Remove old flash-attn3 builds

build/torch26-cxx11-cu124-x86_64-linux/flash_attn3/__init__.py

@@ -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/torch26-cxx11-cu124-x86_64-linux/flash_attn3/_flash_attn3_2e75662.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:21b44e8e5e447a8b8ee051d347f0e32a3446a750f79d0bd1755e553f2119aa3b
-size 838459656

build/torch26-cxx11-cu124-x86_64-linux/flash_attn3/_flash_attn3_557701f.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:12d4ff964085fd02252777a2008f5ca47c90ea6a93da590e2fc5065dd5330207
-size 838459656

build/torch26-cxx11-cu124-x86_64-linux/flash_attn3/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _flash_attn3_557701f
-ops = torch.ops._flash_attn3_557701f
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_flash_attn3_557701f::{op_name}"

build/torch26-cxx11-cu124-x86_64-linux/flash_attn3/flash_attn_interface.py

@@ -1,828 +0,0 @@
-# Copyright (c) 2023, Tri Dao.
-
-from typing import Optional, Union
-
-import torch
-import torch.nn as nn
-
-from ._ops import ops as flash_attn_3_cuda
-
-def maybe_contiguous(x):
-    return x.contiguous() if x is not None and x.stride(-1) != 1 else x
-
-
-def _flash_attn_forward(
-        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=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        rotary_interleaved=True,
-        scheduler_metadata=None,
-        num_splits=1,
-        pack_gqa=None,
-        sm_margin=0):
-    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, *rest = 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[0],
-        window_size[1],
-        attention_chunk,
-        softcap,
-        rotary_interleaved,
-        scheduler_metadata,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    return out, softmax_lse, *rest
-
-
-def _flash_attn_backward(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        sequed_q,
-        sequed_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        dq,
-        dk,
-        dv,
-        softmax_scale,
-        causal,
-        window_size=(-1, -1),
-        softcap=0.0,
-        deterministic=False,
-        sm_margin=0,
-):
-    # 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)]
-    dq, dk, dv, 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,
-        causal,
-        window_size[0],
-        window_size[1],
-        softcap,
-        deterministic,
-        sm_margin,
-    )
-    return dq, dk, dv, softmax_d
-
-
-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,
-    ):
-        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=window_size,
-            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
-        return 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,
-            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
-
-
-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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-):
-    """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,
-    )
-
-
-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,
-):
-    """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,
-    )
-
-
-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 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,
-    )
-
-
-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 must be a multiple of 256.
-        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(
-            (k_cache.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=window_size,
-        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/torch26-cxx11-cu126-x86_64-linux/flash_attn3/__init__.py

@@ -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/torch26-cxx11-cu126-x86_64-linux/flash_attn3/_flash_attn3_2e75662.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:21b44e8e5e447a8b8ee051d347f0e32a3446a750f79d0bd1755e553f2119aa3b
-size 838459656

build/torch26-cxx11-cu126-x86_64-linux/flash_attn3/_flash_attn3_557701f.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:12d4ff964085fd02252777a2008f5ca47c90ea6a93da590e2fc5065dd5330207
-size 838459656

build/torch26-cxx11-cu126-x86_64-linux/flash_attn3/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _flash_attn3_557701f
-ops = torch.ops._flash_attn3_557701f
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_flash_attn3_557701f::{op_name}"

build/torch26-cxx11-cu126-x86_64-linux/flash_attn3/flash_attn_interface.py

@@ -1,828 +0,0 @@
-# Copyright (c) 2023, Tri Dao.
-
-from typing import Optional, Union
-
-import torch
-import torch.nn as nn
-
-from ._ops import ops as flash_attn_3_cuda
-
-def maybe_contiguous(x):
-    return x.contiguous() if x is not None and x.stride(-1) != 1 else x
-
-
-def _flash_attn_forward(
-        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=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        rotary_interleaved=True,
-        scheduler_metadata=None,
-        num_splits=1,
-        pack_gqa=None,
-        sm_margin=0):
-    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, *rest = 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[0],
-        window_size[1],
-        attention_chunk,
-        softcap,
-        rotary_interleaved,
-        scheduler_metadata,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    return out, softmax_lse, *rest
-
-
-def _flash_attn_backward(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        sequed_q,
-        sequed_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        dq,
-        dk,
-        dv,
-        softmax_scale,
-        causal,
-        window_size=(-1, -1),
-        softcap=0.0,
-        deterministic=False,
-        sm_margin=0,
-):
-    # 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)]
-    dq, dk, dv, 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,
-        causal,
-        window_size[0],
-        window_size[1],
-        softcap,
-        deterministic,
-        sm_margin,
-    )
-    return dq, dk, dv, softmax_d
-
-
-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,
-    ):
-        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=window_size,
-            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
-        return 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,
-            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
-
-
-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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-):
-    """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,
-    )
-
-
-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,
-):
-    """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,
-    )
-
-
-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 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,
-    )
-
-
-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 must be a multiple of 256.
-        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(
-            (k_cache.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=window_size,
-        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/torch26-cxx98-cu124-x86_64-linux/flash_attn3/__init__.py

@@ -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/torch26-cxx98-cu124-x86_64-linux/flash_attn3/_flash_attn3_2e75662.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9627e08ec8778d2a409a2a0477572edb3e03eaca2b45e7b4810ee0a9126d6547
-size 838456048

build/torch26-cxx98-cu124-x86_64-linux/flash_attn3/_flash_attn3_557701f.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:07fe025ba95671f6ff957991f74c66063bfb10ab6737641c88f88116c9f83718
-size 838456048

build/torch26-cxx98-cu124-x86_64-linux/flash_attn3/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _flash_attn3_557701f
-ops = torch.ops._flash_attn3_557701f
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_flash_attn3_557701f::{op_name}"

build/torch26-cxx98-cu124-x86_64-linux/flash_attn3/flash_attn_interface.py

@@ -1,828 +0,0 @@
-# Copyright (c) 2023, Tri Dao.
-
-from typing import Optional, Union
-
-import torch
-import torch.nn as nn
-
-from ._ops import ops as flash_attn_3_cuda
-
-def maybe_contiguous(x):
-    return x.contiguous() if x is not None and x.stride(-1) != 1 else x
-
-
-def _flash_attn_forward(
-        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=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        rotary_interleaved=True,
-        scheduler_metadata=None,
-        num_splits=1,
-        pack_gqa=None,
-        sm_margin=0):
-    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, *rest = 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[0],
-        window_size[1],
-        attention_chunk,
-        softcap,
-        rotary_interleaved,
-        scheduler_metadata,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    return out, softmax_lse, *rest
-
-
-def _flash_attn_backward(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        sequed_q,
-        sequed_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        dq,
-        dk,
-        dv,
-        softmax_scale,
-        causal,
-        window_size=(-1, -1),
-        softcap=0.0,
-        deterministic=False,
-        sm_margin=0,
-):
-    # 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)]
-    dq, dk, dv, 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,
-        causal,
-        window_size[0],
-        window_size[1],
-        softcap,
-        deterministic,
-        sm_margin,
-    )
-    return dq, dk, dv, softmax_d
-
-
-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,
-    ):
-        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=window_size,
-            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
-        return 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,
-            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
-
-
-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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-):
-    """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,
-    )
-
-
-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,
-):
-    """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,
-    )
-
-
-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 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,
-    )
-
-
-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 must be a multiple of 256.
-        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(
-            (k_cache.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=window_size,
-        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/torch26-cxx98-cu126-x86_64-linux/flash_attn3/__init__.py

@@ -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/torch26-cxx98-cu126-x86_64-linux/flash_attn3/_flash_attn3_2e75662.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9627e08ec8778d2a409a2a0477572edb3e03eaca2b45e7b4810ee0a9126d6547
-size 838456048

build/torch26-cxx98-cu126-x86_64-linux/flash_attn3/_flash_attn3_557701f.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:07fe025ba95671f6ff957991f74c66063bfb10ab6737641c88f88116c9f83718
-size 838456048

build/torch26-cxx98-cu126-x86_64-linux/flash_attn3/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _flash_attn3_557701f
-ops = torch.ops._flash_attn3_557701f
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_flash_attn3_557701f::{op_name}"

build/torch26-cxx98-cu126-x86_64-linux/flash_attn3/flash_attn_interface.py

@@ -1,828 +0,0 @@
-# Copyright (c) 2023, Tri Dao.
-
-from typing import Optional, Union
-
-import torch
-import torch.nn as nn
-
-from ._ops import ops as flash_attn_3_cuda
-
-def maybe_contiguous(x):
-    return x.contiguous() if x is not None and x.stride(-1) != 1 else x
-
-
-def _flash_attn_forward(
-        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=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        rotary_interleaved=True,
-        scheduler_metadata=None,
-        num_splits=1,
-        pack_gqa=None,
-        sm_margin=0):
-    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, *rest = 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[0],
-        window_size[1],
-        attention_chunk,
-        softcap,
-        rotary_interleaved,
-        scheduler_metadata,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    return out, softmax_lse, *rest
-
-
-def _flash_attn_backward(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        sequed_q,
-        sequed_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        dq,
-        dk,
-        dv,
-        softmax_scale,
-        causal,
-        window_size=(-1, -1),
-        softcap=0.0,
-        deterministic=False,
-        sm_margin=0,
-):
-    # 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)]
-    dq, dk, dv, 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,
-        causal,
-        window_size[0],
-        window_size[1],
-        softcap,
-        deterministic,
-        sm_margin,
-    )
-    return dq, dk, dv, softmax_d
-
-
-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,
-    ):
-        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=window_size,
-            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
-        return 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,
-            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
-
-
-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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-):
-    """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,
-    )
-
-
-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,
-):
-    """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,
-    )
-
-
-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 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,
-    )
-
-
-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 must be a multiple of 256.
-        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(
-            (k_cache.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=window_size,
-        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/torch27-cxx11-cu126-x86_64-linux/flash_attn3/__init__.py

@@ -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/torch27-cxx11-cu126-x86_64-linux/flash_attn3/_flash_attn3_2e75662.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c0302224ac29ba4773d926d4cb16c01c45a374c6dd61286aae1f423f2bf495ea
-size 838459544

build/torch27-cxx11-cu126-x86_64-linux/flash_attn3/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _flash_attn3_2e75662
-ops = torch.ops._flash_attn3_2e75662
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_flash_attn3_2e75662::{op_name}"

build/torch27-cxx11-cu126-x86_64-linux/flash_attn3/flash_attn_interface.py

@@ -1,828 +0,0 @@
-# Copyright (c) 2023, Tri Dao.
-
-from typing import Optional, Union
-
-import torch
-import torch.nn as nn
-
-from ._ops import ops as flash_attn_3_cuda
-
-def maybe_contiguous(x):
-    return x.contiguous() if x is not None and x.stride(-1) != 1 else x
-
-
-def _flash_attn_forward(
-        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=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        rotary_interleaved=True,
-        scheduler_metadata=None,
-        num_splits=1,
-        pack_gqa=None,
-        sm_margin=0):
-    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, *rest = 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[0],
-        window_size[1],
-        attention_chunk,
-        softcap,
-        rotary_interleaved,
-        scheduler_metadata,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    return out, softmax_lse, *rest
-
-
-def _flash_attn_backward(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        sequed_q,
-        sequed_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        dq,
-        dk,
-        dv,
-        softmax_scale,
-        causal,
-        window_size=(-1, -1),
-        softcap=0.0,
-        deterministic=False,
-        sm_margin=0,
-):
-    # 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)]
-    dq, dk, dv, 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,
-        causal,
-        window_size[0],
-        window_size[1],
-        softcap,
-        deterministic,
-        sm_margin,
-    )
-    return dq, dk, dv, softmax_d
-
-
-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,
-    ):
-        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=window_size,
-            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
-        return 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,
-            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
-
-
-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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-):
-    """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,
-    )
-
-
-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,
-):
-    """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,
-    )
-
-
-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 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,
-    )
-
-
-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 must be a multiple of 256.
-        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(
-            (k_cache.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=window_size,
-        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/torch27-cxx11-cu128-x86_64-linux/flash_attn3/__init__.py

@@ -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/torch27-cxx11-cu128-x86_64-linux/flash_attn3/_flash_attn3_2e75662.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c0302224ac29ba4773d926d4cb16c01c45a374c6dd61286aae1f423f2bf495ea
-size 838459544

build/torch27-cxx11-cu128-x86_64-linux/flash_attn3/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _flash_attn3_2e75662
-ops = torch.ops._flash_attn3_2e75662
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_flash_attn3_2e75662::{op_name}"

build/torch27-cxx11-cu128-x86_64-linux/flash_attn3/flash_attn_interface.py

@@ -1,828 +0,0 @@
-# Copyright (c) 2023, Tri Dao.
-
-from typing import Optional, Union
-
-import torch
-import torch.nn as nn
-
-from ._ops import ops as flash_attn_3_cuda
-
-def maybe_contiguous(x):
-    return x.contiguous() if x is not None and x.stride(-1) != 1 else x
-
-
-def _flash_attn_forward(
-        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=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        rotary_interleaved=True,
-        scheduler_metadata=None,
-        num_splits=1,
-        pack_gqa=None,
-        sm_margin=0):
-    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, *rest = 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[0],
-        window_size[1],
-        attention_chunk,
-        softcap,
-        rotary_interleaved,
-        scheduler_metadata,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    return out, softmax_lse, *rest
-
-
-def _flash_attn_backward(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        sequed_q,
-        sequed_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        dq,
-        dk,
-        dv,
-        softmax_scale,
-        causal,
-        window_size=(-1, -1),
-        softcap=0.0,
-        deterministic=False,
-        sm_margin=0,
-):
-    # 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)]
-    dq, dk, dv, 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,
-        causal,
-        window_size[0],
-        window_size[1],
-        softcap,
-        deterministic,
-        sm_margin,
-    )
-    return dq, dk, dv, softmax_d
-
-
-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,
-    ):
-        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=window_size,
-            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
-        return 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,
-            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
-
-
-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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-):
-    """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,
-    )
-
-
-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,
-):
-    """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,
-    )
-
-
-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 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,
-    )
-
-
-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 must be a multiple of 256.
-        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(
-            (k_cache.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=window_size,
-        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/torch28-cxx11-cu126-aarch64-linux/flash_attn3/__init__.py

@@ -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/torch28-cxx11-cu126-aarch64-linux/flash_attn3/_flash_attn3_8d4f83f.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e9aef52109e5974778e3ccc2f697c4e6050b365624c843a675ce894b938341cc
-size 822395576

build/torch28-cxx11-cu126-aarch64-linux/flash_attn3/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _flash_attn3_8d4f83f
-ops = torch.ops._flash_attn3_8d4f83f
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_flash_attn3_8d4f83f::{op_name}"

build/torch28-cxx11-cu126-aarch64-linux/flash_attn3/flash_attn_interface.py

@@ -1,828 +0,0 @@
-# Copyright (c) 2023, Tri Dao.
-
-from typing import Optional, Union
-
-import torch
-import torch.nn as nn
-
-from ._ops import ops as flash_attn_3_cuda
-
-def maybe_contiguous(x):
-    return x.contiguous() if x is not None and x.stride(-1) != 1 else x
-
-
-def _flash_attn_forward(
-        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=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        rotary_interleaved=True,
-        scheduler_metadata=None,
-        num_splits=1,
-        pack_gqa=None,
-        sm_margin=0):
-    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, *rest = 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[0],
-        window_size[1],
-        attention_chunk,
-        softcap,
-        rotary_interleaved,
-        scheduler_metadata,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    return out, softmax_lse, *rest
-
-
-def _flash_attn_backward(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        sequed_q,
-        sequed_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        dq,
-        dk,
-        dv,
-        softmax_scale,
-        causal,
-        window_size=(-1, -1),
-        softcap=0.0,
-        deterministic=False,
-        sm_margin=0,
-):
-    # 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)]
-    dq, dk, dv, 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,
-        causal,
-        window_size[0],
-        window_size[1],
-        softcap,
-        deterministic,
-        sm_margin,
-    )
-    return dq, dk, dv, softmax_d
-
-
-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,
-    ):
-        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=window_size,
-            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
-        return 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,
-            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
-
-
-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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-):
-    """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,
-    )
-
-
-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,
-):
-    """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,
-    )
-
-
-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 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,
-    )
-
-
-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 must be a multiple of 256.
-        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(
-            (k_cache.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=window_size,
-        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/torch28-cxx11-cu126-x86_64-linux/flash_attn3/__init__.py

@@ -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/torch28-cxx11-cu126-x86_64-linux/flash_attn3/_flash_attn3_48fe103_dirty.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bc32b815563bc9051986a333a362ff61e37cbd967893212243292fef03b461a5
-size 838544688

build/torch28-cxx11-cu126-x86_64-linux/flash_attn3/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _flash_attn3_48fe103_dirty
-ops = torch.ops._flash_attn3_48fe103_dirty
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_flash_attn3_48fe103_dirty::{op_name}"

build/torch28-cxx11-cu126-x86_64-linux/flash_attn3/flash_attn_interface.py

@@ -1,828 +0,0 @@
-# Copyright (c) 2023, Tri Dao.
-
-from typing import Optional, Union
-
-import torch
-import torch.nn as nn
-
-from ._ops import ops as flash_attn_3_cuda
-
-def maybe_contiguous(x):
-    return x.contiguous() if x is not None and x.stride(-1) != 1 else x
-
-
-def _flash_attn_forward(
-        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=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        rotary_interleaved=True,
-        scheduler_metadata=None,
-        num_splits=1,
-        pack_gqa=None,
-        sm_margin=0):
-    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, *rest = 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[0],
-        window_size[1],
-        attention_chunk,
-        softcap,
-        rotary_interleaved,
-        scheduler_metadata,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    return out, softmax_lse, *rest
-
-
-def _flash_attn_backward(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        sequed_q,
-        sequed_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        dq,
-        dk,
-        dv,
-        softmax_scale,
-        causal,
-        window_size=(-1, -1),
-        softcap=0.0,
-        deterministic=False,
-        sm_margin=0,
-):
-    # 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)]
-    dq, dk, dv, 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,
-        causal,
-        window_size[0],
-        window_size[1],
-        softcap,
-        deterministic,
-        sm_margin,
-    )
-    return dq, dk, dv, softmax_d
-
-
-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,
-    ):
-        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=window_size,
-            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
-        return 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,
-            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
-
-
-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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-):
-    """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,
-    )
-
-
-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,
-):
-    """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,
-    )
-
-
-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 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,
-    )
-
-
-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 must be a multiple of 256.
-        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(
-            (k_cache.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=window_size,
-        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/torch28-cxx11-cu128-aarch64-linux/flash_attn3/__init__.py

@@ -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/torch28-cxx11-cu128-aarch64-linux/flash_attn3/_flash_attn3_8d4f83f.abi3.so

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e9aef52109e5974778e3ccc2f697c4e6050b365624c843a675ce894b938341cc
-size 822395576

build/torch28-cxx11-cu128-aarch64-linux/flash_attn3/_ops.py

@@ -1,9 +0,0 @@
-import torch
-from . import _flash_attn3_8d4f83f
-ops = torch.ops._flash_attn3_8d4f83f
-
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_flash_attn3_8d4f83f::{op_name}"

build/torch28-cxx11-cu128-aarch64-linux/flash_attn3/flash_attn_interface.py

@@ -1,828 +0,0 @@
-# Copyright (c) 2023, Tri Dao.
-
-from typing import Optional, Union
-
-import torch
-import torch.nn as nn
-
-from ._ops import ops as flash_attn_3_cuda
-
-def maybe_contiguous(x):
-    return x.contiguous() if x is not None and x.stride(-1) != 1 else x
-
-
-def _flash_attn_forward(
-        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=(-1, -1),
-        attention_chunk=0,
-        softcap=0.0,
-        rotary_interleaved=True,
-        scheduler_metadata=None,
-        num_splits=1,
-        pack_gqa=None,
-        sm_margin=0):
-    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, *rest = 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[0],
-        window_size[1],
-        attention_chunk,
-        softcap,
-        rotary_interleaved,
-        scheduler_metadata,
-        num_splits,
-        pack_gqa,
-        sm_margin,
-    )
-    return out, softmax_lse, *rest
-
-
-def _flash_attn_backward(
-        dout,
-        q,
-        k,
-        v,
-        out,
-        softmax_lse,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        sequed_q,
-        sequed_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        dq,
-        dk,
-        dv,
-        softmax_scale,
-        causal,
-        window_size=(-1, -1),
-        softcap=0.0,
-        deterministic=False,
-        sm_margin=0,
-):
-    # 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)]
-    dq, dk, dv, 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,
-        causal,
-        window_size[0],
-        window_size[1],
-        softcap,
-        deterministic,
-        sm_margin,
-    )
-    return dq, dk, dv, softmax_d
-
-
-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,
-    ):
-        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=window_size,
-            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
-        return 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,
-            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
-
-
-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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-    ):
-        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=window_size,
-            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
-
-    @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,
-            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,
-):
-    """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,
-    )
-
-
-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,
-):
-    """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,
-    )
-
-
-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 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,
-    )
-
-
-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 must be a multiple of 256.
-        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(
-            (k_cache.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=window_size,
-        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/torch28-cxx11-cu129-aarch64-linux/flash_attn3/__init__.py

@@ -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",
-]