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/i8w8o32.py

@@ -0,0 +1,32 @@
+import os
+from typing import Union
+
+import ixformer._C as ops
+import torch
+
+__all__ = ["i8w8o32", "ref_i8w8o32"]
+
+
+def ref_i8w8o32(input: torch.Tensor, weight: torch.Tensor):
+    output = torch.nn.functional.linear(input.float(), weight.float()).int()
+    return output
+
+
+def i8w8o32(input: torch.Tensor, weight: torch.Tensor):
+    
+    """
+    Args:
+        input:              (bs, ic)              torch.int8
+        weight:             (oc, ic)              torch.int8
+    Returns:
+        Tensor:             (bs, oc))             torch.int32
+    """
+    if not torch.is_tensor(input):
+        raise RuntimeError("Not impl.")
+    output_shape = list(input.shape)
+    output_shape[-1] = weight.size(0)
+    output = torch.empty(output_shape, dtype=torch.int32, device=input.device)
+    ic_dim = input.size(-1)
+    input = input.view(-1, ic_dim)
+    ops.infer.linear_i8w8o32(input.view(-1, ic_dim), weight, output)
+    return output