABLE_SPLIT': False, 'FLASHATTENTION_DISABLE_PAGEDKV': False, 'FLASHATTENTION_DISABLE_APPENDKV': False, 'FLASHATTENTION_DISABLE_LOCAL': False, 'FLASHATTENTION_DISABLE_SOFTCAP': False, 'FLASHATTENTION_DISABLE_PACKGQA': False, 'FLASHATTENTION_DISABLE_FP16': False, 'FLASHATTENTION_DISABLE_FP8': False, 'FLASHATTENTION_DISABLE_VARLEN': False, 'FLASHATTENTION_DISABLE_CLUSTER': False, 'FLASHATTENTION_DISABLE_HDIM64': False, 'FLASHATTENTION_DISABLE_HDIM96': False, 'FLASHATTENTION_DISABLE_HDIM128': False, 'FLASHATTENTION_DISABLE_HDIM192': False, 'FLASHATTENTION_DISABLE_HDIM256': False, 'FLASHATTENTION_DISABLE_SM8x': False, 'FLASHATTENTION_ENABLE_VCOLMAJOR': False, 'FLASH_ATTENTION_DISABLE_HDIMDIFF64': False, 'FLASH_ATTENTION_DISABLE_HDIMDIFF192': False}}
-def show():
-    from pprint import pprint
-    pprint(CONFIG)

build/torch-stable-abi210-cu128-x86_64-linux/flash_attn_interface.py DELETED Viewed

@@ -1,1127 +0,0 @@
-# Copyright (c) 2023, Tri Dao.
-from typing import Optional, Union, List, Tuple
-import torch
-import torch.nn as nn
-from ._ops import ops as flash_attn_3_cuda
-from ._ops import add_op_namespace_prefix
-def maybe_contiguous(x):
-    return x.contiguous() if x is not None and x.stride(-1) != 1 else x
-def round_multiple(x, m):
-    return (x + m - 1) // m * m
-def round_up_headdim(head_size: int) -> int:
-    from .flash_attn_config import CONFIG
-    if not CONFIG["build_flags"]["FLASHATTENTION_DISABLE_HDIM64"]:
-        if head_size <= 64:
-            return 64
-    if not CONFIG["build_flags"]["FLASHATTENTION_DISABLE_HDIM96"]:
-        if head_size <= 96:
-            return 96
-    if not CONFIG["build_flags"]["FLASHATTENTION_DISABLE_HDIM128"]:
-        if head_size <= 128:
-            return 128
-    if not CONFIG["build_flags"]["FLASHATTENTION_DISABLE_HDIM192"]:
-        if head_size <= 192:
-            return 192
-    if not CONFIG["build_flags"]["FLASHATTENTION_DISABLE_HDIM256"]:
-        if head_size <= 256:
-            return 256
-    return 256
-@torch.library.custom_op(add_op_namespace_prefix("_flash_attn_forward"), mutates_args=(), device_types="cuda")
-def _flash_attn_forward(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    k_new: Optional[torch.Tensor] = None,
-    v_new: Optional[torch.Tensor] = None,
-    qv: Optional[torch.Tensor] = None,
-    out_: Optional[torch.Tensor] = None,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k: Optional[torch.Tensor] = None,
-    cu_seqlens_k_new: Optional[torch.Tensor] = None,
-    seqused_q: Optional[torch.Tensor] = None,
-    seqused_k: Optional[torch.Tensor] = None,
-    max_seqlen_q: Optional[int] = None,
-    max_seqlen_k: Optional[int] = None,
-    page_table: Optional[torch.Tensor] = None,
-    kv_batch_idx: Optional[torch.Tensor] = None,
-    leftpad_k: Optional[torch.Tensor] = None,
-    rotary_cos: Optional[torch.Tensor] = None,
-    rotary_sin: Optional[torch.Tensor] = None,
-    seqlens_rotary: Optional[torch.Tensor] = None,
-    q_descale: Optional[torch.Tensor] = None,
-    k_descale: Optional[torch.Tensor] = None,
-    v_descale: Optional[torch.Tensor] = None,
-    softmax_scale: Optional[float] = None,
-    causal: bool = False,
-    window_size_left: int = -1,
-    window_size_right: int = -1,
-    attention_chunk: int = 0,
-    softcap: float = 0.0,
-    rotary_interleaved: bool = True,
-    scheduler_metadata: Optional[torch.Tensor] = None,
-    num_splits: int = 1,
-    pack_gqa: Optional[bool] = None,
-    sm_margin: int = 0,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-    q, k, k_new, v_new = [maybe_contiguous(x) for x in (q, k, k_new, v_new)]
-    v = v.contiguous() if v.stride(-1) != 1 and v.stride(-3) != 1 else v
-    cu_seqlens_q, cu_seqlens_k, cu_seqlens_k_new = [
-        maybe_contiguous(x) for x in (cu_seqlens_q, cu_seqlens_k, cu_seqlens_k_new)
-    ]
-    seqused_q, seqused_k = [maybe_contiguous(x) for x in (seqused_q, seqused_k)]
-    page_table, kv_batch_idx, leftpad_k = [
-        maybe_contiguous(x) for x in (page_table, kv_batch_idx, leftpad_k)
-    ]
-    rotary_cos, rotary_sin = [maybe_contiguous(x) for x in (rotary_cos, rotary_sin)]
-    seqlens_rotary = maybe_contiguous(seqlens_rotary)
-    out, softmax_lse, out_accum, softmax_lse_accum = flash_attn_3_cuda.fwd(
-        q,
-        k,
-        v,
-        k_new,
-        v_new,
-        qv,
-        out_,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        cu_seqlens_k_new,
-        seqused_q,
-        seqused_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        page_table,
-        kv_batch_idx,
-        leftpad_k,
-        rotary_cos,
-        rotary_sin,
-        seqlens_rotary,
-        q_descale,
-        k_descale,
-        v_descale,
-        softmax_scale,
-        causal,
-        window_size_left,
-        window_size_right,
-        attention_chunk,
-        softcap,
-        rotary_interleaved,
-        scheduler_metadata,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    if out_accum is None:
-        out_accum = torch.tensor([], device=out.device)
-    if softmax_lse_accum is None:
-        softmax_lse_accum = torch.tensor([], device=out.device)
-    return out, softmax_lse, out_accum, softmax_lse_accum
-@torch.library.register_fake(add_op_namespace_prefix("_flash_attn_forward"))
-def _flash_attn_forward_fake(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    k_new: Optional[torch.Tensor] = None,
-    v_new: Optional[torch.Tensor] = None,
-    qv: Optional[torch.Tensor] = None,
-    out_: Optional[torch.Tensor] = None,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k: Optional[torch.Tensor] = None,
-    cu_seqlens_k_new: Optional[torch.Tensor] = None,
-    seqused_q: Optional[torch.Tensor] = None,
-    seqused_k: Optional[torch.Tensor] = None,
-    max_seqlen_q: Optional[int] = None,
-    max_seqlen_k: Optional[int] = None,
-    page_table: Optional[torch.Tensor] = None,
-    kv_batch_idx: Optional[torch.Tensor] = None,
-    leftpad_k: Optional[torch.Tensor] = None,
-    rotary_cos: Optional[torch.Tensor] = None,
-    rotary_sin: Optional[torch.Tensor] = None,
-    seqlens_rotary: Optional[torch.Tensor] = None,
-    q_descale: Optional[torch.Tensor] = None,
-    k_descale: Optional[torch.Tensor] = None,
-    v_descale: Optional[torch.Tensor] = None,
-    softmax_scale: Optional[float] = None,
-    causal: bool = False,
-    window_size_left: int = -1,
-    window_size_right: int = -1,
-    attention_chunk: int = 0,
-    softcap: float = 0.0,
-    rotary_interleaved: bool = True,
-    scheduler_metadata: Optional[torch.Tensor] = None,
-    num_splits: int = 1,
-    pack_gqa: Optional[bool] = None,
-    sm_margin: int = 0,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-    """
-    Symbolic fake implementation of flash attention forward.
-    Returns tensors with the correct shapes and dtypes without actual computation.
-    """
-    # Determine if we're in varlen mode
-    is_varlen_q = cu_seqlens_q is not None
-    # Get dimensions from query tensor
-    if is_varlen_q:
-        # varlen mode: q is (total_q, num_heads, head_size)
-        total_q, num_heads, head_size = q.shape
-        batch_size = cu_seqlens_q.shape[0] - 1
-        if max_seqlen_q is None:
-            raise ValueError("max_seqlen_q must be provided if cu_seqlens_q is provided")
-        seqlen_q = max_seqlen_q
-    else:
-        # batch mode: q is (batch_size, seqlen_q, num_heads, head_size)
-        batch_size, seqlen_q, num_heads, head_size = q.shape
-        total_q = batch_size * q.shape[1]
-    # Get value head dimension
-    head_size_v = v.shape[-1]
-    # Determine output dtype (FP8 inputs produce BF16 outputs)
-    q_type = q.dtype
-    if q_type == torch.float8_e4m3fn:
-        out_dtype = torch.bfloat16
-    else:
-        out_dtype = q_type
-    # Create output tensor
-    if out_ is not None:
-        # If out_ is provided, _flash_attn_forward becomes non-functional
-        raise TypeError("Tracing (torch.compile/torch.export) with pre-allocated output tensor is not supported.")
-    if is_varlen_q:
-        out = torch.empty((total_q, num_heads, head_size_v), dtype=out_dtype, device=q.device)
-    else:
-        out = torch.empty((batch_size, seqlen_q, num_heads, head_size_v), dtype=out_dtype, device=q.device)
-    # Create softmax_lse tensor
-    if is_varlen_q:
-        softmax_lse = torch.empty((num_heads, total_q), dtype=torch.float32, device=q.device)
-    else:
-        softmax_lse = torch.empty((batch_size, num_heads, seqlen_q), dtype=torch.float32, device=q.device)
-    # TODO(guilhermeleobas): Implement "get_num_splits"
-    # There's an heuristic to compute num_splits when "num_splits <= 0"
-    # assert that num_splits is > 0 for now
-    if num_splits <= 0:
-        raise ValueError(f"tracing (torch.compile/torch.export) with num_splits <= 0 not supported. Got {num_splits=}")
-    if num_splits > 1:
-        if is_varlen_q:
-            out_accum = torch.empty((num_splits, num_heads, total_q, head_size_v), dtype=torch.float32, device=q.device)
-            softmax_lse_accum = torch.empty((num_splits, num_heads, total_q), dtype=torch.float32, device=q.device)
-        else:
-            out_accum = torch.empty((num_splits, batch_size, num_heads, seqlen_q, head_size_v), dtype=torch.float32, device=q.device)
-            softmax_lse_accum = torch.empty((num_splits, batch_size, num_heads, seqlen_q), dtype=torch.float32, device=q.device)
-    else:
-        # Tensors are not set when num_splits < 1
-        out_accum = torch.tensor([], device=out.device)
-        softmax_lse_accum = torch.tensor([], device=out.device)
-    return out, softmax_lse, out_accum, softmax_lse_accum
-@torch.library.custom_op(add_op_namespace_prefix("_flash_attn_backward"), mutates_args=("dq", "dk", "dv"), device_types="cuda")
-def _flash_attn_backward(
-    dout: torch.Tensor,
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    out: torch.Tensor,
-    softmax_lse: torch.Tensor,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k: Optional[torch.Tensor] = None,
-    sequed_q: Optional[torch.Tensor] = None,
-    sequed_k: Optional[torch.Tensor] = None,
-    max_seqlen_q: Optional[int] = None,
-    max_seqlen_k: Optional[int] = None,
-    dq: Optional[torch.Tensor] = None,
-    dk: Optional[torch.Tensor] = None,
-    dv: Optional[torch.Tensor] = None,
-    softmax_scale: Optional[float] = None,
-    is_causal: bool = False,
-    window_size_left: int = -1,
-    window_size_right: int = -1,
-    softcap: float = 0.0,
-    deterministic: bool = False,
-    sm_margin: int = 0,
-) -> torch.Tensor:
-    # dq, dk, dv are allocated by us so they should already be contiguous
-    dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)]
-    softmax_d, *rest = flash_attn_3_cuda.bwd(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        dq,
-        dk,
-        dv,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        sequed_q,
-        sequed_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        softmax_scale,
-        is_causal,
-        window_size_left,
-        window_size_right,
-        softcap,
-        deterministic,
-        sm_margin,
-    )
-    return softmax_d
-@torch.library.register_fake(add_op_namespace_prefix("_flash_attn_backward"))
-def _flash_attn_backward_fake(
-    dout: torch.Tensor,
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    out: torch.Tensor,
-    softmax_lse: torch.Tensor,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k: Optional[torch.Tensor] = None,
-    sequed_q: Optional[torch.Tensor] = None,
-    sequed_k: Optional[torch.Tensor] = None,
-    max_seqlen_q: Optional[int] = None,
-    max_seqlen_k: Optional[int] = None,
-    dq: Optional[torch.Tensor] = None,
-    dk: Optional[torch.Tensor] = None,
-    dv: Optional[torch.Tensor] = None,
-    softmax_scale: Optional[float] = None,
-    is_causal: bool = False,
-    window_size_left: int = -1,
-    window_size_right: int = -1,
-    softcap: float = 0.0,
-    deterministic: bool = False,
-    sm_margin: int = 0,
-) -> torch.Tensor:
-    is_varlen_q = cu_seqlens_q is not None
-    is_varlen_k = cu_seqlens_q is not None
-    is_varlen = is_varlen_q or is_varlen_k or sequed_q is not None or sequed_k is not None
-    if not is_varlen_q:
-        batch_size = q.size(0)
-        seqlen_q = q.size(1)
-        seqlen_k = k.size(1)
-        total_q = batch_size * q.size(1)
-    else:
-        batch_size = cu_seqlens_q.size(0) - 1
-        total_q = q.size(0)
-        seqlen_q = max_seqlen_q
-        seqlen_k = max_seqlen_k
-    if window_size_left >= seqlen_k - 1:
-        window_size_left = -1
-    if window_size_right >= seqlen_q - 1:
-        window_size_right = -1
-    if is_causal:
-        window_size_right = 0
-    is_causal = window_size_left < 0 and window_size_right == 0
-    head_size = q.size(-1)
-    head_size_v = v.size(-1)
-    head_size_rounded = round_up_headdim(max(head_size, head_size_v))
-    # Hopper gpus uses cuda compute capabilities 9.0
-    cap = torch.cuda.get_device_capability(q.device)
-    arch = cap[0] * 10 + cap[1]
-    is_local = (window_size_left >= 0 or window_size_right >= 0) and not is_causal
-    if head_size_rounded <= 64:
-        kBlockM_sm90 = 96 if (is_causal and softcap > 0.0) else 128
-    elif head_size_rounded <= 96:
-        kBlockM_sm90 = 64
-    elif head_size_rounded <= 128:
-        kBlockM_sm90 = 64 if (is_causal or is_local or softcap > 0.0) else 80
-    else:
-        kBlockM_sm90 = 64
-    kBlockM_sm80 = 128 if head_size_rounded <= 64 else 64
-    kBlockM_sm86 = 64 if head_size_rounded <= 192 else 32
-    if arch >= 90:
-        kBlockM = kBlockM_sm90
-    elif arch == 86 or arch == 89:
-        kBlockM = kBlockM_sm86
-    else:
-        kBlockM = kBlockM_sm80
-    num_heads = q.shape[-2]
-    seqlen_q_rounded = round_multiple(seqlen_q, kBlockM)
-    total_q_padded_rounded = round_multiple(total_q + batch_size * kBlockM, kBlockM)
-    dq = torch.empty_like(q) if dq is None else dq
-    dk = torch.empty_like(k) if dk is None else dk
-    dv = torch.empty_like(v) if dv is None else dv
-    if not is_varlen:
-        softmax_d = torch.empty((batch_size, num_heads, seqlen_q_rounded), dtype=torch.float32, device=q.device)
-    else:
-        softmax_d = torch.empty((num_heads, total_q_padded_rounded), dtype=torch.float32, device=q.device)
-    return softmax_d
-def setup_context(ctx, inputs, output):
-    q, k, v = inputs[:3]
-    out, softmax_lse, _, _ = output
-    ctx.save_for_backward(q, k, v, out, softmax_lse)
-    ctx.softmax_scale = inputs[-11]
-    ctx.causal = inputs[-10]
-    ctx.window_size = [inputs[-9], inputs[-8]]
-    ctx.attention_chunk = inputs[-7]
-    ctx.softcap = inputs[-6]
-    ctx.sm_margin = inputs[-1]
-def _backward(ctx, dout, *grads):
-    q, k, v, out, softmax_lse = ctx.saved_tensors
-    dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
-    _flash_attn_backward(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        None, None, # cu_seqlens_q, cu_seqlens_k,
-        None, None, # sequed_q, sequed_k,
-        None, None, # max_seqlen_q, max_seqlen_k,
-        dq,
-        dk,
-        dv,
-        ctx.softmax_scale,
-        ctx.causal,
-        ctx.window_size[0],
-        ctx.window_size[1],
-        ctx.softcap,
-        False, # deterministic
-        ctx.sm_margin,
-    )
-    return dq, dk, dv, *((None,) * 21)
-_flash_attn_forward.register_autograd(_backward, setup_context=setup_context)
-class FlashAttnQKVPackedFunc(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        qkv,
-        softmax_scale,
-        causal,
-        q_descale=None, k_descale=None, v_descale=None,
-        window_size=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        deterministic=False,
-        num_heads_q=None,
-        sm_margin=0,
-        return_softmax=False,
-    ):
-        if softmax_scale is None:
-            softmax_scale = qkv.shape[-1] ** (-0.5)
-        if qkv.dim() == 5:
-            assert qkv.shape[-3] == 3
-            q, k, v = qkv.unbind(dim=-3)
-        else:
-            assert qkv.dim() == 4
-            assert num_heads_q is not None
-            num_heads_k = (qkv.shape[2] - num_heads_q) // 2
-            assert num_heads_k * 2 + num_heads_q == qkv.shape[2]
-            q, k, v = qkv.split([num_heads_q, num_heads_k, num_heads_k], dim=-2)
-        out, softmax_lse, *rest = _flash_attn_forward(
-            q,
-            k,
-            v,
-            None, None,  # k_new, v_new
-            None,  # qv
-            None,  # out
-            None, None, None,   # cu_seqlens_q/k/k_new
-            None, None,   # seqused_q/k
-            None, None,   # max_seqlen_q/k
-            None, None, None,   # page_table, kv_batch_idx, leftpad_k,
-            None, None, None,  # rotary_cos/sin, seqlens_rotary
-            q_descale, k_descale, v_descale,
-            softmax_scale,
-            causal=causal,
-            window_size_left=window_size[0],
-            window_size_right=window_size[1],
-            attention_chunk=attention_chunk,
-            softcap=softcap,
-            sm_margin=sm_margin,
-        )
-        # ctx.save_for_backward(q, k, v, out_padded, softmax_lse)
-        ctx.save_for_backward(q, k, v, out, softmax_lse)
-        ctx.softmax_scale = softmax_scale
-        ctx.causal = causal
-        ctx.window_size = window_size
-        ctx.attention_chunk = attention_chunk
-        ctx.softcap = softcap
-        ctx.deterministic = deterministic
-        ctx.ndim = qkv.dim()
-        ctx.sm_margin = sm_margin
-        return (out, softmax_lse) if return_softmax else out
-    @staticmethod
-    def backward(ctx, dout, *args):
-        q, k, v, out, softmax_lse = ctx.saved_tensors
-        assert ctx.attention_chunk == 0, "FA3 backward does not support attention_chunk"
-        if ctx.ndim == 5:
-            qkv_shape = q.shape[:-2] + (3, *q.shape[-2:])
-            dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device)
-            dq, dk, dv = dqkv.unbind(dim=-3)
-        else:
-            num_heads_q = q.shape[2]
-            num_heads_k = k.shape[2]
-            qkv_shape = q.shape[:-2] + (num_heads_q + num_heads_k * 2, *q.shape[-1:])
-            dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device)
-            dq, dk, dv = dqkv.split([num_heads_q, num_heads_k, num_heads_k], dim=-2)
-        _flash_attn_backward(
-            dout,
-            q,
-            k,
-            v,
-            out,
-            softmax_lse,
-            None, None, # cu_seqlens_q, cu_seqlens_k,
-            None, None, # sequed_q, sequed_k,
-            None, None, # max_seqlen_q, max_seqlen_k,
-            dq,
-            dk,
-            dv,
-            ctx.softmax_scale,
-            ctx.causal,
-            ctx.window_size[0],
-            ctx.window_size[1],
-            ctx.softcap,
-            ctx.deterministic,
-            ctx.sm_margin,
-        )
-        dqkv = dqkv[..., : dout.shape[-1]]  # We could have padded the head dimension
-        return dqkv, None, None, None, None, None, None, None, None, None, None, None, None
-class FlashAttnFunc(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        q,
-        k,
-        v,
-        softmax_scale,
-        causal,
-        qv=None,
-        q_descale=None, k_descale=None, v_descale=None,
-        window_size=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        num_splits=1,
-        pack_gqa=None,
-        deterministic=False,
-        sm_margin=0,
-        return_softmax=False,
-    ):
-        if softmax_scale is None:
-            softmax_scale = (q.shape[-1] + (qv.shape[-1] if qv is not None else 0)) ** (-0.5)
-        # out, q, k, v, out_padded, softmax_lse = _flash_attn_forward(
-        out, softmax_lse, *rest = _flash_attn_forward(
-            q,
-            k,
-            v,
-            None, None,  # k_new, v_new
-            qv,  # qv
-            None,  # out
-            None, None, None,   # cu_seqlens_q/k/k_new
-            None, None,   # seqused_q/k
-            None, None,   # max_seqlen_q/k
-            None, None, None,   # page_table, kv_batch_idx, leftpad_k,
-            None, None, None,  # rotary_cos/sin, seqlens_rotary
-            q_descale, k_descale, v_descale,
-            softmax_scale,
-            causal=causal,
-            window_size_left=window_size[0],
-            window_size_right=window_size[1],
-            attention_chunk=attention_chunk,
-            softcap=softcap,
-            num_splits=num_splits,
-            pack_gqa=pack_gqa,
-            sm_margin=sm_margin,
-        )
-        # ctx.save_for_backward(q, k, v, out_padded, softmax_lse)
-        ctx.save_for_backward(q, k, v, out, softmax_lse)
-        ctx.softmax_scale = softmax_scale
-        ctx.causal = causal
-        ctx.window_size = window_size
-        ctx.attention_chunk = attention_chunk
-        ctx.softcap = softcap
-        ctx.deterministic = deterministic
-        ctx.sm_margin = sm_margin
-        return (out, softmax_lse) if return_softmax else out
-    @staticmethod
-    def backward(ctx, dout, *args):
-        q, k, v, out, softmax_lse = ctx.saved_tensors
-        assert ctx.attention_chunk == 0, "FA3 backward does not support attention_chunk"
-        dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
-        _flash_attn_backward(
-            dout,
-            q,
-            k,
-            v,
-            out,
-            softmax_lse,
-            None, None, # cu_seqlens_q, cu_seqlens_k,
-            None, None, # sequed_q, sequed_k,
-            None, None, # max_seqlen_q, max_seqlen_k,
-            dq,
-            dk,
-            dv,
-            ctx.softmax_scale,
-            ctx.causal,
-            ctx.window_size[0],
-            ctx.window_size[1],
-            ctx.softcap,
-            ctx.deterministic,
-            ctx.sm_margin,
-        )
-        dq = dq[..., : q.shape[-1]]  # We could have padded the head dimension
-        dk = dk[..., : k.shape[-1]]
-        dv = dv[..., : v.shape[-1]]
-        return dq, dk, dv, None, None, None, None, None, None, None, None, None, None, None, None, None, None
-class FlashAttnVarlenFunc(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        q,
-        k,
-        v,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        seqused_q,
-        seqused_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        softmax_scale,
-        causal,
-        qv=None,
-        q_descale=None, k_descale=None, v_descale=None,
-        window_size=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        num_splits=1,
-        pack_gqa=None,
-        deterministic=False,
-        sm_margin=0,
-        return_softmax=False,
-    ):
-        if softmax_scale is None:
-            softmax_scale = (q.shape[-1] + (qv.shape[-1] if qv is not None else 0)) ** (-0.5)
-        # out, q, k, v, out_padded, softmax_lse = _flash_attn_varlen_forward(
-        out, softmax_lse, *rest = _flash_attn_forward(
-            q,
-            k,
-            v,
-            None, None,  # k_new, v_new
-            qv,  # qv
-            None,  # out
-            cu_seqlens_q,
-            cu_seqlens_k,
-            None,   # cu_seqlens_k_new
-            seqused_q,
-            seqused_k,
-            max_seqlen_q,
-            max_seqlen_k,
-            None, None, None,   # page_table, kv_batch_idx, leftpad_k,
-            None, None, None,  # rotary_cos/sin, seqlens_rotary
-            q_descale, k_descale, v_descale,
-            softmax_scale,
-            causal=causal,
-            window_size_left=window_size[0],
-            window_size_right=window_size[1],
-            attention_chunk=attention_chunk,
-            softcap=softcap,
-            num_splits=num_splits,
-            pack_gqa=pack_gqa,
-            sm_margin=sm_margin,
-        )
-        # ctx.save_for_backward(q, k, v, out_padded, softmax_lse, cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k)
-        ctx.save_for_backward(q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k)
-        ctx.max_seqlen_q = max_seqlen_q
-        ctx.max_seqlen_k = max_seqlen_k
-        ctx.softmax_scale = softmax_scale
-        ctx.causal = causal
-        ctx.window_size = window_size
-        ctx.attention_chunk = attention_chunk
-        ctx.softcap = softcap
-        ctx.deterministic = deterministic
-        ctx.sm_margin = sm_margin
-        return (out, softmax_lse) if return_softmax else out
-    @staticmethod
-    def backward(ctx, dout, *args):
-        q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k = ctx.saved_tensors
-        assert ctx.attention_chunk == 0, "FA3 backward does not support attention_chunk"
-        dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
-        _flash_attn_backward(
-            dout,
-            q,
-            k,
-            v,
-            out,
-            softmax_lse,
-            cu_seqlens_q,
-            cu_seqlens_k,
-            seqused_q,
-            seqused_k,
-            ctx.max_seqlen_q,
-            ctx.max_seqlen_k,
-            dq,
-            dk,
-            dv,
-            ctx.softmax_scale,
-            ctx.causal,
-            ctx.window_size[0],
-            ctx.window_size[1],
-            ctx.softcap,
-            ctx.deterministic,
-            ctx.sm_margin,
-        )
-        dq = dq[..., : q.shape[-1]]  # We could have padded the head dimension
-        dk = dk[..., : k.shape[-1]]
-        dv = dv[..., : v.shape[-1]]
-        return dq, dk, dv, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None
-def flash_attn_qkvpacked_func(
-    qkv,
-    softmax_scale=None,
-    causal=False,
-    q_descale=None, k_descale=None, v_descale=None,
-    window_size=(-1, -1),
-    attention_chunk=0,
-    softcap=0.0,
-    deterministic=False,
-    num_heads_q=None,
-    sm_margin=0,
-    return_attn_probs=False,
-):
-    """dropout_p should be set to 0.0 during evaluation
-    If Q, K, V are already stacked into 1 tensor, this function will be faster than
-    calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation
-    of the gradients of Q, K, V.
-    For multi-query and grouped-query attention (MQA/GQA), please see
-    flash_attn_kvpacked_func and flash_attn_func.
-    If window_size != (-1, -1), implements sliding window local attention. Query at position i
-    will only attend to keys between [i - window_size[0], i + window_size[1]] inclusive.
-    Arguments:
-        qkv: (batch_size, seqlen, 3, nheads, headdim)
-        dropout_p: float. Dropout probability.
-        softmax_scale: float. The scaling of QK^T before applying softmax.
-            Default to 1 / sqrt(headdim).
-        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
-        window_size: (left, right). If not (-1, -1), implements sliding window local attention.
-        softcap: float. Anything > 0 activates softcapping attention.
-        alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of (-alibi_slope * |i - j|) is added to
-            the attention score of query i and key j.
-        deterministic: bool. Whether to use the deterministic implementation of the backward pass,
-            which is slightly slower and uses more memory. The forward pass is always deterministic.
-        return_attn_probs: bool. Whether to return the attention probabilities. This option is for
-           testing only. The returned probabilities are not guaranteed to be correct
-           (they might not have the right scaling).
-    Return:
-        out: (batch_size, seqlen, nheads, headdim).
-        softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
-            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
-            normalization factor).
-        S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).
-            The output of softmax (possibly with different scaling). It also encodes the dropout
-            pattern (negative means that location was dropped, nonnegative means it was kept).
-    """
-    return FlashAttnQKVPackedFunc.apply(
-        qkv,
-        softmax_scale,
-        causal,
-        q_descale, k_descale, v_descale,
-        window_size,
-        attention_chunk,
-        softcap,
-        deterministic,
-        num_heads_q,
-        sm_margin,
-        return_attn_probs,
-    )
-def flash_attn_func(
-    q,
-    k,
-    v,
-    softmax_scale=None,
-    causal=False,
-    qv=None,
-    q_descale=None, k_descale=None, v_descale=None,
-    window_size=(-1, -1),
-    attention_chunk=0,
-    softcap=0.0,
-    num_splits=1,
-    pack_gqa=None,
-    deterministic=False,
-    sm_margin=0,
-    return_attn_probs=False,
-):
-    """dropout_p should be set to 0.0 during evaluation
-    Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
-    than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
-    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
-    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
-    If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
-    For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
-        1 1 1 1 0
-        1 1 1 1 1
-    If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
-        0 0
-        0 0
-        0 0
-        1 0
-        1 1
-    If the row of the mask is all zero, the output will be zero.
-    If window_size != (-1, -1), implements sliding window local attention. Query at position i
-    will only attend to keys between
-    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
-    Arguments:
-        q: (batch_size, seqlen, nheads, headdim)
-        k: (batch_size, seqlen, nheads_k, headdim)
-        v: (batch_size, seqlen, nheads_k, headdim)
-        dropout_p: float. Dropout probability.
-        softmax_scale: float. The scaling of QK^T before applying softmax.
-            Default to 1 / sqrt(headdim).
-        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
-        window_size: (left, right). If not (-1, -1), implements sliding window local attention.
-        alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
-            (-alibi_slope * |i + seqlen_k - seqlen_q - j|)
-            is added to the attention score of query i and key j.
-        deterministic: bool. Whether to use the deterministic implementation of the backward pass,
-            which is slightly slower and uses more memory. The forward pass is always deterministic.
-        return_attn_probs: bool. Whether to return the attention probabilities. This option is for
-           testing only. The returned probabilities are not guaranteed to be correct
-           (they might not have the right scaling).
-    Return:
-        out: (batch_size, seqlen, nheads, headdim).
-        softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
-            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
-            normalization factor).
-    """
-    return FlashAttnFunc.apply(
-        q,
-        k,
-        v,
-        softmax_scale,
-        causal,
-        qv,
-        q_descale, k_descale, v_descale,
-        window_size,
-        attention_chunk,
-        softcap,
-        num_splits,
-        pack_gqa,
-        deterministic,
-        sm_margin,
-        return_attn_probs,
-    )
-def flash_attn_varlen_func(
-    q,
-    k,
-    v,
-    cu_seqlens_q,
-    cu_seqlens_k,
-    max_seqlen_q,
-    max_seqlen_k,
-    seqused_q=None,
-    seqused_k=None,
-    softmax_scale=None,
-    causal=False,
-    qv=None,
-    q_descale=None, k_descale=None, v_descale=None,
-    window_size=(-1, -1),
-    attention_chunk=0,
-    softcap=0.0,
-    num_splits=1,
-    pack_gqa=None,
-    deterministic=False,
-    sm_margin=0,
-    return_attn_probs=False,
-):
-    return FlashAttnVarlenFunc.apply(
-        q,
-        k,
-        v,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        seqused_q,
-        seqused_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        softmax_scale,
-        causal,
-        qv,
-        q_descale, k_descale, v_descale,
-        window_size,
-        attention_chunk,
-        softcap,
-        num_splits,
-        pack_gqa,
-        deterministic,
-        sm_margin,
-        return_attn_probs,
-    )
-def flash_attn_combine(out_partial, lse_partial, out=None, out_dtype=None):
-    return flash_attn_3_cuda.fwd_combine(out_partial, lse_partial, out, out_dtype)
-def flash_attn_with_kvcache(
-    q,
-    k_cache,
-    v_cache,
-    k=None,
-    v=None,
-    qv=None,
-    rotary_cos=None,
-    rotary_sin=None,
-    cache_seqlens: Optional[Union[(int, torch.Tensor)]] = None,
-    cache_batch_idx: Optional[torch.Tensor] = None,
-    cache_leftpad: Optional[torch.Tensor] = None,
-    page_table: Optional[torch.Tensor] = None,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k_new: Optional[torch.Tensor] = None,
-    max_seqlen_q: Optional[int] = None,
-    rotary_seqlens: Optional[torch.Tensor] = None,
-    q_descale: Optional[torch.Tensor] = None,
-    k_descale: Optional[torch.Tensor] = None,
-    v_descale: Optional[torch.Tensor] = None,
-    softmax_scale=None,
-    causal=False,
-    window_size=(-1, -1),  # -1 means infinite context window
-    attention_chunk=0,
-    softcap=0.0, # 0.0 means deactivated
-    rotary_interleaved=True,
-    scheduler_metadata=None,
-    num_splits=0,    # Can be tuned for speed
-    pack_gqa=None,   # Can be tuned for speed
-    sm_margin=0,     # Can be tuned if some SMs are used for communication
-    return_softmax_lse=False,
-):
-    """
-    If k and v are not None, k_cache and v_cache will be updated *inplace* with the new values from
-    k and v. This is useful for incremental decoding: you can pass in the cached keys/values from
-    the previous step, and update them with the new keys/values from the current step, and do
-    attention with the updated cache, all in 1 kernel.
-    If you pass in k / v, you must make sure that the cache is large enough to hold the new values.
-    For example, the KV cache could be pre-allocated with the max sequence length, and you can use
-    cache_seqlens to keep track of the current sequence lengths of each sequence in the batch.
-    Also apply rotary embedding if rotary_cos and rotary_sin are passed in. The key @k will be
-    rotated by rotary_cos and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
-    If causal or local (i.e., window_size != (-1, -1)), the query @q will be rotated by rotary_cos
-    and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
-    If not causal and not local, the query @q will be rotated by rotary_cos and rotary_sin at
-    indices cache_seqlens only (i.e. we consider all tokens in @q to be at position cache_seqlens).
-    See tests/test_flash_attn.py::test_flash_attn_kvcache for examples of how to use this function.
-    Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
-    than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
-    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
-    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
-    If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
-    For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
-        1 1 1 1 0
-        1 1 1 1 1
-    If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
-        0 0
-        0 0
-        0 0
-        1 0
-        1 1
-    If the row of the mask is all zero, the output will be zero.
-    If window_size != (-1, -1), implements sliding window local attention. Query at position i
-    will only attend to keys between
-    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
-    Note: Does not support backward pass.
-    Arguments:
-        q: (batch_size, seqlen, nheads, headdim)
-        k_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim) if there's no page_table,
-            or (num_blocks, page_block_size, nheads_k, headdim) if there's a page_table (i.e. paged KV cache)
-            page_block_size can be arbitrary (e.g, 1, 2, 3, 64, etc.).
-        v_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim_v) if there's no page_table,
-            or (num_blocks, page_block_size, nheads_k, headdim_v) if there's a page_table (i.e. paged KV cache)
-        k [optional]: (batch_size, seqlen_new, nheads_k, headdim). If not None, we concatenate
-            k with k_cache, starting at the indices specified by cache_seqlens.
-        v [optional]: (batch_size, seqlen_new, nheads_k, headdim_v). Similar to k.
-        qv [optional]: (batch_size, seqlen, nheads, headdim_v)
-        rotary_cos [optional]: (seqlen_ro, rotary_dim / 2). If not None, we apply rotary embedding
-            to k and q. Only applicable if k and v are passed in. rotary_dim must be divisible by 16.
-        rotary_sin [optional]: (seqlen_ro, rotary_dim / 2). Similar to rotary_cos.
-        cache_seqlens: int, or (batch_size,), dtype torch.int32. The sequence lengths of the
-            KV cache.
-        cache_batch_idx: (batch_size,), dtype torch.int32. The indices used to index into the KV cache.
-            If None, we assume that the batch indices are [0, 1, 2, ..., batch_size - 1].
-            If the indices are not distinct, and k and v are provided, the values updated in the cache
-                 might come from any of the duplicate indices.
-        cache_leftpad: (batch_size,), dtype torch.int32. The index that the KV cache starts. If None, assume 0.
-        page_table [optional]: (batch_size, max_num_blocks_per_seq), dtype torch.int32.
-        softmax_scale: float. The scaling of QK^T before applying softmax.
-            Default to 1 / sqrt(headdim).
-        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
-        window_size: (left, right). If not (-1, -1), implements sliding window local attention.
-        softcap: float. Anything > 0 activates softcapping attention.
-        rotary_interleaved: bool. Only applicable if rotary_cos and rotary_sin are passed in.
-            If True, rotary embedding will combine dimensions 0 & 1, 2 & 3, etc. If False,
-            rotary embedding will combine dimensions 0 & rotary_dim / 2, 1 & rotary_dim / 2 + 1
-            (i.e. GPT-NeoX style).
-        num_splits: int. If > 1, split the key/value into this many chunks along the sequence.
-           If num_splits == 1, we don't split the key/value. If num_splits == 0, we use a heuristic
-           to automatically determine the number of splits.
-           Don't change this unless you know what you are doing.
-        return_softmax_lse: bool. Whether to return the logsumexp of the attention scores.
-    Return:
-        out: (batch_size, seqlen, nheads, headdim).
-        softmax_lse [optional, if return_softmax_lse=True]: (batch_size, nheads, seqlen). The
-            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
-            normalization factor).
-    """
-    assert k_cache.stride(-1) == 1, "k_cache must have contiguous last dimension"
-    assert v_cache.stride(-1) == 1, "v_cache must have contiguous last dimension"
-    if softmax_scale is None:
-        softmax_scale = (q.shape[-1] + (qv.shape[-1] if qv is not None else 0)) ** (-0.5)
-    if cache_seqlens is not None and isinstance(cache_seqlens, int):
-        cache_seqlens = torch.full(
-            (q.shape[0],), cache_seqlens, dtype=torch.int32, device=k_cache.device
-        )
-        cache_seqlens = maybe_contiguous(cache_seqlens)
-    out, softmax_lse, *rest = _flash_attn_forward(
-        q,
-        k_cache,
-        v_cache,
-        k,
-        v,
-        qv,
-        None,  # out
-        cu_seqlens_q,
-        None,  # cu_seqlens_k
-        cu_seqlens_k_new,
-        None,  # seqused_q
-        cache_seqlens,
-        max_seqlen_q,
-        None,  # max_seqlen_k
-        page_table,
-        cache_batch_idx,
-        cache_leftpad,
-        rotary_cos,
-        rotary_sin,
-        rotary_seqlens,
-        q_descale, k_descale, v_descale,
-        softmax_scale,
-        causal=causal,
-        window_size_left=window_size[0],
-        window_size_right=window_size[1],
-        attention_chunk=attention_chunk,
-        softcap=softcap,
-        rotary_interleaved=rotary_interleaved,
-        scheduler_metadata=scheduler_metadata,
-        num_splits=num_splits,
-        pack_gqa=pack_gqa,
-        sm_margin=sm_margin,
-    )
-    # return (out, softmax_lse) if return_softmax_lse else out
-    return (out, softmax_lse, *rest) if return_softmax_lse else out
-def get_scheduler_metadata(
-    batch_size, max_seqlen_q, max_seqlen_k, num_heads_q, num_heads_kv, headdim,
-    cache_seqlens: torch.Tensor,
-    qkv_dtype=torch.bfloat16,
-    headdim_v=None,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k_new: Optional[torch.Tensor] = None,
-    cache_leftpad: Optional[torch.Tensor] = None,
-    page_size: Optional[int] = None,
-    max_seqlen_k_new=0,
-    causal=False,
-    window_size=(-1, -1),  # -1 means infinite context window
-    attention_chunk=0,
-    has_softcap=False,
-    num_splits=0,    # Can be tuned for speed
-    pack_gqa=None,   # Can be tuned for speed
-    sm_margin=0,     # Can be tuned if some SMs are used for communication
-):
-    cache_seqlens = maybe_contiguous(cache_seqlens)
-    if headdim_v is None:
-        headdim_v = headdim
-    scheduler_metadata = flash_attn_3_cuda.get_scheduler_metadata(
-        batch_size, max_seqlen_q, max_seqlen_k, num_heads_q, num_heads_kv, headdim, headdim_v,
-        qkv_dtype,
-        cache_seqlens,
-        cu_seqlens_q,
-        None,  # cu_seqlens_k
-        cu_seqlens_k_new,
-        None,  # seqused_q
-        cache_leftpad,
-        page_size,
-        max_seqlen_k_new,
-        causal,
-        window_size[0], window_size[1],
-        attention_chunk,
-        has_softcap,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    return scheduler_metadata

build/torch-stable-abi210-cu128-x86_64-linux/metadata.json DELETED Viewed

@@ -1,25 +0,0 @@
-{
-  "name": "flash-attn3",
-  "id": "_flash_attn3_cuda_477ab85",
-  "version": 1,
-  "license": "BSD-3-Clause",
-  "python-depends": [],
-  "backend": {
-    "type": "cuda",
-    "archs": [
-      "8.0",
-      "9.0a"
-    ]
-  },
-  "digest": {
-    "algorithm": "sha256",
-    "files": {
-      "__init__.py": "KXVmQJM+KhWc2UqWJesupCaP+mqAvTx7yGueFATglHE=",
-      "_flash_attn3_cuda_477ab85.abi3.so": "3DLxY8Xh+2Ni4aLsFlo9K21iKSnEpGP6I7xuVh5+RoA=",
-      "_ops.py": "hccJrV95SPARE3ynEBHpwny/YFXZqpZKdGdbTTnKTsc=",
-      "flash_attn3/__init__.py": "DFYPlrhXwYjEqCl/8n0SmWGZV8NFml5DPhMjKfv98GY=",
-      "flash_attn_config.py": "uxo+eyMcDit//8YDaS4Rtk5BTMKzTSK9tOKTjdWVe/w=",
-      "flash_attn_interface.py": "y0bD2JYGMFgX6iDre3zJN2sBPuq/12N6Mo55Z+HQCNQ="
-    }
-  }
-}

build/torch-stable-abi210-cu128-x86_64-linux/metadata.json.sigstore DELETED Viewed

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build/torch-stable-abi210-cu130-x86_64-linux/__init__.py DELETED Viewed

@@ -1,17 +0,0 @@
-from .flash_attn_interface import (
-    flash_attn_combine,
-    flash_attn_func,
-    flash_attn_qkvpacked_func,
-    flash_attn_varlen_func,
-    flash_attn_with_kvcache,
-    get_scheduler_metadata,
-)
-__all__ = [
-    "flash_attn_combine",
-    "flash_attn_func",
-    "flash_attn_qkvpacked_func",
-    "flash_attn_varlen_func",
-    "flash_attn_with_kvcache",
-    "get_scheduler_metadata",
-]

build/torch-stable-abi210-cu130-x86_64-linux/_flash_attn3_cuda_477ab85.abi3.so DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a79c38ca3cbf5f6630e96eb1ab6022b21be7d93e2aa2461d0da7341a0f243f30
-size 821408616

build/torch-stable-abi210-cu130-x86_64-linux/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _flash_attn3_cuda_477ab85
-ops = torch.ops._flash_attn3_cuda_477ab85
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_flash_attn3_cuda_477ab85::{op_name}"

build/torch-stable-abi210-cu130-x86_64-linux/flash_attn3/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import importlib.util
-import sys
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch-stable-abi210-cu130-x86_64-linux/flash_attn_config.py DELETED Viewed

@@ -1,7 +0,0 @@
-# Auto-generated by flash attention 3 setup.py
-CONFIG = {'build_flags': {'FLASHATTENTION_DISABLE_BACKWARD': False, 'FLASHATTENTION_DISABLE_SPLIT': False, 'FLASHATTENTION_DISABLE_PAGEDKV': False, 'FLASHATTENTION_DISABLE_APPENDKV': False, 'FLASHATTENTION_DISABLE_LOCAL': False, 'FLASHATTENTION_DISABLE_SOFTCAP': False, 'FLASHATTENTION_DISABLE_PACKGQA': False, 'FLASHATTENTION_DISABLE_FP16': False, 'FLASHATTENTION_DISABLE_FP8': False, 'FLASHATTENTION_DISABLE_VARLEN': False, 'FLASHATTENTION_DISABLE_CLUSTER': False, 'FLASHATTENTION_DISABLE_HDIM64': False, 'FLASHATTENTION_DISABLE_HDIM96': False, 'FLASHATTENTION_DISABLE_HDIM128': False, 'FLASHATTENTION_DISABLE_HDIM192': False, 'FLASHATTENTION_DISABLE_HDIM256': False, 'FLASHATTENTION_DISABLE_SM8x': False, 'FLASHATTENTION_ENABLE_VCOLMAJOR': False, 'FLASH_ATTENTION_DISABLE_HDIMDIFF64': False, 'FLASH_ATTENTION_DISABLE_HDIMDIFF192': False}}
-def show():
-    from pprint import pprint
-    pprint(CONFIG)

build/torch-stable-abi210-cu130-x86_64-linux/flash_attn_interface.py DELETED Viewed

@@ -1,1127 +0,0 @@
-# Copyright (c) 2023, Tri Dao.
-from typing import Optional, Union, List, Tuple
-import torch
-import torch.nn as nn
-from ._ops import ops as flash_attn_3_cuda
-from ._ops import add_op_namespace_prefix
-def maybe_contiguous(x):
-    return x.contiguous() if x is not None and x.stride(-1) != 1 else x
-def round_multiple(x, m):
-    return (x + m - 1) // m * m
-def round_up_headdim(head_size: int) -> int:
-    from .flash_attn_config import CONFIG
-    if not CONFIG["build_flags"]["FLASHATTENTION_DISABLE_HDIM64"]:
-        if head_size <= 64:
-            return 64
-    if not CONFIG["build_flags"]["FLASHATTENTION_DISABLE_HDIM96"]:
-        if head_size <= 96:
-            return 96
-    if not CONFIG["build_flags"]["FLASHATTENTION_DISABLE_HDIM128"]:
-        if head_size <= 128:
-            return 128
-    if not CONFIG["build_flags"]["FLASHATTENTION_DISABLE_HDIM192"]:
-        if head_size <= 192:
-            return 192
-    if not CONFIG["build_flags"]["FLASHATTENTION_DISABLE_HDIM256"]:
-        if head_size <= 256:
-            return 256
-    return 256
-@torch.library.custom_op(add_op_namespace_prefix("_flash_attn_forward"), mutates_args=(), device_types="cuda")
-def _flash_attn_forward(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    k_new: Optional[torch.Tensor] = None,
-    v_new: Optional[torch.Tensor] = None,
-    qv: Optional[torch.Tensor] = None,
-    out_: Optional[torch.Tensor] = None,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k: Optional[torch.Tensor] = None,
-    cu_seqlens_k_new: Optional[torch.Tensor] = None,
-    seqused_q: Optional[torch.Tensor] = None,
-    seqused_k: Optional[torch.Tensor] = None,
-    max_seqlen_q: Optional[int] = None,
-    max_seqlen_k: Optional[int] = None,
-    page_table: Optional[torch.Tensor] = None,
-    kv_batch_idx: Optional[torch.Tensor] = None,
-    leftpad_k: Optional[torch.Tensor] = None,
-    rotary_cos: Optional[torch.Tensor] = None,
-    rotary_sin: Optional[torch.Tensor] = None,
-    seqlens_rotary: Optional[torch.Tensor] = None,
-    q_descale: Optional[torch.Tensor] = None,
-    k_descale: Optional[torch.Tensor] = None,
-    v_descale: Optional[torch.Tensor] = None,
-    softmax_scale: Optional[float] = None,
-    causal: bool = False,
-    window_size_left: int = -1,
-    window_size_right: int = -1,
-    attention_chunk: int = 0,
-    softcap: float = 0.0,
-    rotary_interleaved: bool = True,
-    scheduler_metadata: Optional[torch.Tensor] = None,
-    num_splits: int = 1,
-    pack_gqa: Optional[bool] = None,
-    sm_margin: int = 0,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-    q, k, k_new, v_new = [maybe_contiguous(x) for x in (q, k, k_new, v_new)]
-    v = v.contiguous() if v.stride(-1) != 1 and v.stride(-3) != 1 else v
-    cu_seqlens_q, cu_seqlens_k, cu_seqlens_k_new = [
-        maybe_contiguous(x) for x in (cu_seqlens_q, cu_seqlens_k, cu_seqlens_k_new)
-    ]
-    seqused_q, seqused_k = [maybe_contiguous(x) for x in (seqused_q, seqused_k)]
-    page_table, kv_batch_idx, leftpad_k = [
-        maybe_contiguous(x) for x in (page_table, kv_batch_idx, leftpad_k)
-    ]
-    rotary_cos, rotary_sin = [maybe_contiguous(x) for x in (rotary_cos, rotary_sin)]
-    seqlens_rotary = maybe_contiguous(seqlens_rotary)
-    out, softmax_lse, out_accum, softmax_lse_accum = flash_attn_3_cuda.fwd(
-        q,
-        k,
-        v,
-        k_new,
-        v_new,
-        qv,
-        out_,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        cu_seqlens_k_new,
-        seqused_q,
-        seqused_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        page_table,
-        kv_batch_idx,
-        leftpad_k,
-        rotary_cos,
-        rotary_sin,
-        seqlens_rotary,
-        q_descale,
-        k_descale,
-        v_descale,
-        softmax_scale,
-        causal,
-        window_size_left,
-        window_size_right,
-        attention_chunk,
-        softcap,
-        rotary_interleaved,
-        scheduler_metadata,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    if out_accum is None:
-        out_accum = torch.tensor([], device=out.device)
-    if softmax_lse_accum is None:
-        softmax_lse_accum = torch.tensor([], device=out.device)
-    return out, softmax_lse, out_accum, softmax_lse_accum
-@torch.library.register_fake(add_op_namespace_prefix("_flash_attn_forward"))
-def _flash_attn_forward_fake(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    k_new: Optional[torch.Tensor] = None,
-    v_new: Optional[torch.Tensor] = None,
-    qv: Optional[torch.Tensor] = None,
-    out_: Optional[torch.Tensor] = None,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k: Optional[torch.Tensor] = None,
-    cu_seqlens_k_new: Optional[torch.Tensor] = None,
-    seqused_q: Optional[torch.Tensor] = None,
-    seqused_k: Optional[torch.Tensor] = None,
-    max_seqlen_q: Optional[int] = None,
-    max_seqlen_k: Optional[int] = None,
-    page_table: Optional[torch.Tensor] = None,
-    kv_batch_idx: Optional[torch.Tensor] = None,
-    leftpad_k: Optional[torch.Tensor] = None,
-    rotary_cos: Optional[torch.Tensor] = None,
-    rotary_sin: Optional[torch.Tensor] = None,
-    seqlens_rotary: Optional[torch.Tensor] = None,
-    q_descale: Optional[torch.Tensor] = None,
-    k_descale: Optional[torch.Tensor] = None,
-    v_descale: Optional[torch.Tensor] = None,
-    softmax_scale: Optional[float] = None,
-    causal: bool = False,
-    window_size_left: int = -1,
-    window_size_right: int = -1,
-    attention_chunk: int = 0,
-    softcap: float = 0.0,
-    rotary_interleaved: bool = True,
-    scheduler_metadata: Optional[torch.Tensor] = None,
-    num_splits: int = 1,
-    pack_gqa: Optional[bool] = None,
-    sm_margin: int = 0,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-    """
-    Symbolic fake implementation of flash attention forward.
-    Returns tensors with the correct shapes and dtypes without actual computation.
-    """
-    # Determine if we're in varlen mode
-    is_varlen_q = cu_seqlens_q is not None
-    # Get dimensions from query tensor
-    if is_varlen_q:
-        # varlen mode: q is (total_q, num_heads, head_size)
-        total_q, num_heads, head_size = q.shape
-        batch_size = cu_seqlens_q.shape[0] - 1
-        if max_seqlen_q is None:
-            raise ValueError("max_seqlen_q must be provided if cu_seqlens_q is provided")
-        seqlen_q = max_seqlen_q
-    else:
-        # batch mode: q is (batch_size, seqlen_q, num_heads, head_size)
-        batch_size, seqlen_q, num_heads, head_size = q.shape
-        total_q = batch_size * q.shape[1]
-    # Get value head dimension
-    head_size_v = v.shape[-1]
-    # Determine output dtype (FP8 inputs produce BF16 outputs)
-    q_type = q.dtype
-    if q_type == torch.float8_e4m3fn:
-        out_dtype = torch.bfloat16
-    else:
-        out_dtype = q_type
-    # Create output tensor
-    if out_ is not None:
-        # If out_ is provided, _flash_attn_forward becomes non-functional
-        raise TypeError("Tracing (torch.compile/torch.export) with pre-allocated output tensor is not supported.")
-    if is_varlen_q:
-        out = torch.empty((total_q, num_heads, head_size_v), dtype=out_dtype, device=q.device)
-    else:
-        out = torch.empty((batch_size, seqlen_q, num_heads, head_size_v), dtype=out_dtype, device=q.device)
-    # Create softmax_lse tensor
-    if is_varlen_q:
-        softmax_lse = torch.empty((num_heads, total_q), dtype=torch.float32, device=q.device)
-    else:
-        softmax_lse = torch.empty((batch_size, num_heads, seqlen_q), dtype=torch.float32, device=q.device)
-    # TODO(guilhermeleobas): Implement "get_num_splits"
-    # There's an heuristic to compute num_splits when "num_splits <= 0"
-    # assert that num_splits is > 0 for now
-    if num_splits <= 0:
-        raise ValueError(f"tracing (torch.compile/torch.export) with num_splits <= 0 not supported. Got {num_splits=}")
-    if num_splits > 1:
-        if is_varlen_q:
-            out_accum = torch.empty((num_splits, num_heads, total_q, head_size_v), dtype=torch.float32, device=q.device)
-            softmax_lse_accum = torch.empty((num_splits, num_heads, total_q), dtype=torch.float32, device=q.device)
-        else:
-            out_accum = torch.empty((num_splits, batch_size, num_heads, seqlen_q, head_size_v), dtype=torch.float32, device=q.device)
-            softmax_lse_accum = torch.empty((num_splits, batch_size, num_heads, seqlen_q), dtype=torch.float32, device=q.device)
-    else:
-        # Tensors are not set when num_splits < 1
-        out_accum = torch.tensor([], device=out.device)
-        softmax_lse_accum = torch.tensor([], device=out.device)
-    return out, softmax_lse, out_accum, softmax_lse_accum
-@torch.library.custom_op(add_op_namespace_prefix("_flash_attn_backward"), mutates_args=("dq", "dk", "dv"), device_types="cuda")
-def _flash_attn_backward(
-    dout: torch.Tensor,
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    out: torch.Tensor,
-    softmax_lse: torch.Tensor,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k: Optional[torch.Tensor] = None,
-    sequed_q: Optional[torch.Tensor] = None,
-    sequed_k: Optional[torch.Tensor] = None,
-    max_seqlen_q: Optional[int] = None,
-    max_seqlen_k: Optional[int] = None,
-    dq: Optional[torch.Tensor] = None,
-    dk: Optional[torch.Tensor] = None,
-    dv: Optional[torch.Tensor] = None,
-    softmax_scale: Optional[float] = None,
-    is_causal: bool = False,
-    window_size_left: int = -1,
-    window_size_right: int = -1,
-    softcap: float = 0.0,
-    deterministic: bool = False,
-    sm_margin: int = 0,
-) -> torch.Tensor:
-    # dq, dk, dv are allocated by us so they should already be contiguous
-    dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)]
-    softmax_d, *rest = flash_attn_3_cuda.bwd(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        dq,
-        dk,
-        dv,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        sequed_q,
-        sequed_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        softmax_scale,
-        is_causal,
-        window_size_left,
-        window_size_right,
-        softcap,
-        deterministic,
-        sm_margin,
-    )
-    return softmax_d
-@torch.library.register_fake(add_op_namespace_prefix("_flash_attn_backward"))
-def _flash_attn_backward_fake(
-    dout: torch.Tensor,
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    out: torch.Tensor,
-    softmax_lse: torch.Tensor,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k: Optional[torch.Tensor] = None,
-    sequed_q: Optional[torch.Tensor] = None,
-    sequed_k: Optional[torch.Tensor] = None,
-    max_seqlen_q: Optional[int] = None,
-    max_seqlen_k: Optional[int] = None,
-    dq: Optional[torch.Tensor] = None,
-    dk: Optional[torch.Tensor] = None,
-    dv: Optional[torch.Tensor] = None,
-    softmax_scale: Optional[float] = None,
-    is_causal: bool = False,
-    window_size_left: int = -1,
-    window_size_right: int = -1,
-    softcap: float = 0.0,
-    deterministic: bool = False,
-    sm_margin: int = 0,
-) -> torch.Tensor:
-    is_varlen_q = cu_seqlens_q is not None
-    is_varlen_k = cu_seqlens_q is not None
-    is_varlen = is_varlen_q or is_varlen_k or sequed_q is not None or sequed_k is not None
-    if not is_varlen_q:
-        batch_size = q.size(0)
-        seqlen_q = q.size(1)
-        seqlen_k = k.size(1)
-        total_q = batch_size * q.size(1)
-    else:
-        batch_size = cu_seqlens_q.size(0) - 1
-        total_q = q.size(0)
-        seqlen_q = max_seqlen_q
-        seqlen_k = max_seqlen_k
-    if window_size_left >= seqlen_k - 1:
-        window_size_left = -1
-    if window_size_right >= seqlen_q - 1:
-        window_size_right = -1
-    if is_causal:
-        window_size_right = 0
-    is_causal = window_size_left < 0 and window_size_right == 0
-    head_size = q.size(-1)
-    head_size_v = v.size(-1)
-    head_size_rounded = round_up_headdim(max(head_size, head_size_v))
-    # Hopper gpus uses cuda compute capabilities 9.0
-    cap = torch.cuda.get_device_capability(q.device)
-    arch = cap[0] * 10 + cap[1]
-    is_local = (window_size_left >= 0 or window_size_right >= 0) and not is_causal
-    if head_size_rounded <= 64:
-        kBlockM_sm90 = 96 if (is_causal and softcap > 0.0) else 128
-    elif head_size_rounded <= 96:
-        kBlockM_sm90 = 64
-    elif head_size_rounded <= 128:
-        kBlockM_sm90 = 64 if (is_causal or is_local or softcap > 0.0) else 80
-    else:
-        kBlockM_sm90 = 64
-    kBlockM_sm80 = 128 if head_size_rounded <= 64 else 64
-    kBlockM_sm86 = 64 if head_size_rounded <= 192 else 32
-    if arch >= 90:
-        kBlockM = kBlockM_sm90
-    elif arch == 86 or arch == 89:
-        kBlockM = kBlockM_sm86
-    else:
-        kBlockM = kBlockM_sm80
-    num_heads = q.shape[-2]
-    seqlen_q_rounded = round_multiple(seqlen_q, kBlockM)
-    total_q_padded_rounded = round_multiple(total_q + batch_size * kBlockM, kBlockM)
-    dq = torch.empty_like(q) if dq is None else dq
-    dk = torch.empty_like(k) if dk is None else dk
-    dv = torch.empty_like(v) if dv is None else dv
-    if not is_varlen:
-        softmax_d = torch.empty((batch_size, num_heads, seqlen_q_rounded), dtype=torch.float32, device=q.device)
-    else:
-        softmax_d = torch.empty((num_heads, total_q_padded_rounded), dtype=torch.float32, device=q.device)
-    return softmax_d
-def setup_context(ctx, inputs, output):
-    q, k, v = inputs[:3]
-    out, softmax_lse, _, _ = output
-    ctx.save_for_backward(q, k, v, out, softmax_lse)
-    ctx.softmax_scale = inputs[-11]
-    ctx.causal = inputs[-10]
-    ctx.window_size = [inputs[-9], inputs[-8]]
-    ctx.attention_chunk = inputs[-7]
-    ctx.softcap = inputs[-6]
-    ctx.sm_margin = inputs[-1]
-def _backward(ctx, dout, *grads):
-    q, k, v, out, softmax_lse = ctx.saved_tensors
-    dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
-    _flash_attn_backward(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        None, None, # cu_seqlens_q, cu_seqlens_k,
-        None, None, # sequed_q, sequed_k,
-        None, None, # max_seqlen_q, max_seqlen_k,
-        dq,
-        dk,
-        dv,
-        ctx.softmax_scale,
-        ctx.causal,
-        ctx.window_size[0],
-        ctx.window_size[1],
-        ctx.softcap,
-        False, # deterministic
-        ctx.sm_margin,
-    )
-    return dq, dk, dv, *((None,) * 21)
-_flash_attn_forward.register_autograd(_backward, setup_context=setup_context)
-class FlashAttnQKVPackedFunc(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        qkv,
-        softmax_scale,
-        causal,
-        q_descale=None, k_descale=None, v_descale=None,
-        window_size=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        deterministic=False,
-        num_heads_q=None,
-        sm_margin=0,
-        return_softmax=False,
-    ):
-        if softmax_scale is None:
-            softmax_scale = qkv.shape[-1] ** (-0.5)
-        if qkv.dim() == 5:
-            assert qkv.shape[-3] == 3
-            q, k, v = qkv.unbind(dim=-3)
-        else:
-            assert qkv.dim() == 4
-            assert num_heads_q is not None
-            num_heads_k = (qkv.shape[2] - num_heads_q) // 2
-            assert num_heads_k * 2 + num_heads_q == qkv.shape[2]
-            q, k, v = qkv.split([num_heads_q, num_heads_k, num_heads_k], dim=-2)
-        out, softmax_lse, *rest = _flash_attn_forward(
-            q,
-            k,
-            v,
-            None, None,  # k_new, v_new
-            None,  # qv
-            None,  # out
-            None, None, None,   # cu_seqlens_q/k/k_new
-            None, None,   # seqused_q/k
-            None, None,   # max_seqlen_q/k
-            None, None, None,   # page_table, kv_batch_idx, leftpad_k,
-            None, None, None,  # rotary_cos/sin, seqlens_rotary
-            q_descale, k_descale, v_descale,
-            softmax_scale,
-            causal=causal,
-            window_size_left=window_size[0],
-            window_size_right=window_size[1],
-            attention_chunk=attention_chunk,
-            softcap=softcap,
-            sm_margin=sm_margin,
-        )
-        # ctx.save_for_backward(q, k, v, out_padded, softmax_lse)
-        ctx.save_for_backward(q, k, v, out, softmax_lse)
-        ctx.softmax_scale = softmax_scale
-        ctx.causal = causal
-        ctx.window_size = window_size
-        ctx.attention_chunk = attention_chunk
-        ctx.softcap = softcap
-        ctx.deterministic = deterministic
-        ctx.ndim = qkv.dim()
-        ctx.sm_margin = sm_margin
-        return (out, softmax_lse) if return_softmax else out
-    @staticmethod
-    def backward(ctx, dout, *args):
-        q, k, v, out, softmax_lse = ctx.saved_tensors
-        assert ctx.attention_chunk == 0, "FA3 backward does not support attention_chunk"
-        if ctx.ndim == 5:
-            qkv_shape = q.shape[:-2] + (3, *q.shape[-2:])
-            dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device)
-            dq, dk, dv = dqkv.unbind(dim=-3)
-        else:
-            num_heads_q = q.shape[2]
-            num_heads_k = k.shape[2]
-            qkv_shape = q.shape[:-2] + (num_heads_q + num_heads_k * 2, *q.shape[-1:])
-            dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device)
-            dq, dk, dv = dqkv.split([num_heads_q, num_heads_k, num_heads_k], dim=-2)
-        _flash_attn_backward(
-            dout,
-            q,
-            k,
-            v,
-            out,
-            softmax_lse,
-            None, None, # cu_seqlens_q, cu_seqlens_k,
-            None, None, # sequed_q, sequed_k,
-            None, None, # max_seqlen_q, max_seqlen_k,
-            dq,
-            dk,
-            dv,
-            ctx.softmax_scale,
-            ctx.causal,
-            ctx.window_size[0],
-            ctx.window_size[1],
-            ctx.softcap,
-            ctx.deterministic,
-            ctx.sm_margin,
-        )
-        dqkv = dqkv[..., : dout.shape[-1]]  # We could have padded the head dimension
-        return dqkv, None, None, None, None, None, None, None, None, None, None, None, None
-class FlashAttnFunc(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        q,
-        k,
-        v,
-        softmax_scale,
-        causal,
-        qv=None,
-        q_descale=None, k_descale=None, v_descale=None,
-        window_size=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        num_splits=1,
-        pack_gqa=None,
-        deterministic=False,
-        sm_margin=0,
-        return_softmax=False,
-    ):
-        if softmax_scale is None:
-            softmax_scale = (q.shape[-1] + (qv.shape[-1] if qv is not None else 0)) ** (-0.5)
-        # out, q, k, v, out_padded, softmax_lse = _flash_attn_forward(
-        out, softmax_lse, *rest = _flash_attn_forward(
-            q,
-            k,
-            v,
-            None, None,  # k_new, v_new
-            qv,  # qv
-            None,  # out
-            None, None, None,   # cu_seqlens_q/k/k_new
-            None, None,   # seqused_q/k
-            None, None,   # max_seqlen_q/k
-            None, None, None,   # page_table, kv_batch_idx, leftpad_k,
-            None, None, None,  # rotary_cos/sin, seqlens_rotary
-            q_descale, k_descale, v_descale,
-            softmax_scale,
-            causal=causal,
-            window_size_left=window_size[0],
-            window_size_right=window_size[1],
-            attention_chunk=attention_chunk,
-            softcap=softcap,
-            num_splits=num_splits,
-            pack_gqa=pack_gqa,
-            sm_margin=sm_margin,
-        )
-        # ctx.save_for_backward(q, k, v, out_padded, softmax_lse)
-        ctx.save_for_backward(q, k, v, out, softmax_lse)
-        ctx.softmax_scale = softmax_scale
-        ctx.causal = causal
-        ctx.window_size = window_size
-        ctx.attention_chunk = attention_chunk
-        ctx.softcap = softcap
-        ctx.deterministic = deterministic
-        ctx.sm_margin = sm_margin
-        return (out, softmax_lse) if return_softmax else out
-    @staticmethod
-    def backward(ctx, dout, *args):
-        q, k, v, out, softmax_lse = ctx.saved_tensors
-        assert ctx.attention_chunk == 0, "FA3 backward does not support attention_chunk"
-        dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
-        _flash_attn_backward(
-            dout,
-            q,
-            k,
-            v,
-            out,
-            softmax_lse,
-            None, None, # cu_seqlens_q, cu_seqlens_k,
-            None, None, # sequed_q, sequed_k,
-            None, None, # max_seqlen_q, max_seqlen_k,
-            dq,
-            dk,
-            dv,
-            ctx.softmax_scale,
-            ctx.causal,
-            ctx.window_size[0],
-            ctx.window_size[1],
-            ctx.softcap,
-            ctx.deterministic,
-            ctx.sm_margin,
-        )
-        dq = dq[..., : q.shape[-1]]  # We could have padded the head dimension
-        dk = dk[..., : k.shape[-1]]
-        dv = dv[..., : v.shape[-1]]
-        return dq, dk, dv, None, None, None, None, None, None, None, None, None, None, None, None, None, None
-class FlashAttnVarlenFunc(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        q,
-        k,
-        v,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        seqused_q,
-        seqused_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        softmax_scale,
-        causal,
-        qv=None,
-        q_descale=None, k_descale=None, v_descale=None,
-        window_size=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        num_splits=1,
-        pack_gqa=None,
-        deterministic=False,
-        sm_margin=0,
-        return_softmax=False,
-    ):
-        if softmax_scale is None:
-            softmax_scale = (q.shape[-1] + (qv.shape[-1] if qv is not None else 0)) ** (-0.5)
-        # out, q, k, v, out_padded, softmax_lse = _flash_attn_varlen_forward(
-        out, softmax_lse, *rest = _flash_attn_forward(
-            q,
-            k,
-            v,
-            None, None,  # k_new, v_new
-            qv,  # qv
-            None,  # out
-            cu_seqlens_q,
-            cu_seqlens_k,
-            None,   # cu_seqlens_k_new
-            seqused_q,
-            seqused_k,
-            max_seqlen_q,
-            max_seqlen_k,
-            None, None, None,   # page_table, kv_batch_idx, leftpad_k,
-            None, None, None,  # rotary_cos/sin, seqlens_rotary
-            q_descale, k_descale, v_descale,
-            softmax_scale,
-            causal=causal,
-            window_size_left=window_size[0],
-            window_size_right=window_size[1],
-            attention_chunk=attention_chunk,
-            softcap=softcap,
-            num_splits=num_splits,
-            pack_gqa=pack_gqa,
-            sm_margin=sm_margin,
-        )
-        # ctx.save_for_backward(q, k, v, out_padded, softmax_lse, cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k)
-        ctx.save_for_backward(q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k)
-        ctx.max_seqlen_q = max_seqlen_q
-        ctx.max_seqlen_k = max_seqlen_k
-        ctx.softmax_scale = softmax_scale
-        ctx.causal = causal
-        ctx.window_size = window_size
-        ctx.attention_chunk = attention_chunk
-        ctx.softcap = softcap
-        ctx.deterministic = deterministic
-        ctx.sm_margin = sm_margin
-        return (out, softmax_lse) if return_softmax else out
-    @staticmethod
-    def backward(ctx, dout, *args):
-        q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k = ctx.saved_tensors
-        assert ctx.attention_chunk == 0, "FA3 backward does not support attention_chunk"
-        dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
-        _flash_attn_backward(
-            dout,
-            q,
-            k,
-            v,
-            out,
-            softmax_lse,
-            cu_seqlens_q,
-            cu_seqlens_k,
-            seqused_q,
-            seqused_k,
-            ctx.max_seqlen_q,
-            ctx.max_seqlen_k,
-            dq,
-            dk,
-            dv,
-            ctx.softmax_scale,
-            ctx.causal,
-            ctx.window_size[0],
-            ctx.window_size[1],
-            ctx.softcap,
-            ctx.deterministic,
-            ctx.sm_margin,
-        )
-        dq = dq[..., : q.shape[-1]]  # We could have padded the head dimension
-        dk = dk[..., : k.shape[-1]]
-        dv = dv[..., : v.shape[-1]]
-        return dq, dk, dv, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None
-def flash_attn_qkvpacked_func(
-    qkv,
-    softmax_scale=None,
-    causal=False,
-    q_descale=None, k_descale=None, v_descale=None,
-    window_size=(-1, -1),
-    attention_chunk=0,
-    softcap=0.0,
-    deterministic=False,
-    num_heads_q=None,
-    sm_margin=0,
-    return_attn_probs=False,
-):
-    """dropout_p should be set to 0.0 during evaluation
-    If Q, K, V are already stacked into 1 tensor, this function will be faster than
-    calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation
-    of the gradients of Q, K, V.
-    For multi-query and grouped-query attention (MQA/GQA), please see
-    flash_attn_kvpacked_func and flash_attn_func.
-    If window_size != (-1, -1), implements sliding window local attention. Query at position i
-    will only attend to keys between [i - window_size[0], i + window_size[1]] inclusive.
-    Arguments:
-        qkv: (batch_size, seqlen, 3, nheads, headdim)
-        dropout_p: float. Dropout probability.
-        softmax_scale: float. The scaling of QK^T before applying softmax.
-            Default to 1 / sqrt(headdim).
-        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
-        window_size: (left, right). If not (-1, -1), implements sliding window local attention.
-        softcap: float. Anything > 0 activates softcapping attention.
-        alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of (-alibi_slope * |i - j|) is added to
-            the attention score of query i and key j.
-        deterministic: bool. Whether to use the deterministic implementation of the backward pass,
-            which is slightly slower and uses more memory. The forward pass is always deterministic.
-        return_attn_probs: bool. Whether to return the attention probabilities. This option is for
-           testing only. The returned probabilities are not guaranteed to be correct
-           (they might not have the right scaling).
-    Return:
-        out: (batch_size, seqlen, nheads, headdim).
-        softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
-            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
-            normalization factor).
-        S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).
-            The output of softmax (possibly with different scaling). It also encodes the dropout
-            pattern (negative means that location was dropped, nonnegative means it was kept).
-    """
-    return FlashAttnQKVPackedFunc.apply(
-        qkv,
-        softmax_scale,
-        causal,
-        q_descale, k_descale, v_descale,
-        window_size,
-        attention_chunk,
-        softcap,
-        deterministic,
-        num_heads_q,
-        sm_margin,
-        return_attn_probs,
-    )
-def flash_attn_func(
-    q,
-    k,
-    v,
-    softmax_scale=None,
-    causal=False,
-    qv=None,
-    q_descale=None, k_descale=None, v_descale=None,
-    window_size=(-1, -1),
-    attention_chunk=0,
-    softcap=0.0,
-    num_splits=1,
-    pack_gqa=None,
-    deterministic=False,
-    sm_margin=0,
-    return_attn_probs=False,
-):
-    """dropout_p should be set to 0.0 during evaluation
-    Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
-    than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
-    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
-    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
-    If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
-    For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
-        1 1 1 1 0
-        1 1 1 1 1
-    If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
-        0 0
-        0 0
-        0 0
-        1 0
-        1 1
-    If the row of the mask is all zero, the output will be zero.
-    If window_size != (-1, -1), implements sliding window local attention. Query at position i
-    will only attend to keys between
-    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
-    Arguments:
-        q: (batch_size, seqlen, nheads, headdim)
-        k: (batch_size, seqlen, nheads_k, headdim)
-        v: (batch_size, seqlen, nheads_k, headdim)
-        dropout_p: float. Dropout probability.
-        softmax_scale: float. The scaling of QK^T before applying softmax.
-            Default to 1 / sqrt(headdim).
-        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
-        window_size: (left, right). If not (-1, -1), implements sliding window local attention.
-        alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
-            (-alibi_slope * |i + seqlen_k - seqlen_q - j|)
-            is added to the attention score of query i and key j.
-        deterministic: bool. Whether to use the deterministic implementation of the backward pass,
-            which is slightly slower and uses more memory. The forward pass is always deterministic.
-        return_attn_probs: bool. Whether to return the attention probabilities. This option is for
-           testing only. The returned probabilities are not guaranteed to be correct
-           (they might not have the right scaling).
-    Return:
-        out: (batch_size, seqlen, nheads, headdim).
-        softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
-            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
-            normalization factor).
-    """
-    return FlashAttnFunc.apply(
-        q,
-        k,
-        v,
-        softmax_scale,
-        causal,
-        qv,
-        q_descale, k_descale, v_descale,
-        window_size,
-        attention_chunk,
-        softcap,
-        num_splits,
-        pack_gqa,
-        deterministic,
-        sm_margin,
-        return_attn_probs,
-    )
-def flash_attn_varlen_func(
-    q,
-    k,
-    v,
-    cu_seqlens_q,
-    cu_seqlens_k,
-    max_seqlen_q,
-    max_seqlen_k,
-    seqused_q=None,
-    seqused_k=None,
-    softmax_scale=None,
-    causal=False,
-    qv=None,
-    q_descale=None, k_descale=None, v_descale=None,
-    window_size=(-1, -1),
-    attention_chunk=0,
-    softcap=0.0,
-    num_splits=1,
-    pack_gqa=None,
-    deterministic=False,
-    sm_margin=0,
-    return_attn_probs=False,
-):
-    return FlashAttnVarlenFunc.apply(
-        q,
-        k,
-        v,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        seqused_q,
-        seqused_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        softmax_scale,
-        causal,
-        qv,
-        q_descale, k_descale, v_descale,
-        window_size,
-        attention_chunk,
-        softcap,
-        num_splits,
-        pack_gqa,
-        deterministic,
-        sm_margin,
-        return_attn_probs,
-    )
-def flash_attn_combine(out_partial, lse_partial, out=None, out_dtype=None):
-    return flash_attn_3_cuda.fwd_combine(out_partial, lse_partial, out, out_dtype)
-def flash_attn_with_kvcache(
-    q,
-    k_cache,
-    v_cache,
-    k=None,
-    v=None,
-    qv=None,
-    rotary_cos=None,
-    rotary_sin=None,
-    cache_seqlens: Optional[Union[(int, torch.Tensor)]] = None,
-    cache_batch_idx: Optional[torch.Tensor] = None,
-    cache_leftpad: Optional[torch.Tensor] = None,
-    page_table: Optional[torch.Tensor] = None,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k_new: Optional[torch.Tensor] = None,
-    max_seqlen_q: Optional[int] = None,
-    rotary_seqlens: Optional[torch.Tensor] = None,
-    q_descale: Optional[torch.Tensor] = None,
-    k_descale: Optional[torch.Tensor] = None,
-    v_descale: Optional[torch.Tensor] = None,
-    softmax_scale=None,
-    causal=False,
-    window_size=(-1, -1),  # -1 means infinite context window
-    attention_chunk=0,
-    softcap=0.0, # 0.0 means deactivated
-    rotary_interleaved=True,
-    scheduler_metadata=None,
-    num_splits=0,    # Can be tuned for speed
-    pack_gqa=None,   # Can be tuned for speed
-    sm_margin=0,     # Can be tuned if some SMs are used for communication
-    return_softmax_lse=False,
-):
-    """
-    If k and v are not None, k_cache and v_cache will be updated *inplace* with the new values from
-    k and v. This is useful for incremental decoding: you can pass in the cached keys/values from
-    the previous step, and update them with the new keys/values from the current step, and do
-    attention with the updated cache, all in 1 kernel.
-    If you pass in k / v, you must make sure that the cache is large enough to hold the new values.
-    For example, the KV cache could be pre-allocated with the max sequence length, and you can use
-    cache_seqlens to keep track of the current sequence lengths of each sequence in the batch.
-    Also apply rotary embedding if rotary_cos and rotary_sin are passed in. The key @k will be
-    rotated by rotary_cos and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
-    If causal or local (i.e., window_size != (-1, -1)), the query @q will be rotated by rotary_cos
-    and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
-    If not causal and not local, the query @q will be rotated by rotary_cos and rotary_sin at
-    indices cache_seqlens only (i.e. we consider all tokens in @q to be at position cache_seqlens).
-    See tests/test_flash_attn.py::test_flash_attn_kvcache for examples of how to use this function.
-    Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
-    than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
-    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
-    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
-    If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
-    For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
-        1 1 1 1 0
-        1 1 1 1 1
-    If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
-        0 0
-        0 0
-        0 0
-        1 0
-        1 1
-    If the row of the mask is all zero, the output will be zero.
-    If window_size != (-1, -1), implements sliding window local attention. Query at position i
-    will only attend to keys between
-    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
-    Note: Does not support backward pass.
-    Arguments:
-        q: (batch_size, seqlen, nheads, headdim)
-        k_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim) if there's no page_table,
-            or (num_blocks, page_block_size, nheads_k, headdim) if there's a page_table (i.e. paged KV cache)
-            page_block_size can be arbitrary (e.g, 1, 2, 3, 64, etc.).
-        v_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim_v) if there's no page_table,
-            or (num_blocks, page_block_size, nheads_k, headdim_v) if there's a page_table (i.e. paged KV cache)
-        k [optional]: (batch_size, seqlen_new, nheads_k, headdim). If not None, we concatenate
-            k with k_cache, starting at the indices specified by cache_seqlens.
-        v [optional]: (batch_size, seqlen_new, nheads_k, headdim_v). Similar to k.
-        qv [optional]: (batch_size, seqlen, nheads, headdim_v)
-        rotary_cos [optional]: (seqlen_ro, rotary_dim / 2). If not None, we apply rotary embedding
-            to k and q. Only applicable if k and v are passed in. rotary_dim must be divisible by 16.
-        rotary_sin [optional]: (seqlen_ro, rotary_dim / 2). Similar to rotary_cos.
-        cache_seqlens: int, or (batch_size,), dtype torch.int32. The sequence lengths of the
-            KV cache.
-        cache_batch_idx: (batch_size,), dtype torch.int32. The indices used to index into the KV cache.
-            If None, we assume that the batch indices are [0, 1, 2, ..., batch_size - 1].
-            If the indices are not distinct, and k and v are provided, the values updated in the cache
-                 might come from any of the duplicate indices.
-        cache_leftpad: (batch_size,), dtype torch.int32. The index that the KV cache starts. If None, assume 0.
-        page_table [optional]: (batch_size, max_num_blocks_per_seq), dtype torch.int32.
-        softmax_scale: float. The scaling of QK^T before applying softmax.
-            Default to 1 / sqrt(headdim).
-        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
-        window_size: (left, right). If not (-1, -1), implements sliding window local attention.
-        softcap: float. Anything > 0 activates softcapping attention.
-        rotary_interleaved: bool. Only applicable if rotary_cos and rotary_sin are passed in.
-            If True, rotary embedding will combine dimensions 0 & 1, 2 & 3, etc. If False,
-            rotary embedding will combine dimensions 0 & rotary_dim / 2, 1 & rotary_dim / 2 + 1
-            (i.e. GPT-NeoX style).
-        num_splits: int. If > 1, split the key/value into this many chunks along the sequence.
-           If num_splits == 1, we don't split the key/value. If num_splits == 0, we use a heuristic
-           to automatically determine the number of splits.
-           Don't change this unless you know what you are doing.
-        return_softmax_lse: bool. Whether to return the logsumexp of the attention scores.
-    Return:
-        out: (batch_size, seqlen, nheads, headdim).
-        softmax_lse [optional, if return_softmax_lse=True]: (batch_size, nheads, seqlen). The
-            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
-            normalization factor).
-    """
-    assert k_cache.stride(-1) == 1, "k_cache must have contiguous last dimension"
-    assert v_cache.stride(-1) == 1, "v_cache must have contiguous last dimension"
-    if softmax_scale is None:
-        softmax_scale = (q.shape[-1] + (qv.shape[-1] if qv is not None else 0)) ** (-0.5)
-    if cache_seqlens is not None and isinstance(cache_seqlens, int):
-        cache_seqlens = torch.full(
-            (q.shape[0],), cache_seqlens, dtype=torch.int32, device=k_cache.device
-        )
-        cache_seqlens = maybe_contiguous(cache_seqlens)
-    out, softmax_lse, *rest = _flash_attn_forward(
-        q,
-        k_cache,
-        v_cache,
-        k,
-        v,
-        qv,
-        None,  # out
-        cu_seqlens_q,
-        None,  # cu_seqlens_k
-        cu_seqlens_k_new,
-        None,  # seqused_q
-        cache_seqlens,
-        max_seqlen_q,
-        None,  # max_seqlen_k
-        page_table,
-        cache_batch_idx,
-        cache_leftpad,
-        rotary_cos,
-        rotary_sin,
-        rotary_seqlens,
-        q_descale, k_descale, v_descale,
-        softmax_scale,
-        causal=causal,
-        window_size_left=window_size[0],
-        window_size_right=window_size[1],
-        attention_chunk=attention_chunk,
-        softcap=softcap,
-        rotary_interleaved=rotary_interleaved,
-        scheduler_metadata=scheduler_metadata,
-        num_splits=num_splits,
-        pack_gqa=pack_gqa,
-        sm_margin=sm_margin,
-    )
-    # return (out, softmax_lse) if return_softmax_lse else out
-    return (out, softmax_lse, *rest) if return_softmax_lse else out
-def get_scheduler_metadata(
-    batch_size, max_seqlen_q, max_seqlen_k, num_heads_q, num_heads_kv, headdim,
-    cache_seqlens: torch.Tensor,
-    qkv_dtype=torch.bfloat16,
-    headdim_v=None,
-    cu_seqlens_q: Optional[torch.Tensor] = None,
-    cu_seqlens_k_new: Optional[torch.Tensor] = None,
-    cache_leftpad: Optional[torch.Tensor] = None,
-    page_size: Optional[int] = None,
-    max_seqlen_k_new=0,
-    causal=False,
-    window_size=(-1, -1),  # -1 means infinite context window
-    attention_chunk=0,
-    has_softcap=False,
-    num_splits=0,    # Can be tuned for speed
-    pack_gqa=None,   # Can be tuned for speed
-    sm_margin=0,     # Can be tuned if some SMs are used for communication
-):
-    cache_seqlens = maybe_contiguous(cache_seqlens)
-    if headdim_v is None:
-        headdim_v = headdim
-    scheduler_metadata = flash_attn_3_cuda.get_scheduler_metadata(
-        batch_size, max_seqlen_q, max_seqlen_k, num_heads_q, num_heads_kv, headdim, headdim_v,
-        qkv_dtype,
-        cache_seqlens,
-        cu_seqlens_q,
-        None,  # cu_seqlens_k
-        cu_seqlens_k_new,
-        None,  # seqused_q
-        cache_leftpad,
-        page_size,
-        max_seqlen_k_new,
-        causal,
-        window_size[0], window_size[1],
-        attention_chunk,
-        has_softcap,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    return scheduler_metadata

build/torch-stable-abi210-cu130-x86_64-linux/metadata.json DELETED Viewed

@@ -1,25 +0,0 @@
-{
-  "name": "flash-attn3",
-  "id": "_flash_attn3_cuda_477ab85",
-  "version": 1,
-  "license": "BSD-3-Clause",
-  "python-depends": [],
-  "backend": {
-    "type": "cuda",
-    "archs": [
-      "8.0",
-      "9.0a"
-    ]
-  },
-  "digest": {
-    "algorithm": "sha256",
-    "files": {
-      "__init__.py": "KXVmQJM+KhWc2UqWJesupCaP+mqAvTx7yGueFATglHE=",
-      "_flash_attn3_cuda_477ab85.abi3.so": "p5w4yjy/X2Yw6W6xq2Aishvn2T4qokYdDac0Gg8kPzA=",
-      "_ops.py": "hccJrV95SPARE3ynEBHpwny/YFXZqpZKdGdbTTnKTsc=",
-      "flash_attn3/__init__.py": "DFYPlrhXwYjEqCl/8n0SmWGZV8NFml5DPhMjKfv98GY=",
-      "flash_attn_config.py": "uxo+eyMcDit//8YDaS4Rtk5BTMKzTSK9tOKTjdWVe/w=",
-      "flash_attn_interface.py": "y0bD2JYGMFgX6iDre3zJN2sBPuq/12N6Mo55Z+HQCNQ="
-    }
-  }
-}

build/torch-stable-abi210-cu130-x86_64-linux/metadata.json.sigstore DELETED Viewed

@@ -1 +0,0 @@

- 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