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/inference/functions/softmax.py

@@ -0,0 +1,33 @@
+from typing import Union
+
+import ixformer._C as ops
+import torch
+from torch.autograd.function import Function, FunctionCtx
+
+__all__ = ["softmax", "ref_softmax"]
+
+
+def ref_softmax(input: torch.Tensor, dim: int = None, _stacklevel: int = 3, dtype=None):
+    out = torch.nn.functional.softmax(
+        input, dim=dim, _stacklevel=_stacklevel, dtype=dtype
+    )
+    return out
+
+
+def softmax(input: torch.Tensor, dim=None, _stacklevel=3, dtype=None):
+    
+    """
+    Args:
+        input:              (...)                                           torch.float16
+        dim:                                                                int
+            要进行softmax的维度,目前只支持最后一维， dim==-1 or dim == input.dim()-1
+        _stacklevel:                                                        int
+            这个参数只是为了与pytorch中对齐。 stacklevel is used in python to indicate warning mechanism how far up the stack it has to go to find the line that called the function which issued the warning.
+        dtype:                                                              torch.float16                        
+    Returns:
+        Tensor:             (...)                                           torch.float16
+    """    
+    output = torch.empty_like(input)
+    ops.infer.softmax(input, output, dim)
+    output = output.to(dtype)
+    return output