392 lines
16 KiB
Python
392 lines
16 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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# mypy: ignore-errors
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import warnings
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import torch
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from einops import rearrange
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from vllm.distributed import get_pcp_group
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from vllm.forward_context import get_forward_context
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from vllm.model_executor.layers.fla.ops.utils import SUPPRESS_LEVEL
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from vllm_ascend.ops.gdn_attn_builder import _compact_empty_segments
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from .chunk_delta_h import chunk_gated_delta_rule_fwd_h # noqa: F401
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from .chunk_delta_hupdate import chunk_gated_delta_rule_fwd_hupdate
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from .chunk_o import chunk_fwd_o # noqa: F401
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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 input_guard, prepare_final_chunk_indices
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from .wy_fast import recompute_w_u_fwd
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def chunk_gated_delta_rule_fwd(
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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: torch.LongTensor | None = None,
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prebuilt_meta=None,
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):
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forward_context = get_forward_context()
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num_decodes = 0
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attn_metadata = forward_context.attn_metadata
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if attn_metadata is not None and isinstance(attn_metadata, dict):
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attn_metadata = next(iter(attn_metadata.values()), None)
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if attn_metadata is not None:
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num_decodes = attn_metadata.num_decodes
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chunk_size = 64
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block_indices_cumsum = None if prebuilt_meta is None else prebuilt_meta.block_indices_cumsum
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cu_seqlens_host = None if prebuilt_meta is None else prebuilt_meta.cu_seqlens_host
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chunk_indices_chunk64 = None if prebuilt_meta is None else prebuilt_meta.chunk_indices_chunk64
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chunk_indices_chunk64_host = None if prebuilt_meta is None else prebuilt_meta.chunk_indices_chunk64_host
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chunk_offsets_chunk64 = None if prebuilt_meta is None else prebuilt_meta.chunk_offsets_chunk64
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update_chunk_offsets_chunk64 = None if prebuilt_meta is None else prebuilt_meta.update_chunk_offsets_chunk64
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final_chunk_indices_chunk64 = None if prebuilt_meta is None else prebuilt_meta.final_chunk_indices_chunk64
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chunk_indices_large_block = None if prebuilt_meta is None else prebuilt_meta.chunk_indices_large_block
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g = chunk_local_cumsum(
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g,
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chunk_size=chunk_size,
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cu_seqlens=cu_seqlens,
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block_indices=block_indices_cumsum,
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)
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# obtain WY representation. u is actually the new v.
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A = chunk_scaled_dot_kkt_fwd(
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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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chunk_indices=chunk_indices_chunk64,
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output_dtype=torch.float32,
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)
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A = solve_tril(
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A=A,
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cu_seqlens=cu_seqlens,
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chunk_indices_large_block=chunk_indices_large_block,
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chunk_indices_bt=chunk_indices_chunk64,
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output_dtype=k.dtype,
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)
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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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chunk_indices=chunk_indices_chunk64,
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)
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k_ascendc = k.to(torch.bfloat16).transpose(1, 2).contiguous()
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w_ascendc = w.to(torch.bfloat16).transpose(1, 2).contiguous()
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u_ascendc = u.to(torch.bfloat16).transpose(1, 2).contiguous()
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g_ascendc = g.transpose(1, 2).contiguous()
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q_ascendc = q.to(torch.bfloat16).transpose(1, 2).contiguous()
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cu_seqlens = None if cu_seqlens is None else cu_seqlens.to(torch.int64)
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chunk_indices = None if chunk_indices_chunk64 is None else chunk_indices_chunk64.to(torch.int64)
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if cu_seqlens_host is None and cu_seqlens is not None:
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cu_seqlens_host = tuple(cu_seqlens.tolist())
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if chunk_indices_chunk64_host is None and chunk_indices is not None:
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chunk_indices_chunk64_host = tuple(chunk_indices.flatten().tolist())
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# Compact zero-length segments for the AscendC kernels (see
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# _compact_empty_segments). chunk_indices_chunk64 is already compact-
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# ranked and is reused as-is; only cu_seqlens / initial_state need
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# compacting.
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if prebuilt_meta is not None and hasattr(prebuilt_meta, "keep_meta"):
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cu_seqlens_kern = cu_seqlens_host if prebuilt_meta.cu_seqlens_kern is None else prebuilt_meta.cu_seqlens_kern
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keep_meta = prebuilt_meta.keep_meta
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initial_state_kern = (
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initial_state[keep_meta] if initial_state is not None and keep_meta is not None else initial_state
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)
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else:
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cu_seqlens_kern, initial_state_kern, keep_meta = _compact_empty_segments(
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cu_seqlens_host,
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initial_state,
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device=initial_state.device if initial_state is not None else None,
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)
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h, v_new, final_state = torch.ops._C_ascend.chunk_gated_delta_rule_fwd_h(
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k_ascendc,
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w_ascendc,
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u_ascendc,
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g=g_ascendc,
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gk=None,
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initial_state=initial_state_kern,
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output_final_state=True,
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chunk_size=64,
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save_new_value=True,
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cu_seqlens=cu_seqlens_kern,
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chunk_indices=chunk_indices_chunk64_host,
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use_exp2=False,
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transpose_state_layout=False,
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)
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if keep_meta is not None:
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# Scatter the compacted final_state back to the original [N, H, K, V]
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# layout the PCP state recursion expects; empty segments keep their
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# initial state.
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_fs_full = initial_state.clone()
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_fs_full[keep_meta] = final_state
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final_state = _fs_full
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if get_pcp_group().world_size > 1:
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# When integrating mtp, since `mix_qkv` has been split, `num_decode`
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# cannot be directly obtained from the metadata and needs to be recalculated.
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actual_num_decodes = getattr(prebuilt_meta, "num_decodes", None)
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if actual_num_decodes is None:
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actual_num_decodes = num_decodes
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h_update = chunk_gated_delta_rule_fwd_hupdate(
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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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cu_seqlens=cu_seqlens,
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chunk_indices=chunk_indices_chunk64,
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chunk_offsets=chunk_offsets_chunk64,
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update_chunk_offsets=update_chunk_offsets_chunk64,
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num_decodes=actual_num_decodes,
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)
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all_final_state = get_pcp_group().all_gather(final_state.unsqueeze(0), 0)
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final_chunk_indices = final_chunk_indices_chunk64
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if final_chunk_indices is None:
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final_chunk_indices = prepare_final_chunk_indices(cu_seqlens, chunk_size)
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final_h_update = h_update[:, final_chunk_indices, :, :, :]
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all_final_h_update = get_pcp_group().all_gather(final_h_update, 0)
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updated_state = final_state.new_empty(get_pcp_group().world_size, *final_state.shape)
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updated_state[0, ...] = all_final_state[0]
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for i in range(1, get_pcp_group().world_size):
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# correct_i = all_final_state[i] + Phi_i * (correct_{i-1} - s0)
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updated_final_state = all_final_state[i] + torch.matmul(
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all_final_h_update[i, ...], updated_state[i - 1, ...] - initial_state
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)
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updated_state[i, ...] = updated_final_state
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final_state = updated_state[-1, ...]
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if get_pcp_group().rank_in_group == 0:
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updated_h_state = torch.zeros_like(final_state)
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else:
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updated_h_state = updated_state[get_pcp_group().rank_in_group - 1, ...]
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if get_pcp_group().rank_in_group > 0:
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rerun_initial_state = initial_state.clone()
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prefill_seq_offset = actual_num_decodes
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prefill_slice = slice(prefill_seq_offset, final_state.shape[0])
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rerun_initial_state[prefill_slice] = updated_h_state[prefill_slice]
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h, v_new, _ = 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=rerun_initial_state,
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output_final_state=True,
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cu_seqlens=cu_seqlens,
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chunk_indices=chunk_indices_chunk64,
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chunk_offsets=chunk_offsets_chunk64,
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)
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h = h.transpose(1, 2).contiguous()
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v_new = v_new.transpose(1, 2).contiguous()
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o_ascendc = torch.ops._C_ascend.chunk_fwd_o(
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q_ascendc,
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k_ascendc,
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v_new,
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h,
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scale,
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g=g_ascendc,
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g_gamma=None,
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cu_seqlens=cu_seqlens_host,
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chunk_indices=chunk_indices_chunk64_host,
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chunk_size=64,
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transpose_state_layout=False,
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)
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o = o_ascendc.to(torch.bfloat16).transpose(1, 2).contiguous()
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v_new = v_new.to(torch.bfloat16).transpose(1, 2).contiguous()
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h = h.to(torch.bfloat16).transpose(1, 2).contiguous()
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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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def forward(
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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: torch.LongTensor | None = None,
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prebuilt_meta=None,
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use_qk_l2norm_in_kernel: bool = False,
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):
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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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prebuilt_meta=prebuilt_meta,
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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(
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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 = 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: torch.LongTensor | None = None,
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prebuilt_meta=None,
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head_first: bool = False,
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use_qk_l2norm_in_kernel: bool = False,
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chunk_indices: torch.Tensor | None = None,
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chunk_offsets: torch.Tensor | None = None,
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core_attn_out: torch.Tensor | None = None,
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):
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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(beta.shape) == 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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"chunk_gated_delta_rule: 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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)
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q, k, v, beta, g = map(lambda x: rearrange(x, "b h t ... -> b t h ..."), (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"chunk_gated_delta_rule: 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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)
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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"chunk_gated_delta_rule: 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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)
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if initial_state is not None and initial_state.shape[0] != len(cu_seqlens) - 1:
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raise ValueError(
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f"chunk_gated_delta_rule: 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,
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k,
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v,
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g,
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beta,
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scale,
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initial_state,
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output_final_state,
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cu_seqlens,
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prebuilt_meta,
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use_qk_l2norm_in_kernel,
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)
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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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