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/functions/linear.py

@@ -0,0 +1,89 @@
+import os
+from typing import Union
+
+import ixformer._C as ops
+import torch
+from torch.autograd.function import Function, FunctionCtx
+
+__all__ = ["linear"]
+
+
+class LinearFunction(Function):
+    @staticmethod
+    def forward(
+        ctx,
+        input: torch.Tensor,
+        weight: torch.Tensor,
+        bias: torch.Tensor = None,
+        output: torch.Tensor = None,
+    ):
+        if bias is not None:
+            if output is None:
+                output = ops.train.linear_forward(input, weight, bias)
+            else:
+                ops.train.linear_forward_(input, weight, bias, output)
+        else:
+            if output is None:
+                output = ops.train.linear_forward(input, weight)
+            else:
+                ops.train.linear_forward_(input, weight, output)
+
+        ctx.has_bias = bias is not None
+        ctx.save_for_backward(input, weight)
+
+        return output
+
+    @staticmethod
+    def backward(ctx: FunctionCtx, dy: torch.Tensor):
+        x, w = ctx.saved_tensors
+
+        dx = ops.train.linear_backward_dx(w, dy, x.shape)
+
+        dw = ops.train.linear_backward_dw(x, dy, w.shape)
+
+        if ctx.has_bias:
+            reduce_dims = list(range(dy.ndim - 1))
+            db = torch.sum(dy, reduce_dims)
+            return dx, dw, db, None
+        else:
+            return dx, dw, None, None
+
+
+def gemv_conditions(input, weight, bias, gemv_max_batch):
+    # gemv 使用的条件 input:[m,k] weight:[n,k]
+    # 1. m<=gemv_max_batch
+    # 2. k%2==0 n%2==0
+    # 3. bias is None
+    input = input.view(-1, input.shape[-1])
+    weight = weight.view(-1, weight.shape[-1])
+    m = input.shape[0]
+    k = input.shape[1]
+    n = weight.shape[0]
+    if bias is None and m <= gemv_max_batch and k % 2 == 0 and n % 2 == 0:
+        return True
+    return False
+
+
+def linear(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor = None,
+    output: torch.Tensor = None,
+    use_gemv: bool = True,
+    gemv_max_batch=1,
+):
+    """
+     Arguments:
+        input    :  [...,k]      dtype: [torch.half, torch.bfloat16]
+        weights :   [n,k]        dtype: [torch.half, torch.bfloat16]
+        use_gemv: bool           是否使用gemv
+            gemv 使用的条件 input:[m,k] weight:[n,k]
+                1. m<=gemv_max_batch
+                2. k%2==0 n%2==0
+                3. bias is None
+        gemv_max_batch: int       用于是否满足gemv使用条件的判断
+    Return:
+        output  :   [...,n]      dtype: [torch.half, torch.bfloat16]
+
+    """
+    return LinearFunction.apply(input, weight, bias, output)