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/overlap_comm.py

@@ -0,0 +1,84 @@
+import itertools
+from functools import partial
+from typing import Callable, Dict, Iterable, Tuple
+
+import torch
+import torch.distributed as dist
+
+import ixformer.distributed as ixfd
+from ixformer.core.dispatcher import Dispatcher
+from ixformer.core.operator_autotuning import (
+    OperatorPreBaseRangeAutotuning,
+    sync_ranks_metric,
+)
+from ixformer.distributed import overlap_comm
+from ixformer.inference.overlap.linear_mlp_overlap_comm import linear_mlp_overlap
+from ixformer.distributed.overlap_comm import GemmMethod
+
+__all__ = ["linear_allreduce_overlap", "linear_mlp_overlap"]
+
+
+class LinearAllReducePreAutotuning(OperatorPreBaseRangeAutotuning, Dispatcher):
+    def __init__(self, comm_group, *args, **kwargs):
+        dist_barrier = True
+        if "dist_barrier" in kwargs:
+            dist_barrier = kwargs.pop("dist_barrier")
+
+        super().__init__(dist_barrier=dist_barrier, *args, **kwargs)
+        self._comm_group = comm_group
+        self._world_size = ixfd.get_group_world_size(comm_group)
+
+    @classmethod
+    def dispatcher_key(cls, comm_group, *args, **kwargs):
+        return (comm_group,)
+
+    def operators(self):
+        chunks = [2, 4]
+        gemm_algos = [GemmMethod.kCUINFER, GemmMethod.kCUBLAS, GemmMethod.kLIMITED_GEMM]
+        candidate_ops = [overlap_comm.GemmAllReduceSplitOverlapComm.native_forward]
+        for num_chunks, algo in itertools.product(chunks, gemm_algos):
+            candidate_ops.append(
+                partial(
+                    overlap_comm.linear_allreduce_overlap,
+                    num_chunks=num_chunks,
+                    gemm_method=algo,
+                )
+            )
+
+        return candidate_ops
+
+    @property
+    def _gemm_shapes(self):
+        basic_k = [4096, 6114, 8192]
+        tp_k = [k // self._world_size for k in basic_k]
+        basic_k = tp_k
+
+        basic_m = (512, 1024, 2048, 4096, 8192)
+
+        shapes = set(itertools.product(basic_m, basic_k))
+
+        return shapes
+
+    def get_operator_key(self, input, *args, **kwargs):
+        ndim = input.ndim
+        shape = input.shape
+
+        if ndim == 1:
+            return (1, shape[0])
+        elif ndim == 2:
+            return shape
+        else:
+            return (sum(shape[:-1]), shape[-1])
+
+    def generate_operator_inputs(self) -> Iterable[Tuple[Tuple, Dict]]:
+        for m, kn in self._gemm_shapes:
+            input = torch.randn(m, kn, device="cuda", dtype=torch.half)
+            weight = torch.randn(kn, kn, device="cuda", dtype=torch.half)
+            yield (input, weight), {}
+
+    def perf_operator_time(self, op: Callable, *args, **kwargs) -> float:
+        op_time = super().perf_operator_time(op, *args, **kwargs)
+        return sync_ranks_metric(op_time, group=self._comm_group)
+
+
+linear_allreduce_overlap = overlap_comm.linear_allreduce_overlap