feat(CRITICAL): 从 GitHub 扫描搬运 ixformer SDK + xllm 完整 GDN/MoE 代码

来源:
  1. Chranos/ixformer (GitHub) → ixformer_sdk/ (230 files, 70K lines)
     - inference/functions/vllm.py: vllm_moe_topk_softmax 完整实现 (2033 lines)
     - inference/functions/moe.py: MoE ops 完整实现 (1380 lines)
     - contrib/vllm_flash_attn/: FA2 Python 接口 (1018 lines)
     - contrib/tgi/fused_moe.py: TGI fused MoE (429 lines)
     - csrc/include/ixformer/: C++ kernel headers + cmake

  2. Deep-Spark/xllm (GitHub) → upstream_ref/xllm_latest/ (+15 files)
     - npu_torch/qwen3_5_decoder_layer_impl.cpp/.h
     - npu_torch/qwen3_5_gated_delta_net.cpp/.h
     - npu_torch/qwen3_next_*.cpp/.h (6 files)
     - npu_torch/attention.cpp/.h + fused_moe.cpp/.h + CMakeLists.txt
     - models/llm/qwen3_5.h + qwen3_5_mtp.h + qwen3_next.h
     - models/vlm/qwen3_5.h

调用链完整性:
  ixformer_sdk/inference/functions/vllm.py
    → ops.infer.moe_topk_softmax() (C++ 层)
    → 这就是 base 镜像 libixformer.so 里的实现

  upstream_ref/xllm_latest/core/layers/ilu/fused_moe.cpp
    → ixformer::infer::topk_softmax() (直接 C++ 调用)
    → ixformer::infer::group_gemm() → 完整 7-step MoE pipeline

ixformer_sdk/train/speedformer/policy/gpt2.py

@@ -0,0 +1,27 @@
+import torch
+import torch.nn as nn
+from torch.nn import LayerNorm
+from types import ModuleType, MethodType
+from abc import ABC
+
+from ixformer.train.speedformer.models.gpt2.modeling_gpt2 import GPT2FlashAttention2
+
+from ixformer.train.speedformer.layers.normalization import replace_layernorm_forward
+from ixformer.train.speedformer.layers.gpt2.attention import replace_flash_attn_forward
+
+
+class GPT2Replacer(ABC):
+    def __init__(self) -> None:
+        super().__init__()
+
+    @staticmethod
+    def accelerate(model):
+        # layer/kernel replace
+        for name, module in model.named_modules():
+            if isinstance(module, LayerNorm):
+                module.forward = MethodType(replace_layernorm_forward, module)
+            if isinstance(module, GPT2FlashAttention2):
+                module._flash_attention_forward = MethodType(
+                    replace_flash_attn_forward, module)
+
+        return model