631 lines
22 KiB
Python
631 lines
22 KiB
Python
# Copyright (c) 2023, Tri Dao.
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from typing import Optional, Union
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import torch
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import torch.nn as nn
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# isort: off
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# We need to import the CUDA kernels after importing torch
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import flashattn_hopper_cuda
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# isort: on
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def maybe_contiguous(x):
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return x.contiguous() if x is not None and x.stride(-1) != 1 else x
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def _flash_attn_forward(q, k, v, softmax_scale, causal, window_size, descale_q = None, descale_k = None, descale_v = None, gqa_parallel=False):
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q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
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out, q, k, v, out_padded, softmax_lse, S_dmask = flashattn_hopper_cuda.fwd(
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q,
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k,
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v,
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None,
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softmax_scale,
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descale_q,
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descale_k,
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descale_v,
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causal,
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window_size[0],
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window_size[1],
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gqa_parallel
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)
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return out, q, k, v, out_padded, softmax_lse, S_dmask
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def _flash_attn_backward(
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dout,
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q,
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k,
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v,
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out,
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softmax_lse,
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dq,
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dk,
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dv,
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softmax_scale,
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causal,
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window_size,
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deterministic=False
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):
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# dq, dk, dv are allocated by us so they should already be contiguous
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dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)]
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dq, dk, dv, softmax_d, *rest = flashattn_hopper_cuda.bwd(
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dout,
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q,
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k,
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v,
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out,
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softmax_lse,
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dq,
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dk,
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dv,
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softmax_scale,
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causal,
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window_size[0],
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window_size[1],
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deterministic,
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)
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return dq, dk, dv, softmax_d
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def _flash_attn_varlen_forward(
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q,
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k,
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v,
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cu_seqlens_q,
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cu_seqlens_k,
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max_seqlen_q,
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max_seqlen_k,
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softmax_scale,
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causal,
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window_size=(-1, -1),
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seqused_q=None,
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seqused_k=None,
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):
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maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
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q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
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out, q, k, v, out_padded, softmax_lse = flashattn_hopper_cuda.varlen_fwd(
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q,
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k,
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v,
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None,
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cu_seqlens_q,
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cu_seqlens_k,
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seqused_q,
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seqused_k,
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max_seqlen_q,
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max_seqlen_k,
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softmax_scale,
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causal,
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window_size[0],
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window_size[1],
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)
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# if out.isnan().any() or softmax_lse.isnan().any():
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# breakpoint()
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return out, q, k, v, out_padded, softmax_lse
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def _flash_attn_varlen_backward(
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dout,
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q,
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k,
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v,
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out,
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softmax_lse,
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dq,
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dk,
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dv,
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cu_seqlens_q,
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cu_seqlens_k,
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max_seqlen_q,
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max_seqlen_k,
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softmax_scale,
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causal,
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window_size,
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deterministic=False,
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seqused_q=None,
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seqused_k=None,
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):
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maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
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# dq, dk, dv are allocated by us so they should already be contiguous
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dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)]
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(
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dq,
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dk,
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dv,
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softmax_d,
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*rest,
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) = flashattn_hopper_cuda.varlen_bwd(
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dout,
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q,
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k,
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v,
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out,
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softmax_lse,
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dq,
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dk,
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dv,
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cu_seqlens_q,
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cu_seqlens_k,
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seqused_q,
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seqused_k,
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max_seqlen_q,
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max_seqlen_k,
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softmax_scale,
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causal,
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window_size[0],
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window_size[1],
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deterministic,
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)
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# if dk.isnan().any() or dk.isnan().any() or dv.isnan().any() or softmax_d.isnan().any():
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# breakpoint()
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return dq, dk, dv, softmax_d
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class FlashAttnFunc(torch.autograd.Function):
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@staticmethod
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def forward(
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ctx,
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q,
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k,
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v,
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softmax_scale,
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causal,
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window_size,
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deterministic=False,
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descale_q=None,
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descale_k=None,
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descale_v=None,
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gqa_parallel=False,
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):
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if softmax_scale is None:
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softmax_scale = q.shape[-1] ** (-0.5)
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out, q, k, v, out_padded, softmax_lse, S_dmask = _flash_attn_forward(
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q,
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k,
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v,
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softmax_scale,
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causal,
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window_size,
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descale_q=descale_q,
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descale_k=descale_k,
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descale_v=descale_v,
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gqa_parallel=gqa_parallel,
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)
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ctx.save_for_backward(q, k, v, out_padded, softmax_lse)
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ctx.softmax_scale = softmax_scale
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ctx.causal = causal
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ctx.window_size = window_size
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ctx.deterministic = deterministic
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ctx.gqa_parallel = gqa_parallel
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return out, softmax_lse
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@staticmethod
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def backward(ctx, dout, *args):
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q, k, v, out, softmax_lse = ctx.saved_tensors
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dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
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_flash_attn_backward(
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dout,
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q,
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k,
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v,
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out,
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softmax_lse,
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dq,
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dk,
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dv,
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ctx.softmax_scale,
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ctx.causal,
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ctx.window_size,
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ctx.deterministic,
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)
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dq = dq[..., : dout.shape[-1]] # We could have padded the head dimension
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dk = dk[..., : dout.shape[-1]]
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dv = dv[..., : dout.shape[-1]]
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return dq, dk, dv, None, None, None, None, None, None, None, None
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class FlashAttnVarlenFunc(torch.autograd.Function):
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@staticmethod
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def forward(
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ctx,
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q,
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k,
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v,
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cu_seqlens_q,
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cu_seqlens_k,
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max_seqlen_q,
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max_seqlen_k,
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softmax_scale,
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causal,
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window_size,
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deterministic=False,
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seqused_q=None,
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seqused_k=None,
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):
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if softmax_scale is None:
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softmax_scale = q.shape[-1] ** (-0.5)
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out, q, k, v, out_padded, softmax_lse = _flash_attn_varlen_forward(
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q,
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k,
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v,
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cu_seqlens_q,
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cu_seqlens_k,
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max_seqlen_q,
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max_seqlen_k,
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softmax_scale,
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causal=causal,
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window_size=window_size,
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seqused_q=seqused_q,
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seqused_k=seqused_k,
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)
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ctx.save_for_backward(
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q, k, v, out_padded, softmax_lse, cu_seqlens_q, cu_seqlens_k,
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seqused_q, seqused_k
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)
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ctx.max_seqlen_q = max_seqlen_q
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ctx.max_seqlen_k = max_seqlen_k
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ctx.softmax_scale = softmax_scale
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ctx.causal = causal
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ctx.window_size = window_size
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ctx.deterministic = deterministic
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return out, softmax_lse
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@staticmethod
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def backward(ctx, dout, *args):
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q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k = ctx.saved_tensors
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dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
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_flash_attn_varlen_backward(
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dout,
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q,
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k,
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v,
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out,
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softmax_lse,
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dq,
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dk,
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dv,
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cu_seqlens_q,
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cu_seqlens_k,
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ctx.max_seqlen_q,
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ctx.max_seqlen_k,
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ctx.softmax_scale,
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ctx.causal,
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ctx.window_size,
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ctx.deterministic,
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seqused_q,
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seqused_k,
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)
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dq = dq[..., : dout.shape[-1]] # We could have padded the head dimension
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dk = dk[..., : dout.shape[-1]]
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dv = dv[..., : dout.shape[-1]]
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return dq, dk, dv, None, None, None, None, None, None, None, None, None, None
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def flash_attn_func(
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q,
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k,
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v,
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softmax_scale=None,
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causal=False,
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window_size=(-1, -1),
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deterministic=False,
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descale_q=None,
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descale_k=None,
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descale_v=None,
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gqa_parallel=False,
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):
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"""dropout_p should be set to 0.0 during evaluation
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Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
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than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
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For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
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0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
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If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
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For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
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1 1 1 1 0
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1 1 1 1 1
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If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
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0 0
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0 0
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0 0
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1 0
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1 1
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If the row of the mask is all zero, the output will be zero.
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If window_size != (-1, -1), implements sliding window local attention. Query at position i
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will only attend to keys between
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[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
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Arguments:
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q: (batch_size, seqlen, nheads, headdim)
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k: (batch_size, seqlen, nheads_k, headdim)
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v: (batch_size, seqlen, nheads_k, headdim)
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dropout_p: float. Dropout probability.
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softmax_scale: float. The scaling of QK^T before applying softmax.
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Default to 1 / sqrt(headdim).
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causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
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window_size: (left, right). If not (-1, -1), implements sliding window local attention.
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alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
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(-alibi_slope * |i + seqlen_k - seqlen_q - j|)
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is added to the attention score of query i and key j.
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deterministic: bool. Whether to use the deterministic implementation of the backward pass,
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which is slightly slower and uses more memory. The forward pass is always deterministic.
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descale_q: (1,), fp32. A de-quantization scaling factor for q in fp8 execution.
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descale_k: (1,), fp32. A de-quantization scaling factor for k in fp8 execution.
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descale_v: (1,), fp32. A de-quantization scaling factor for v in fp8 execution.
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return_attn_probs: bool. Whether to return the attention probabilities. This option is for
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testing only. The returned probabilities are not guaranteed to be correct
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(they might not have the right scaling).
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Return:
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out: (batch_size, seqlen, nheads, headdim).
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softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
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logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
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normalization factor).
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S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).
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The output of softmax (possibly with different scaling). It also encodes the dropout
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pattern (negative means that location was dropped, nonnegative means it was kept).
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"""
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return FlashAttnFunc.apply(
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q,
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k,
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v,
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softmax_scale,
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causal,
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window_size,
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deterministic,
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descale_q,
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descale_k,
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descale_v,
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gqa_parallel
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)
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def flash_attn_varlen_func(
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q,
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k,
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v,
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cu_seqlens_q,
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cu_seqlens_k,
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max_seqlen_q,
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max_seqlen_k,
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softmax_scale=None,
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causal=False,
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window_size=(-1, -1),
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deterministic=False,
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seqused_q=None,
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seqused_k=None,
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):
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"""
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Supports multi-query and grouped-query attention (MQA/GQA) by passing in K, V 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
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0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
|
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If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
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For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
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1 1 1 1 0
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1 1 1 1 1
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If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
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0 0
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0 0
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0 0
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1 0
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1 1
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If the row of the mask is all zero, the output will be zero.
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Arguments:
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q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.
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k: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.
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v: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.
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cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
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of the sequences in the batch, used to index into q.
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cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
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of the sequences in the batch, used to index into kv.
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max_seqlen_q: int. Maximum query sequence length in the batch.
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max_seqlen_k: int. Maximum key sequence length in the batch.
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softmax_scale: float. The scaling of QK^T before applying softmax.
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Default to 1 / sqrt(headdim).
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causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
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window_size: (left, right). If not (-1, -1), implements sliding window local attention.
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seqused_q: (batch_size,), dtype torch.int32. If not None, it defines the actual number of
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query and output tokens in each sequence.
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seqused_k: (batch_size,), dtype torch.int32. If not None, it defines the actual number of
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key and value tokens in each sequence.
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Return:
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out: (total, nheads, headdim).
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softmax_lse [optional, if return_attn_probs=True]: (nheads, total_q_seqlen). The
|
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logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
|
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normalization factor).
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"""
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return FlashAttnVarlenFunc.apply(
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q,
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k,
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v,
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cu_seqlens_q,
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cu_seqlens_k,
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|
max_seqlen_q,
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|
max_seqlen_k,
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softmax_scale,
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causal,
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window_size,
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deterministic,
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seqused_q,
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seqused_k,
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)
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def flash_attn_with_kvcache(
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q,
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k_cache,
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v_cache,
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# k=None,
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# v=None,
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# rotary_cos=None,
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# rotary_sin=None,
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cache_seqlens: Optional[Union[(int, torch.Tensor)]] = None,
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cache_batch_idx: Optional[torch.Tensor] = None,
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# cache_leftpad: Optional[torch.Tensor] = None,
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# block_table: Optional[torch.Tensor] = None,
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softmax_scale=None,
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causal=False,
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window_size=(-1, -1), # -1 means infinite context window
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# softcap=0.0, # 0.0 means deactivated
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# rotary_interleaved=True,
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# alibi_slopes=None,
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num_splits=0,
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return_softmax_lse=False,
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gqa_parallel=None,
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max_seqlen_k_hint=None,
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descale_q=None,
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descale_k=None,
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descale_v=None,
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):
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"""
|
|
NOTE: The KV cache API for FlashAttention-3 is a work in progress. We reproduce the description
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from the FlashAttention-2 method of the same name below.
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If k and v are not None, k_cache and v_cache will be updated *inplace* with the new values from
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k and v. This is useful for incremental decoding: you can pass in the cached keys/values from
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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.
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If you pass in k / v, you must make sure that the cache is large enough to hold the new values.
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For example, the KV cache could be pre-allocated with the max sequence length, and you can use
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cache_seqlens to keep track of the current sequence lengths of each sequence in the batch.
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|
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Also apply rotary embedding if rotary_cos and rotary_sin are passed in. The key @k will be
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rotated by rotary_cos and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
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If causal or local (i.e., window_size != (-1, -1)), the query @q will be rotated by rotary_cos
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and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
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If not causal and not local, the query @q will be rotated by rotary_cos and rotary_sin at
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indices cache_seqlens only (i.e. we consider all tokens in @q to be at position cache_seqlens).
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See tests/test_flash_attn.py::test_flash_attn_kvcache for examples of how to use this function.
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Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
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than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
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For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
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0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
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|
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If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
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For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
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1 1 1 1 0
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1 1 1 1 1
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If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
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0 0
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|
0 0
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|
0 0
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1 0
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|
1 1
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If the row of the mask is all zero, the output will be zero.
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|
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If window_size != (-1, -1), implements sliding window local attention. Query at position i
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will only attend to keys between
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[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
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|
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|
Note: Does not support backward pass.
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|
|
|
Arguments:
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q: (batch_size, seqlen, nheads, headdim)
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k_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim) if there's no block_table,
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|
or (num_blocks, page_block_size, nheads_k, headdim) if there's a block_table (i.e. paged KV cache)
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page_block_size must be a multiple of 256.
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|
v_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim) if there's no block_table,
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|
or (num_blocks, page_block_size, nheads_k, headdim) if there's a block_table (i.e. paged KV cache)
|
|
k [optional]: (batch_size, seqlen_new, nheads_k, headdim). If not None, we concatenate
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|
k with k_cache, starting at the indices specified by cache_seqlens.
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|
v [optional]: (batch_size, seqlen_new, nheads_k, headdim). Similar to k.
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|
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.
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|
rotary_sin [optional]: (seqlen_ro, rotary_dim / 2). Similar to rotary_cos.
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|
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.
|
|
block_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).
|
|
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.
|
|
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).
|
|
"""
|
|
|
|
# unimplemented kwargs
|
|
k=None
|
|
v=None
|
|
rotary_cos=None
|
|
rotary_sin=None
|
|
cache_leftpad=None
|
|
block_table=None
|
|
softcap=0.0
|
|
rotary_interleaved=True
|
|
alibi_slopes=None
|
|
|
|
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"
|
|
q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
|
|
if softmax_scale is None:
|
|
softmax_scale = q.shape[-1] ** (-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)
|
|
cache_batch_idx = maybe_contiguous(cache_batch_idx)
|
|
# block_table = maybe_contiguous(block_table)
|
|
if gqa_parallel is None:
|
|
gqa_parallel = True if q.shape[1] <= 64 else False
|
|
# not in gqa/mqa setup
|
|
if q.shape[2] == k_cache.shape[2]:
|
|
gqa_parallel = False
|
|
if max_seqlen_k_hint is None:
|
|
max_seqlen_k_hint = k_cache.shape[1]
|
|
out, softmax_lse = flashattn_hopper_cuda.fwd_kvcache(
|
|
q,
|
|
k_cache,
|
|
v_cache,
|
|
k,
|
|
v,
|
|
cache_seqlens,
|
|
rotary_cos,
|
|
rotary_sin,
|
|
cache_batch_idx,
|
|
cache_leftpad,
|
|
block_table,
|
|
alibi_slopes,
|
|
None,
|
|
softmax_scale,
|
|
descale_q,
|
|
descale_k,
|
|
descale_v,
|
|
causal,
|
|
window_size[0],
|
|
window_size[1],
|
|
softcap,
|
|
rotary_interleaved,
|
|
num_splits,
|
|
max_seqlen_k_hint,
|
|
gqa_parallel
|
|
)
|
|
return (out, softmax_lse) if return_softmax_lse else out
|