6cdf2ec87b
来源:
1. fla-org/flash-linear-attention (5538 stars)
→ upstream_ref/fla/ops/gated_delta_rule/naive.py (正确的纯 PyTorch GDN)
→ upstream_ref/fla/ops/gated_delta_rule/chunk.py (Triton chunk kernel)
→ upstream_ref/fla/layers/gated_deltanet.py (层集成)
2. vllm-project/vllm main (88717 stars)
→ upstream_ref/vllm_gdn/gdn/qwen_gdn_linear_attn.py (1751行, Qwen3.5 原生 GDN)
→ upstream_ref/vllm_gdn/ops/causal_conv1d.py (1289行, 正确的 Conv1d)
→ upstream_ref/vllm_gdn/third_party/ops/ (FLA Triton ops vendored)
→ upstream_ref/vllm_gdn/models/qwen3_5.py (vllm 最新 Qwen3.5 模型)
3. Deep-Spark/xllm (BI-V100 硬件厂商)
→ upstream_ref/xllm_latest/core/layers/npu_torch/qwen3_gated_delta_net_base.cpp (576行)
→ upstream_ref/xllm_latest/core/kernels/npu/npu_causal_conv1d.cpp
→ upstream_ref/xllm_latest/core/kernels/npu/npu_recurrent_gated_delta_rule.cpp
目的: 修复 corex_gdn.py Conv1d groups 接口不匹配问题
错误: conv1d_weight shape (2560,1,4) 被当成 (num_k_heads,1,4) 索引
conv_dim = key_dim*2 + value_dim = 10240, TP=4 后 2560
FLA naive.py 和 vllm qwen_gdn_linear_attn.py 有正确的实现可直接对接
92 lines
3.2 KiB
Python
92 lines
3.2 KiB
Python
# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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# For a list of all contributors, visit:
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# https://github.com/fla-org/flash-linear-attention/graphs/contributors
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from .abc import chunk_abc
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from .attn import parallel_attn
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from .attnres import fused_attnres
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from .based import fused_chunk_based, parallel_based
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from .comba import chunk_comba, fused_recurrent_comba
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from .delta_rule import chunk_delta_rule, fused_chunk_delta_rule, fused_recurrent_delta_rule
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from .forgetting_attn import parallel_forgetting_attn
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from .gated_delta_rule import chunk_gated_delta_rule, chunk_gdn, fused_recurrent_gated_delta_rule, fused_recurrent_gdn
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from .generalized_delta_rule import (
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chunk_dplr_delta_rule,
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chunk_iplr_delta_rule,
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fused_recurrent_dplr_delta_rule,
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fused_recurrent_iplr_delta_rule,
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)
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from .gla import chunk_gla, fused_chunk_gla, fused_recurrent_gla
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from .gsa import chunk_gsa, fused_recurrent_gsa
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from .hgrn import fused_recurrent_hgrn
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from .kda import chunk_kda, fused_recurrent_kda
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from .lightning_attn import chunk_lightning_attn, fused_recurrent_lightning_attn
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from .linear_attn import chunk_linear_attn, fused_chunk_linear_attn, fused_recurrent_linear_attn
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from .log_linear_attn import chunk_log_linear_attn
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from .mesa_net import chunk_mesa_net
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from .nsa import parallel_nsa
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from .parallax import parallel_parallax
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from .path_attn import parallel_path_attn
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from .retention import chunk_retention, fused_chunk_retention, fused_recurrent_retention, parallel_retention
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from .rwkv6 import chunk_rwkv6, fused_recurrent_rwkv6
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from .rwkv7 import chunk_rwkv7, fused_recurrent_rwkv7
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from .simple_gla import chunk_simple_gla, fused_chunk_simple_gla, fused_recurrent_simple_gla, parallel_simple_gla
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from .wall_attn import parallel_wall_attn, parallel_wall_attn_decode
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__all__ = [
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'chunk_abc',
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'chunk_comba',
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'chunk_delta_rule',
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'chunk_dplr_delta_rule',
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'chunk_gated_delta_rule',
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'chunk_gdn',
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'chunk_gla',
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'chunk_gsa',
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'chunk_iplr_delta_rule',
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'chunk_kda',
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'chunk_lightning_attn',
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'chunk_linear_attn',
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'chunk_log_linear_attn',
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'chunk_mesa_net',
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'chunk_retention',
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'chunk_rwkv6',
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'chunk_rwkv7',
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'chunk_simple_gla',
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'fused_attnres',
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'fused_chunk_based',
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'fused_chunk_delta_rule',
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'fused_chunk_gla',
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'fused_chunk_linear_attn',
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'fused_chunk_retention',
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'fused_chunk_simple_gla',
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'fused_recurrent_comba',
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'fused_recurrent_delta_rule',
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'fused_recurrent_dplr_delta_rule',
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'fused_recurrent_gated_delta_rule',
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'fused_recurrent_gdn',
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'fused_recurrent_gla',
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'fused_recurrent_gsa',
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'fused_recurrent_hgrn',
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'fused_recurrent_iplr_delta_rule',
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'fused_recurrent_kda',
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'fused_recurrent_lightning_attn',
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'fused_recurrent_linear_attn',
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'fused_recurrent_retention',
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'fused_recurrent_rwkv6',
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'fused_recurrent_rwkv7',
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'fused_recurrent_simple_gla',
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'parallel_attn',
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'parallel_based',
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'parallel_forgetting_attn',
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'parallel_nsa',
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'parallel_parallax',
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'parallel_path_attn',
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'parallel_retention',
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'parallel_simple_gla',
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'parallel_wall_attn',
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'parallel_wall_attn_decode',
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]
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