246 lines
10 KiB
Python
246 lines
10 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
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#
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# This file contains code copied from the flash-linear-attention project.
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# The original source code was licensed under the MIT license and included
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# the following copyright notice:
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# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
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# ruff: noqa: E501
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import warnings
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from typing import Optional
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import torch.nn.functional as F
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import torch
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import torch.distributed as dist
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from einops import rearrange
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from .chunk_delta_h import chunk_gated_delta_rule_fwd_h
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from .chunk_o import chunk_fwd_o
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from .chunk_scaled_dot_kkt import chunk_scaled_dot_kkt_fwd
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from .cumsum import chunk_local_cumsum
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from .l2norm import l2norm_fwd
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from .solve_tril import solve_tril
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from .utils import SUPPRESS_LEVEL, input_guard
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from .wy_fast import recompute_w_u_fwd
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def torch_solve_tril(A: torch.Tensor, cu_seqlens: Optional[torch.LongTensor] = None, output_dtype: torch.dtype = torch.float,):
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chunk_size=64
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A = -A.transpose(1,2)
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sequence_length = A.shape[-2]
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pad_size = (chunk_size - sequence_length % chunk_size) % chunk_size
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A = F.pad(A, (0, 0, 0, pad_size))
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A = A.reshape(A.shape[0], A.shape[1], -1, chunk_size, A.shape[-1])
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for i in range(1, chunk_size):
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row = A[..., i, :i].clone()
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sub = A[..., :i, :i].clone()
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A[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
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A = A + torch.eye(chunk_size, dtype=A.dtype, device=A.device)
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return A.reshape(A.shape[0], A.shape[1], -1, A.shape[-1])[:,:,:sequence_length,:].transpose(1,2)
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def chunk_gated_delta_rule_fwd(q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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g: torch.Tensor,
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beta: torch.Tensor,
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scale: float,
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initial_state: torch.Tensor,
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output_final_state: bool,
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cu_seqlens: Optional[torch.LongTensor] = None):
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g = chunk_local_cumsum(g, chunk_size=64, cu_seqlens=cu_seqlens)
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A = chunk_scaled_dot_kkt_fwd(k=k,
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beta=beta,
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g_cumsum=g,
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cu_seqlens=cu_seqlens,
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output_dtype=q.dtype)
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#torch版
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for i in range(len(cu_seqlens)-1):
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A_i = A[:, cu_seqlens[i]:cu_seqlens[i+1], :, :]
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A[:, cu_seqlens[i]:cu_seqlens[i+1], :, :] = torch_solve_tril(A=A_i, cu_seqlens=torch.tensor([0, cu_seqlens[i+1]-cu_seqlens[i]], device=q.device), output_dtype=k.dtype)
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w, u = recompute_w_u_fwd(
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k=k,
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v=v,
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beta=beta,
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A=A,
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g_cumsum=g,
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cu_seqlens=cu_seqlens,
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)
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h, v_new, final_state = chunk_gated_delta_rule_fwd_h(
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k=k,
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w=w,
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u=u,
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g=g,
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initial_state=initial_state,
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output_final_state=output_final_state,
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cu_seqlens=cu_seqlens,
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)
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o = chunk_fwd_o(
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q=q,
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k=k,
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v=v_new,
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h=h,
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g=g,
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scale=scale,
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cu_seqlens=cu_seqlens,
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)
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if SUPPRESS_LEVEL < 3:
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return g, o, A, final_state, None, None, None
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elif SUPPRESS_LEVEL >= 3:
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return g, o, A, final_state, w, h, v_new
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class ChunkGatedDeltaRuleFunction(torch.autograd.Function):
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@staticmethod
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@input_guard
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@torch.amp.custom_fwd(device_type='cuda')
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def forward(ctx,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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g: torch.Tensor,
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beta: torch.Tensor,
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scale: float,
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initial_state: torch.Tensor,
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output_final_state: bool,
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cu_seqlens: Optional[torch.LongTensor] = None,
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use_qk_l2norm_in_kernel: bool = False):
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if use_qk_l2norm_in_kernel:
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q = l2norm_fwd(q)
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k = l2norm_fwd(k)
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g, o, A, final_state, w, h, v_new = chunk_gated_delta_rule_fwd(
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q=q,
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k=k,
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v=v,
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g=g,
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beta=beta,
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scale=scale,
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initial_state=initial_state,
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output_final_state=output_final_state,
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cu_seqlens=cu_seqlens,
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)
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ctx.scale = scale
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ctx.use_qk_l2norm_in_kernel = use_qk_l2norm_in_kernel
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return o.to(q.dtype), final_state
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@torch.compiler.disable
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def chunk_gated_delta_rule(q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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g: torch.Tensor,
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beta: torch.Tensor,
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scale: float = None,
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initial_state: torch.Tensor = None,
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output_final_state: bool = False,
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cu_seqlens: Optional[torch.LongTensor] = None,
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head_first: bool = False,
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use_qk_l2norm_in_kernel: bool = False):
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r"""
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Args:
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q (torch.Tensor):
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queries of shape `[B, T, H, K]` if `head_first=False` else `[B, H, T, K]`.
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k (torch.Tensor):
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keys of shape `[B, T, H, K]` if `head_first=False` else `[B, H, T, K]`.
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v (torch.Tensor):
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values of shape `[B, T, H, V]` if `head_first=False` else `[B, H, T, V]`.
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g (torch.Tensor):
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(forget) gating tensor (in log space!) of shape `[B, T, H]` if `head_first=False` else `[B, H, T]`.
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beta (torch.Tensor):
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betas of shape `[B, T, H]` if `head_first=False` else `[B, H, T]`.
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scale (Optional[int]):
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Scale factor for the RetNet attention scores.
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If not provided, it will default to `1 / sqrt(K)`. Default: `None`.
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initial_state (Optional[torch.Tensor]):
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Initial state of shape `[N, H, K, V]` for `N` input sequences.
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For equal-length input sequences, `N` equals the batch size `B`.
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Default: `None`.
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output_final_state (Optional[bool]):
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Whether to output the final state of shape `[N, H, K, V]`. Default: `False`.
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cu_seqlens (torch.LongTensor):
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Cumulative sequence lengths of shape `[N+1]` used for variable-length training,
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consistent with the FlashAttention API.
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head_first (Optional[bool]):
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Whether the inputs are in the head-first format, which is not supported for variable-length inputs.
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Default: `False`.
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Returns:
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o (torch.Tensor):
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Outputs of shape `[B, T, H, V]` if `head_first=False` else `[B, H, T, V]`.
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final_state (torch.Tensor):
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Final state of shape `[N, H, K, V]` if `output_final_state=True` else `None`.
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Examples::
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>>> import torch
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>>> import torch.nn.functional as F
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>>> from einops import rearrange
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>>> from fla.ops.gated_delta_rule import chunk_gated_delta_rule
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# inputs with equal lengths
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>>> B, T, H, K, V = 4, 2048, 4, 512, 512
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>>> q = torch.randn(B, T, H, K, dtype=torch.bfloat16, device='cuda')
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>>> k = F.normalize(torch.randn(B, T, H, K, dtype=torch.bfloat16, device='cuda'), p=2, dim=-1)
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>>> v = torch.randn(B, T, H, V, dtype=torch.bfloat16, device='cuda')
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>>> beta = torch.rand(B, T, H, dtype=torch.bfloat16, device='cuda').sigmoid()
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>>> g = F.logsigmoid(torch.rand(B, T, H, dtype=torch.bfloat16, device='cuda'))
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>>> h0 = torch.randn(B, H, K, V, dtype=torch.bfloat16, device='cuda')
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>>> o, ht = chunk_gated_delta_rule(
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q, k, v, g, beta,
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initial_state=h0,
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output_final_state=True
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)
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# for variable-length inputs, the batch size `B` is expected to be 1 and `cu_seqlens` is required
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>>> q, k, v, beta, g = map(lambda x: rearrange(x, 'b t ... -> 1 (b t) ...'), (q, k, v, beta, g))
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# for a batch with 4 sequences, `cu_seqlens` with 5 start/end positions are expected
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>>> cu_seqlens = q.new_tensor([0, 2048, 4096, 6144, 8192], dtype=torch.long)
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>>> o_var, ht_var = chunk_gated_delta_rule(
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q, k, v, g, beta,
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initial_state=h0,
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output_final_state=True,
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cu_seqlens=cu_seqlens
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)
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"""
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assert q.dtype == k.dtype == v.dtype
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assert q.dtype != torch.float32, "ChunkGatedDeltaRuleFunction does not support float32. Please use bfloat16."
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assert len(
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beta.shape
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) == 3, "beta must be of shape [B, T, H] if head_first=False, or [B, H, T] otherwise."
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if head_first:
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raise DeprecationWarning(
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"head_first is deprecated and will be removed in a future version. "
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"Please use head_first=False for now instead.",
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stacklevel=2)
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q, k, v, beta, g = map(
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lambda x: rearrange(x, 'b h t ... -> b t h ...'),
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(q, k, v, beta, g))
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if not head_first and q.shape[1] < q.shape[2]:
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warnings.warn(
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f"Input tensor shape suggests potential format mismatch: seq_len ({q.shape[1]}) < num_heads ({q.shape[2]}). "
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"This may indicate the inputs were passed in head-first format [B, H, T, ...] "
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"when head_first=False was specified. "
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"Please verify your input tensor format matches the expected shape [B, T, H, ...].",
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stacklevel=2)
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if cu_seqlens is not None:
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if q.shape[0] != 1:
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raise ValueError(
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f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`."
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f"Please flatten variable-length inputs before processing.")
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if initial_state is not None and initial_state.shape[0] != len(
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cu_seqlens) - 1:
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raise ValueError(
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f"The number of initial states is expected to be equal to the number of input sequences, "
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f"i.e., {len(cu_seqlens) - 1} rather than {initial_state.shape[0]}."
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)
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if scale is None:
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scale = k.shape[-1]**-0.5
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o, final_state = ChunkGatedDeltaRuleFunction.apply(
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q, k, v, g, beta, scale, initial_state, output_final_state, cu_seqlens,
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use_qk_l2norm_in_kernel)
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if head_first:
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o = rearrange(o, 'b t h ... -> b h t ...')
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return o, final_state
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