ref(upstream): 搬运 3 大 GDN 上游仓库 — FLA naive ops + vllm GDN 子树 + xllm C++ 参考
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
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