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/llama.py

@@ -0,0 +1,104 @@
+import warnings
+import types
+from abc import ABC, abstractmethod
+from functools import partial
+from typing import Callable, Dict, List, Union
+
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import Module
+
+from ixformer.train.speedformer.policy.utils import SubModuleReplacementDescription
+from ixformer.train.speedformer.policy.replacer import Replacer
+
+from ixformer.train.speedformer.layers.normalization import APEXFusedRMSNorm, IXFFusedRMSNorm
+from ixformer.train.speedformer.layers.llama.attention import LlamaAttention as IXF_LlamaAttention
+from ixformer.train.speedformer.layers.llama.mlp import IXFLlamaMLP
+from ixformer.train.speedformer.layers.llama.llama_method import LlamaModel_forward, LlamaForCausalLM_forward
+from ixformer.train.speedformer.layers.fast_lora.fast_lora import apply_lora_mlp_swiglu
+
+from peft import PeftType
+
+
+class LlamaReplacer(Replacer):
+    def __init__(self):
+        self.policy = {}
+
+    def module_policy(self) -> Dict[Union[str, nn.Module], List[SubModuleReplacementDescription]]:
+        self.append_or_create_submodule_replacement(
+            description=[
+                SubModuleReplacementDescription(
+                    suffix="input_layernorm",
+                    target_module=APEXFusedRMSNorm,
+                    kwargs={}
+                ),
+                SubModuleReplacementDescription(
+                    suffix="post_attention_layernorm",
+                    target_module=APEXFusedRMSNorm,
+                    kwargs={},
+                ),
+                SubModuleReplacementDescription(
+                    suffix="self_attn",
+                    target_module=IXF_LlamaAttention,
+                    kwargs={}
+                ),
+                # SubModuleReplacementDescription(
+                #     suffix="mlp",
+                #     target_module=IXFLlamaMLP,
+                #     kwargs={}
+                # ),
+            ],
+            target_key="LlamaDecoderLayer"
+        )
+
+        self.append_or_create_submodule_replacement(
+            description=[
+                SubModuleReplacementDescription(
+                    suffix="norm",
+                    target_module=APEXFusedRMSNorm,
+                    kwargs={}
+                ),
+            ],
+            target_key="LlamaModel"
+        )
+
+        self.append_or_create_method_replacement(
+            description=[
+                {"forward": LlamaModel_forward()}
+            ],
+            target_key="LlamaModel"
+        )
+        self.append_or_create_method_replacement(
+            description=[
+                {"forward": LlamaForCausalLM_forward()}
+            ],
+            target_key="LlamaForCausalLM"
+        )
+
+    def post_process(self, model: nn.Module):
+        if model.peft_type != PeftType.LORA:
+            return
+        peft_config = model.peft_config
+        active_adapter = model.active_adapters[0] if \
+            hasattr(model, "active_adapters") else model.active_adapter
+        target_modules = peft_config[active_adapter].target_modules
+
+        # for now, fast_lora only support lora_dropout=0 and bias=None
+        lora_dropout = model.peft_config[active_adapter].lora_dropout
+        bias = model.peft_config[active_adapter].bias
+
+        # 首先判断是否可以使用fast_lora
+        check = lora_dropout == 0 and bias == "none"
+
+        # 其次确定mlp的3个线性层是否在target_modules
+        mlp_use_fastlora = "gate_proj" in target_modules and "up_proj" in target_modules and "up_proj" in target_modules
+
+        n_mlp = 0
+        if check:
+            if mlp_use_fastlora:
+                for layer in model.model.model.layers:
+                    layer.mlp.forward = types.MethodType(
+                        apply_lora_mlp_swiglu, layer.mlp)
+                    n_mlp += 1
+
+        print(f"{len(model.model.model.layers)} layers replace mlp with fast_lora mlp")