253 lines
13 KiB
Python
253 lines
13 KiB
Python
import torch
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import torch.nn as nn
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import flash_attn_cuda
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def _get_block_size(device, head_dim, is_dropout):
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assert head_dim in [16, 32, 64, 128]
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if head_dim in [16, 32]:
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return 256
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elif head_dim == 64:
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return 128 if (torch.cuda.get_device_capability(device) == (7, 5) and is_dropout) else 256
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elif head_dim == 128:
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return 256 if (torch.cuda.get_device_capability(device) == (8, 0) and not is_dropout) else 128
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def _flash_attn_forward(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p,
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softmax_scale, causal, return_softmax):
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out, softmax_lse, *rest = flash_attn_cuda.fwd(
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q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale,
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False, causal, return_softmax, 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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S_dmask = rest[0] if return_softmax else None
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return out, softmax_lse, S_dmask
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def _flash_attn_backward(dout, q, k, v, out, softmax_lse, dq, dk, dv, cu_seqlens_q, cu_seqlens_k,
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max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale, causal):
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softmax_d = flash_attn_cuda.bwd(
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dout, q, k, v, out, softmax_lse, dq, dk, dv, cu_seqlens_q, cu_seqlens_k,
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max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale, False, causal, None)
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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, cu_seqlens, max_seqlen, dropout_p, softmax_scale, causal, return_softmax):
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# Save rng_state because the backward pass will regenerate the dropout mask
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rng_state = torch.cuda.get_rng_state() if dropout_p > 0 else None
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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, softmax_lse, S_dmask = _flash_attn_forward(
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qkv[:, 0], qkv[:, 1], qkv[:, 2], cu_seqlens, cu_seqlens, max_seqlen, max_seqlen,
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dropout_p, softmax_scale, causal=causal, return_softmax=return_softmax
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)
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ctx.save_for_backward(qkv, out, 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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qkv, out, softmax_lse, cu_seqlens, rng_state = ctx.saved_tensors
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if rng_state is not None:
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cur_rng_state = torch.cuda.get_rng_state()
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torch.cuda.set_rng_state(rng_state)
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dqkv = torch.empty_like(qkv)
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_flash_attn_backward(
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dout, qkv[:, 0], qkv[:, 1], qkv[:, 2], out, softmax_lse,
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dqkv[:, 0], dqkv[:, 1], dqkv[:, 2], cu_seqlens, cu_seqlens,
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ctx.max_seqlen, ctx.max_seqlen, ctx.dropout_p, ctx.softmax_scale, ctx.causal
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)
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if rng_state is not None:
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torch.cuda.set_rng_state(cur_rng_state)
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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, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p,
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softmax_scale, causal, return_softmax):
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# Save rng_state because the backward pass will regenerate the dropout mask
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rng_state = torch.cuda.get_rng_state() if dropout_p > 0 else None
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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, softmax_lse, S_dmask = _flash_attn_forward(
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q, kv[:, 0], kv[:, 1], cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
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dropout_p, softmax_scale, causal=causal, return_softmax=return_softmax
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)
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ctx.save_for_backward(q, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state)
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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, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state = ctx.saved_tensors
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if rng_state is not None:
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cur_rng_state = torch.cuda.get_rng_state()
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torch.cuda.set_rng_state(rng_state)
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dq = torch.empty_like(q)
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dkv = torch.empty_like(kv)
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_flash_attn_backward(
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dout, q, kv[:, 0], kv[:, 1], out, softmax_lse,
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dq, dkv[:, 0], dkv[:, 1], cu_seqlens_q, cu_seqlens_k,
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ctx.max_seqlen_q, ctx.max_seqlen_k, ctx.dropout_p, ctx.softmax_scale, ctx.causal
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)
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if rng_state is not None:
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torch.cuda.set_rng_state(cur_rng_state)
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return dq, dkv, None, None, None, None, None, None, None, None
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class FlashAttnFunc(torch.autograd.Function):
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@staticmethod
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def forward(ctx, q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p,
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softmax_scale, causal, return_softmax):
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# Save rng_state because the backward pass will regenerate the dropout mask
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rng_state = torch.cuda.get_rng_state() if dropout_p > 0 else None
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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, softmax_lse, S_dmask = _flash_attn_forward(
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q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
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dropout_p, softmax_scale, causal=causal, return_softmax=return_softmax
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)
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ctx.save_for_backward(q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state)
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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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if rng_state is not None:
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cur_rng_state = torch.cuda.get_rng_state()
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torch.cuda.set_rng_state(rng_state)
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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, q, k, v, out, softmax_lse, dq, dk, dv, cu_seqlens_q, cu_seqlens_k,
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ctx.max_seqlen_q, ctx.max_seqlen_k, ctx.dropout_p, ctx.softmax_scale, ctx.causal
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)
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if rng_state is not None:
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torch.cuda.set_rng_state(cur_rng_state)
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return dq, dk, dv, None, None, None, None, None, None, None, None
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def flash_attn_unpadded_qkvpacked_func(qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale=None,
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causal=False, return_attn_probs=False):
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"""dropout_p should be set to 0.0 during evaluation
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Arguments:
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qkv: (total, 3, nheads, headdim), where total = total number of tokens in the batch.
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cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
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of the sequences in the batch, used to index into qkv.
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max_seqlen: int. Maximum sequence length in the batch.
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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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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: (total, 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 FlashAttnQKVPackedFunc.apply(qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale,
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causal, return_attn_probs)
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def flash_attn_unpadded_kvpacked_func(q, kv, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
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dropout_p, softmax_scale=None, causal=False,
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return_attn_probs=False):
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"""dropout_p should be set to 0.0 during evaluation
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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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kv: (total_k, 2, nheads, 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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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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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: (total, 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 FlashAttnKVPackedFunc.apply(q, kv, cu_seqlens_q, cu_seqlens_k,
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max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale, causal,
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return_attn_probs)
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def flash_attn_unpadded_func(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
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dropout_p, softmax_scale=None, causal=False, return_attn_probs=False):
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"""dropout_p should be set to 0.0 during evaluation
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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, headdim), where total_k = total number of key tokens in the batch.
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v: (total_k, nheads, 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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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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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: (total, 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(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
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dropout_p, softmax_scale, causal, return_attn_probs)
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def flash_attn_func(qkv, cu_seqlens, dropout_p, max_s, softmax_scale=None, causal=False,
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return_attn_probs=False):
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"""For backward-compatibility only, will remove soon.
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dropout_p should be set to 0.0 during evaluation
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"""
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return flash_attn_unpadded_qkvpacked_func(qkv, cu_seqlens, max_s, dropout_p, softmax_scale,
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causal, return_attn_probs)
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