865 lines
32 KiB
Python
865 lines
32 KiB
Python
import torch
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import torch.nn as nn
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from einops import rearrange
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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 flash_attn_2_cuda as flash_attn_cuda
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# isort: on
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def _get_block_size(device, head_dim, is_dropout, is_causal):
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# This should match the block sizes in the CUDA kernel
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assert head_dim <= 256
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major, minor = torch.cuda.get_device_capability(device)
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is_sm8x = major == 8 and minor > 0 # Only include sm86 and sm89, exclude sm80 (A100)
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is_sm80 = major == 8 and minor == 0
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is_sm90 = major == 9 and minor == 0
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if head_dim <= 32:
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return 128, 128
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if head_dim <= 64:
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return (128, 128) if not is_dropout else (128, 64)
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elif head_dim <= 96:
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return (64, 64) if (is_sm8x and is_causal) else (128, 64)
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elif head_dim <= 128:
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if is_sm8x:
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return (64, 64) if (not is_dropout and is_causal) else (128, 32)
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else:
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return 128, (64 if not is_dropout else 32)
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elif head_dim <= 160:
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if is_sm8x:
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return (128, 64) if not is_causal else (64, 64)
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else:
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return 128, 32
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elif head_dim <= 192:
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return (128, 64) if not is_dropout else (64, 64)
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elif head_dim <= 224:
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return (128, 64) if (is_sm80 or is_sm90) else (64, 64)
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elif head_dim <= 256:
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return (128, 64) if is_sm80 else (64, 64)
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def _flash_attn_forward(q, k, v, dropout_p, softmax_scale, causal, return_softmax):
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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, S_dmask, rng_state = flash_attn_cuda.fwd(
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q, k, v, None, dropout_p, softmax_scale, causal, return_softmax, None
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)
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return out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state
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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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dropout_p,
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softmax_scale,
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causal,
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return_softmax,
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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, S_dmask, rng_state = flash_attn_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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max_seqlen_q,
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max_seqlen_k,
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dropout_p,
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softmax_scale,
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False,
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causal,
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return_softmax,
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None,
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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, S_dmask, rng_state
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def _flash_attn_backward(
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dout, q, k, v, out, softmax_lse, dq, dk, dv, dropout_p, softmax_scale, causal, rng_state=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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dq, dk, dv, softmax_d, = flash_attn_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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dropout_p,
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softmax_scale,
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causal,
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None,
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rng_state,
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)
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return dq, dk, dv, softmax_d
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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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dropout_p,
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softmax_scale,
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causal,
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rng_state=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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dq, dk, dv, softmax_d, = flash_attn_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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max_seqlen_q,
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max_seqlen_k,
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dropout_p,
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softmax_scale,
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False,
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causal,
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None,
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rng_state,
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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 FlashAttnQKVPackedFunc(torch.autograd.Function):
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@staticmethod
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def forward(ctx, qkv, dropout_p, softmax_scale, causal, return_softmax):
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if softmax_scale is None:
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softmax_scale = qkv.shape[-1] ** (-0.5)
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out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_forward(
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qkv[:, :, 0],
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qkv[:, :, 1],
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qkv[:, :, 2],
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dropout_p,
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softmax_scale,
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causal=causal,
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return_softmax=return_softmax and dropout_p > 0,
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)
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ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state)
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ctx.dropout_p = dropout_p
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ctx.softmax_scale = softmax_scale
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ctx.causal = causal
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return out if not return_softmax else (out, softmax_lse, S_dmask)
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@staticmethod
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def backward(ctx, dout, *args):
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q, k, v, out, softmax_lse, rng_state = ctx.saved_tensors
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qkv_shape = q.shape[:-2] + (3, *q.shape[-2:])
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dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device)
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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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dqkv[:, :, 0],
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dqkv[:, :, 1],
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dqkv[:, :, 2],
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ctx.dropout_p,
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ctx.softmax_scale,
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ctx.causal,
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rng_state=rng_state,
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)
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dqkv = dqkv[..., : dout.shape[-1]] # We could have padded the head dimension
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return dqkv, None, None, None, None
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class FlashAttnVarlenQKVPackedFunc(torch.autograd.Function):
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@staticmethod
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def forward(ctx, qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale, causal, return_softmax):
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if softmax_scale is None:
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softmax_scale = qkv.shape[-1] ** (-0.5)
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out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_varlen_forward(
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qkv[:, 0],
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qkv[:, 1],
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qkv[:, 2],
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cu_seqlens,
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cu_seqlens,
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max_seqlen,
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max_seqlen,
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dropout_p,
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softmax_scale,
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causal=causal,
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return_softmax=return_softmax and dropout_p > 0,
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)
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ctx.save_for_backward(q, k, v, out_padded, softmax_lse, cu_seqlens, rng_state)
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ctx.dropout_p = dropout_p
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ctx.max_seqlen = max_seqlen
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ctx.softmax_scale = softmax_scale
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ctx.causal = causal
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return out if not return_softmax else (out, softmax_lse, S_dmask)
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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, rng_state = ctx.saved_tensors
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qkv_shape = q.shape[:-2] + (3, *q.shape[-2:])
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dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device)
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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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dqkv[:, 0],
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dqkv[:, 1],
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dqkv[:, 2],
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cu_seqlens,
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cu_seqlens,
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ctx.max_seqlen,
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ctx.max_seqlen,
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ctx.dropout_p,
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ctx.softmax_scale,
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ctx.causal,
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rng_state=rng_state,
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)
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dqkv = dqkv[..., : dout.shape[-1]] # We could have padded the head dimension
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return dqkv, None, None, None, None, None, None
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class FlashAttnKVPackedFunc(torch.autograd.Function):
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@staticmethod
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def forward(ctx, q, kv, dropout_p, softmax_scale, causal, return_softmax):
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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, rng_state = _flash_attn_forward(
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q,
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kv[:, :, 0],
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kv[:, :, 1],
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dropout_p,
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softmax_scale,
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causal=causal,
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return_softmax=return_softmax and dropout_p > 0,
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)
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ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state)
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ctx.dropout_p = dropout_p
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ctx.softmax_scale = softmax_scale
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ctx.causal = causal
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return out if not return_softmax else (out, softmax_lse, S_dmask)
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@staticmethod
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def backward(ctx, dout, *args):
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q, k, v, out, softmax_lse, rng_state = ctx.saved_tensors
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dq = torch.empty_like(q)
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kv_shape = k.shape[:-2] + (2, *k.shape[-2:])
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dkv = torch.empty(kv_shape, dtype=k.dtype, device=k.device)
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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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dkv[:, :, 0],
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dkv[:, :, 1],
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ctx.dropout_p,
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ctx.softmax_scale,
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ctx.causal,
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rng_state=rng_state,
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)
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dq = dq[..., : dout.shape[-1]] # We could have padded the head dimension
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dkv = dkv[..., : dout.shape[-1]]
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return dq, dkv, None, None, None, None
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class FlashAttnVarlenKVPackedFunc(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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kv,
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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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dropout_p,
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softmax_scale,
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causal,
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return_softmax,
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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, rng_state = _flash_attn_varlen_forward(
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q,
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kv[:, 0],
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kv[:, 1],
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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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dropout_p,
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softmax_scale,
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causal=causal,
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return_softmax=return_softmax and dropout_p > 0,
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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, rng_state
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)
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ctx.dropout_p = dropout_p
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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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return out if not return_softmax else (out, softmax_lse, S_dmask)
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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, rng_state = ctx.saved_tensors
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dq = torch.empty_like(q)
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kv_shape = k.shape[:-2] + (2, *k.shape[-2:])
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dkv = torch.empty(kv_shape, dtype=k.dtype, device=k.device)
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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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dkv[:, 0],
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dkv[:, 1],
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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.dropout_p,
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ctx.softmax_scale,
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ctx.causal,
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rng_state=rng_state,
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)
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dq = dq[..., : dout.shape[-1]] # We could have padded the head dimension
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dkv = dkv[..., : dout.shape[-1]]
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return dq, dkv, None, None, None, None, None, None, None, None
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|
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class FlashAttnFunc(torch.autograd.Function):
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@staticmethod
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def forward(ctx, q, k, v, dropout_p, softmax_scale, causal, return_softmax):
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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, rng_state = _flash_attn_forward(
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q,
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k,
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v,
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dropout_p,
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softmax_scale,
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causal=causal,
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return_softmax=return_softmax and dropout_p > 0,
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)
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ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state)
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ctx.dropout_p = dropout_p
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ctx.softmax_scale = softmax_scale
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ctx.causal = causal
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return out if not return_softmax else (out, softmax_lse, S_dmask)
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|
|
|
@staticmethod
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|
def backward(ctx, dout, *args):
|
|
q, k, v, out, softmax_lse, rng_state = 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,
|
|
k,
|
|
v,
|
|
out,
|
|
softmax_lse,
|
|
dq,
|
|
dk,
|
|
dv,
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|
ctx.dropout_p,
|
|
ctx.softmax_scale,
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|
ctx.causal,
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|
rng_state=rng_state,
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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):
|
|
@staticmethod
|
|
def forward(
|
|
ctx,
|
|
q,
|
|
k,
|
|
v,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
max_seqlen_q,
|
|
max_seqlen_k,
|
|
dropout_p,
|
|
softmax_scale,
|
|
causal,
|
|
return_softmax,
|
|
):
|
|
if softmax_scale is None:
|
|
softmax_scale = q.shape[-1] ** (-0.5)
|
|
out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_varlen_forward(
|
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q,
|
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k,
|
|
v,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
max_seqlen_q,
|
|
max_seqlen_k,
|
|
dropout_p,
|
|
softmax_scale,
|
|
causal=causal,
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|
return_softmax=return_softmax and dropout_p > 0,
|
|
)
|
|
ctx.save_for_backward(
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q, k, v, out_padded, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state
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)
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|
ctx.dropout_p = dropout_p
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|
ctx.max_seqlen_q = max_seqlen_q
|
|
ctx.max_seqlen_k = max_seqlen_k
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|
ctx.softmax_scale = softmax_scale
|
|
ctx.causal = causal
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|
return out if not return_softmax else (out, softmax_lse, S_dmask)
|
|
|
|
@staticmethod
|
|
def backward(ctx, dout, *args):
|
|
q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state = ctx.saved_tensors
|
|
dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
|
|
_flash_attn_varlen_backward(
|
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dout,
|
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q,
|
|
k,
|
|
v,
|
|
out,
|
|
softmax_lse,
|
|
dq,
|
|
dk,
|
|
dv,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
ctx.max_seqlen_q,
|
|
ctx.max_seqlen_k,
|
|
ctx.dropout_p,
|
|
ctx.softmax_scale,
|
|
ctx.causal,
|
|
rng_state=rng_state,
|
|
)
|
|
dq = dq[..., : dout.shape[-1]] # We could have padded the head dimension
|
|
dk = dk[..., : dout.shape[-1]]
|
|
dv = dv[..., : dout.shape[-1]]
|
|
return dq, dk, dv, None, None, None, None, None, None, None, None
|
|
|
|
|
|
def flash_attn_qkvpacked_func(
|
|
qkv, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False
|
|
):
|
|
"""dropout_p should be set to 0.0 during evaluation
|
|
If Q, K, V are already stacked into 1 tensor, this function will be faster than
|
|
calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation
|
|
of the gradients of Q, K, V.
|
|
For multi-query and grouped-query attention (MQA/GQA), please see
|
|
flash_attn_kvpacked_func and flash_attn_func.
|
|
|
|
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).
|
|
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, dropout_p, softmax_scale, causal, return_attn_probs)
|
|
|
|
|
|
def flash_attn_kvpacked_func(
|
|
q, kv, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False
|
|
):
|
|
"""dropout_p should be set to 0.0 during evaluation
|
|
If 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 K, V.
|
|
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.
|
|
|
|
Arguments:
|
|
q: (batch_size, seqlen, nheads, headdim)
|
|
kv: (batch_size, seqlen, 2, 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).
|
|
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 FlashAttnKVPackedFunc.apply(q, kv, dropout_p, softmax_scale, causal, return_attn_probs)
|
|
|
|
|
|
def flash_attn_func(
|
|
q, k, v, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False
|
|
):
|
|
"""dropout_p should be set to 0.0 during evaluation
|
|
Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
|
|
than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
|
|
For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
|
|
0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
|
|
|
|
If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
|
|
For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
|
|
1 1 1 1 0
|
|
1 1 1 1 1
|
|
If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
|
|
0 0
|
|
0 0
|
|
0 0
|
|
1 0
|
|
1 1
|
|
If the row of the mask is all zero, the output will be zero.
|
|
|
|
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).
|
|
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 FlashAttnFunc.apply(q, k, v, dropout_p, softmax_scale, causal, return_attn_probs)
|
|
|
|
|
|
def flash_attn_varlen_qkvpacked_func(
|
|
qkv,
|
|
cu_seqlens,
|
|
max_seqlen,
|
|
dropout_p=0.0,
|
|
softmax_scale=None,
|
|
causal=False,
|
|
return_attn_probs=False,
|
|
):
|
|
"""dropout_p should be set to 0.0 during evaluation
|
|
If Q, K, V are already stacked into 1 tensor, this function will be faster than
|
|
calling flash_attn_varlen_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_varlen_kvpacked_func and flash_attn_varlen_func.
|
|
|
|
Arguments:
|
|
qkv: (total, 3, nheads, headdim), where total = total number of tokens in the batch.
|
|
cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
|
|
of the sequences in the batch, used to index into qkv.
|
|
max_seqlen: int. Maximum sequence length in the batch.
|
|
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).
|
|
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: (total, 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 FlashAttnVarlenQKVPackedFunc.apply(
|
|
qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale, causal, return_attn_probs
|
|
)
|
|
|
|
|
|
def flash_attn_varlen_kvpacked_func(
|
|
q,
|
|
kv,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
max_seqlen_q,
|
|
max_seqlen_k,
|
|
dropout_p=0.0,
|
|
softmax_scale=None,
|
|
causal=False,
|
|
return_attn_probs=False,
|
|
):
|
|
"""dropout_p should be set to 0.0 during evaluation
|
|
If 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 K, V.
|
|
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.
|
|
|
|
Arguments:
|
|
q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.
|
|
kv: (total_k, 2, nheads_k, headdim), where total_k = total number of key tokens in the batch.
|
|
cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
|
|
of the sequences in the batch, used to index into q.
|
|
cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
|
|
of the sequences in the batch, used to index into kv.
|
|
max_seqlen_q: int. Maximum query sequence length in the batch.
|
|
max_seqlen_k: int. Maximum key sequence length in the batch.
|
|
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).
|
|
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: (total, 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 FlashAttnVarlenKVPackedFunc.apply(
|
|
q,
|
|
kv,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
max_seqlen_q,
|
|
max_seqlen_k,
|
|
dropout_p,
|
|
softmax_scale,
|
|
causal,
|
|
return_attn_probs,
|
|
)
|
|
|
|
|
|
def flash_attn_varlen_func(
|
|
q,
|
|
k,
|
|
v,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
max_seqlen_q,
|
|
max_seqlen_k,
|
|
dropout_p=0.0,
|
|
softmax_scale=None,
|
|
causal=False,
|
|
return_attn_probs=False,
|
|
):
|
|
"""dropout_p should be set to 0.0 during evaluation
|
|
Supports multi-query and grouped-query attention (MQA/GQA) by passing in 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
|
|
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.
|
|
|
|
Arguments:
|
|
q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.
|
|
k: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.
|
|
v: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.
|
|
cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
|
|
of the sequences in the batch, used to index into q.
|
|
cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
|
|
of the sequences in the batch, used to index into kv.
|
|
max_seqlen_q: int. Maximum query sequence length in the batch.
|
|
max_seqlen_k: int. Maximum key sequence length in the batch.
|
|
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).
|
|
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: (total, 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 FlashAttnVarlenFunc.apply(
|
|
q,
|
|
k,
|
|
v,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
max_seqlen_q,
|
|
max_seqlen_k,
|
|
dropout_p,
|
|
softmax_scale,
|
|
causal,
|
|
return_attn_probs,
|
|
)
|
|
|
|
|
|
def flash_attn_with_kvcache(
|
|
q,
|
|
k_cache,
|
|
v_cache,
|
|
k=None,
|
|
v=None,
|
|
cache_seqlens=None,
|
|
softmax_scale=None,
|
|
causal=False,
|
|
num_splits=0,
|
|
):
|
|
"""
|
|
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.
|
|
|
|
Does not support backward pass.
|
|
|
|
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.
|
|
|
|
Arguments:
|
|
q: (batch_size, seqlen, nheads, headdim)
|
|
k_cache: (batch_size, seqlen_cache, nheads_k, headdim)
|
|
v_cache: (batch_size, seqlen_cache, nheads_k, headdim)
|
|
k [optional]: (batch_size, seqlen, 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, nheads_k, headdim). Similar to k.
|
|
cache_seqlens: (batch_size,), dtype torch.int32. The sequence lengths of the KV cache.
|
|
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).
|
|
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:
|
|
out: (batch_size, seqlen, nheads, headdim).
|
|
"""
|
|
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"
|
|
maybe_contiguous = lambda x: x.contiguous() if x is not None and x.stride(-1) != 1 else x
|
|
q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
|
|
if softmax_scale is None:
|
|
softmax_scale = q.shape[-1] ** (-0.5)
|
|
out, softmax_lse = flash_attn_cuda.fwd_kvcache(
|
|
q, k_cache, v_cache, k, v, cache_seqlens, None, softmax_scale, causal, num_splits
|
|
)
|
|
return out
|