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/rms_norm.py

@@ -0,0 +1,134 @@
+from typing import Union
+
+import ixformer._C as ops
+import torch
+from torch.nn import init
+
+__all__ = ["ref_rms_norm", "rms_norm", "ref_residual_rms_norm", "residual_rms_norm"]
+
+
+def ref_rms_norm(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    eps: float = 1e-5,
+    output: torch.Tensor = None,
+):
+    dtype = input.dtype
+    input = input.float()
+    weight = weight.float()
+    rms_out = input * torch.rsqrt(input.pow(2).mean(-1, keepdim=True) + eps)
+    rms_out = rms_out * weight
+    rms_out = rms_out.to(dtype)
+    if output is not None:
+        output.copy_(rms_out)
+    else:
+        output = rms_out
+    return output
+
+
+def rms_norm(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    eps: float = 1e-5,
+    output: torch.Tensor = None,
+):
+    """
+    This function is deprecated, please use residual_rms_norm.
+    Args:
+        input:              (..., hidden_size)    torch.float16, torch.bfloat16, torch.float32
+        weight:             (hidden_size)         torch.float16, torch.bfloat16, torch.float32
+        eps:                                      float32
+    Returns:
+        output:             (..., hidden_size)    torch.float16, torch.bfloat16, torch.float32
+    """
+    if output is None:
+        output = torch.empty_like(input)
+
+    ops.infer.rms_norm(input, weight, output, None, eps)
+
+    return output
+
+
+def ref_residual_rms_norm(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    eps: float = 1e-5,
+    residual_alpha: float = 1.0,
+    residual: torch.Tensor = None,
+    residual_bias: torch.Tensor = None,
+    output: torch.Tensor = None,
+    residual_output: torch.Tensor = None,
+    is_post: bool = False,
+):
+    dtype = input.dtype
+
+    if residual_bias is not None:
+        input = input + residual_bias
+
+    if residual is not None:
+        residual_output = torch.add(
+            input, residual * residual_alpha, out=residual_output
+        )
+        input = input.float() + residual.float() * residual_alpha
+    else:
+        input = input.float()
+    weight = weight.float()
+    rms_out = input * torch.rsqrt(input.pow(2).mean(-1, keepdim=True) + eps)
+    rms_out = rms_out * weight
+    rms_out = rms_out.to(dtype)
+    if output is not None:
+        output.copy_(rms_out)
+    else:
+        output = rms_out
+
+    if is_post and residual_output is not None:
+        residual_output = output
+
+    return output, residual_output
+
+
+def residual_rms_norm(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    eps: float = 1e-5,
+    residual_alpha: float = 1.0,
+    residual: torch.Tensor = None,
+    residual_bias: torch.Tensor = None,
+    output: torch.Tensor = None,
+    residual_output: torch.Tensor = None,
+    is_post: bool = False,
+):
+    """
+    Args:
+        input:              (..., hidden_size)    torch.float16, torch.bfloat16, torch.float32
+        weight:             (hidden_size)         torch.float16, torch.bfloat16, torch.float32
+        eps:                                      float32
+        residual_alpha:                           float32
+        residual:           (..., hidden_size)    torch.float16, torch.bfloat16, torch.float32
+        residual_bias:      (hidden_size)         torch.float16, torch.bfloat16, torch.float32
+        is_post:                                  bool
+    Returns:
+        output:             (..., hidden_size)    torch.float16, torch.bfloat16, torch.float32 If set to None, an inplace operation will be performed on input.
+        residual_output:    (..., hidden_size)    torch.float16, torch.bfloat16, torch.float32 If set to None, an inplace operation will be performed on residual.
+    """
+    if residual is None:
+        if output is None:
+            output = torch.empty_like(input)
+
+        ops.infer.rms_norm(input, weight, output, residual_bias, eps)
+    else:
+        ops.infer.residual_rms_norm(
+            input,
+            residual,
+            weight,
+            output,
+            residual_output,
+            residual_bias,
+            residual_alpha,
+            eps,
+            is_post,
+        )
+        residual_output = residual_output if residual_output is not None else residual
+        output = output if output is not None else input
+
+    return output, residual_output