data: port complete MoE + xllm layer call chains from upstream repos

MoE call chain from ds_vllm (vllm-project/vllm latest):
  ex_engine/moe/ — 20 files, 8736 lines
  - modular_kernel.py (1630 lines) — base classes for modular MoE
  - experts/fused_batched_moe.py (972 lines) — NaiveBatchedExperts
  - prepare_finalize/batched.py (171 lines) — token grouping by expert
  - topk_weight_and_reduce.py (176 lines) — scatter-add finalize
  - fused_moe.py (1740 lines) — main fused_moe dispatch
  - config.py (1407 lines) — FusedMoEQuantConfig
  - activation.py, utils.py, layer.py, etc.

xllm layer code (jd-opensource/xllm):
  ex_engine/xllm_layers/ — 39 files, 5859 lines
  - ilu/fused_moe.cpp (797 lines) — production ixformer 7-step MoE pipeline
  - ilu/attention.cpp (189 lines) — paged_attention + flash_attn bridge
  - npu_torch/qwen3_gated_delta_net_base.cpp (576 lines) — GDN reference
  - common/rms_norm.cpp, rotary_embedding.cpp, activation.cpp, dense_mlp.cpp

xllm ILU kernels — synced 10 files to upstream (diffs from prior edits)

These are reference implementations, NOT hand-written.
Source repos: vllm-project/vllm, jd-opensource/xllm

ex_engine/moe/__init__.py

@@ -1,10 +1,160 @@
-"""
-ex_engine.moe — MoE expert computation for BI-V100
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-Ported from:
-  upstream_ref/ds_vllm/vllm/model_executor/layers/fused_moe/experts/fused_batched_moe.py
-  upstream_ref/ds_vllm/vllm/model_executor/layers/fused_moe/activation.py
-"""
+from contextlib import contextmanager
+from typing import Any
 
-from ex_engine.moe.naive_batched_experts import naive_batched_moe_forward
-from ex_engine.moe.activation import MoEActivation, apply_moe_activation
+from vllm.model_executor.layers.fused_moe.activation import (
+    MoEActivation,
+    activation_without_mul,
+    apply_moe_activation,
+)
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEParallelConfig,
+    FusedMoEQuantConfig,
+    RoutingMethodType,
+)
+from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
+    FusedMoEMethodBase,
+)
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE,
+    fused_moe_make_expert_params_mapping,
+)
+from vllm.model_executor.layers.fused_moe.modular_kernel import (
+    FusedMoEActivationFormat,
+    FusedMoEExpertsModular,
+    FusedMoEPrepareAndFinalizeModular,
+)
+from vllm.model_executor.layers.fused_moe.routed_experts import (
+    FusedMoeWeightScaleSupported,
+    RoutedExperts,
+)
+from vllm.model_executor.layers.fused_moe.router.fused_moe_router import (
+    FusedMoERouter,
+)
+from vllm.model_executor.layers.fused_moe.router.gate_linear import GateLinear
+from vllm.model_executor.layers.fused_moe.runner.moe_runner import (
+    MoERunner,
+)
+from vllm.model_executor.layers.fused_moe.runner.shared_experts import (
+    SharedExperts,
+)
+from vllm.model_executor.layers.fused_moe.unquantized_fused_moe_method import (
+    UnquantizedFusedMoEMethod,
+)
+from vllm.triton_utils import HAS_TRITON
+
+_config: dict[str, Any] | None = None
+
+
+@contextmanager
+def override_config(config):
+    global _config
+    old_config = _config
+    _config = config
+    yield
+    _config = old_config
+
+
+def get_config() -> dict[str, Any] | None:
+    return _config
+
+
+__all__ = [
+    "FusedMoE",
+    "FusedMoERouter",
+    "FusedMoEConfig",
+    "FusedMoEQuantConfig",
+    "FusedMoEParallelConfig",
+    "FusedMoEMethodBase",
+    "MoEActivation",
+    "UnquantizedFusedMoEMethod",
+    "FusedMoeWeightScaleSupported",
+    "FusedMoEExpertsModular",
+    "FusedMoEActivationFormat",
+    "FusedMoEPrepareAndFinalizeModular",
+    "GateLinear",
+    "MoERunner",
+    "RoutingMethodType",
+    "RoutedExperts",
+    "SharedExperts",
+    "activation_without_mul",
+    "apply_moe_activation",
+    "fused_moe_make_expert_params_mapping",
+    "override_config",
+    "get_config",
+]
+
+if HAS_TRITON:
+    # import to register the custom ops
+    from vllm.model_executor.layers.fused_moe.experts.batched_deep_gemm_moe import (
+        BatchedDeepGemmExperts,
+    )
+    from vllm.model_executor.layers.fused_moe.experts.cutlass_moe import (
+        CutlassBatchedExpertsFp8,
+        CutlassExpertsFp8,
+        CutlassExpertsW4A8Fp8,
+    )
+    from vllm.model_executor.layers.fused_moe.experts.deep_gemm_moe import (
+        DeepGemmExperts,
+    )
+    from vllm.model_executor.layers.fused_moe.experts.fused_batched_moe import (
+        BatchedTritonExperts,
+    )
+    from vllm.model_executor.layers.fused_moe.experts.rocm_aiter_moe import (
+        AiterExperts,
+    )
+    from vllm.model_executor.layers.fused_moe.experts.triton_deep_gemm_moe import (
+        TritonOrDeepGemmExperts,
+    )
+    from vllm.model_executor.layers.fused_moe.experts.triton_moe import (
+        TritonExperts,
+        TritonWNA16Experts,
+    )
+    from vllm.model_executor.layers.fused_moe.experts.xpu_moe import (
+        XPUExperts,
+        XPUExpertsFp8,
+        XPUExpertsMxFp4,
+    )
+    from vllm.model_executor.layers.fused_moe.fused_moe import (
+        fused_experts,
+        get_config_file_name,
+    )
+    from vllm.model_executor.layers.fused_moe.router.fused_topk_router import (
+        fused_topk,
+    )
+    from vllm.model_executor.layers.fused_moe.router.grouped_topk_router import (
+        GroupedTopk,
+    )
+
+    __all__ += [
+        "AiterExperts",
+        "fused_topk",
+        "fused_experts",
+        "get_config_file_name",
+        "GroupedTopk",
+        "CutlassExpertsFp8",
+        "CutlassBatchedExpertsFp8",
+        "CutlassExpertsW4A8Fp8",
+        "TritonExperts",
+        "TritonWNA16Experts",
+        "BatchedTritonExperts",
+        "DeepGemmExperts",
+        "BatchedDeepGemmExperts",
+        "TritonOrDeepGemmExperts",
+        "XPUExperts",
+        "XPUExpertsFp8",
+        "XPUExpertsBlockFp8",
+        "XPUExpertsMxFp8",
+        "XPUExpertsMxFp4",
+    ]
+else:
+    # Some model classes directly use the custom ops. Add placeholders
+    # to avoid import errors.
+    def _raise_exception(method: str):
+        raise NotImplementedError(f"{method} is not implemented as lack of triton.")
+
+    fused_topk = lambda *args, **kwargs: _raise_exception("fused_topk")
+    fused_experts = lambda *args, **kwargs: _raise_exception("fused_experts")