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/act_bias_mm.py

@@ -0,0 +1,89 @@
+import ixformer._C as ops
+import torch
+import torch.nn.functional as NNF
+
+__all__ = ["act_bias_mm", "ref_act_bias_mm"]
+
+
+def ref_act_bias_mm(
+    mat1: torch.Tensor,
+    mat2: torch.Tensor,
+    bias: torch.Tensor = None,
+    scale: float = 1,
+    act_type: str = "none",
+    trans_format: str = "NN",
+):
+    assert len(mat1.shape) >= 2
+    assert len(mat2.shape) >= 2
+    if trans_format == "NN":
+        if bias is not None:
+            output = torch.matmul(mat1, mat2) * scale + bias
+        else:
+            output = torch.matmul(mat1, mat2) * scale
+    else:
+        if bias is not None:
+            output = torch.matmul(mat1, mat2.transpose(-1, -2)) * scale + bias
+        else:
+            output = torch.matmul(mat1, mat2.transpose(-1, -2)) * scale
+    if act_type == "gelu":
+        output = NNF.gelu(output)
+    elif act_type == "relu":
+        output = NNF.relu(output)
+    elif act_type == "silu":
+        output = NNF.silu(output)
+    elif act_type == "none":
+        output = output
+    else:
+        raise NotImplementedError()
+    return output
+
+
+def act_bias_mm(
+    mat1: torch.Tensor,
+    mat2: torch.Tensor,
+    bias: torch.Tensor = None,
+    output: torch.Tensor = None,
+    scale: float = 1,
+    act_type: str = "none",
+    trans_format: str = "NN",
+):
+    """
+    Args:
+        mat1:               [m,k] or [batch_count,m,k]          torch.float16
+        mat2:               [k,n] or [n,k]                      torch.float16
+                当trans_format为"NN"时[k,n], 当trans_format为"TN"时[n,k]
+        bias:               [n]                                 torch.float16
+        output:             [m,n]                               torch.float16
+        scale:                                                  float
+        act_type:           silu/gelu/relu/None                 str
+                如果act_type不为None,则bias也不可以为None
+        trans_format:       NN or TN                            str
+    Returns:
+        output:             [m,n]                               torch.float16
+    """
+    assert len(mat1.shape) >= 2
+    assert len(mat2.shape) >= 2
+    if output is None:
+        output_shape = list(mat1.shape)
+        m = mat1.shape[-2]
+        if trans_format == "NN":
+            n = mat2.shape[-1]
+        else:
+            n = mat2.shape[-2]
+        output_shape[-2] = m
+        output_shape[-1] = n
+        output = mat1.new_empty(output_shape)
+
+    add_bias = False
+    if bias is not None:
+        add_bias = True
+
+    if add_bias:
+        ops.infer.act_bias_mm(
+            mat1, mat2, bias, output, add_bias, scale, act_type, trans_format
+        )
+    else:
+        ops.infer.act_bias_mm(
+            mat1, mat2, mat1, output, add_bias, scale, act_type, trans_format
+        )
+    return output