6415249693
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
67 lines
2.2 KiB
C++
67 lines
2.2 KiB
C++
/* Copyright 2025 The xLLM Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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https://github.com/jd-opensource/xllm/blob/main/LICENSE
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#pragma once
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#include <torch/torch.h>
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#include "activation.h"
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#include "framework/model/model_args.h"
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#include "framework/parallel_state/parallel_args.h"
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#include "framework/quant_args.h"
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#include "framework/state_dict/state_dict.h"
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#include "linear.h"
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namespace xllm {
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namespace layer {
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class DenseMLPImpl : public torch::nn::Module {
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public:
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DenseMLPImpl() = default;
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DenseMLPImpl(int64_t hidden_size,
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int64_t intermediate_size,
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bool is_gated,
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bool has_bias,
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const std::string& hidden_act,
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bool enable_result_reduction,
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const QuantArgs& quant_args,
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ProcessGroup* process_group,
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const torch::TensorOptions& options,
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const std::string& module_prefix = "");
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torch::Tensor forward(const torch::Tensor& hidden_states);
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void load_state_dict(const StateDict& state_dict);
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void load_state_dict(const StateDict& state_dict,
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const std::vector<std::string>& gate_up_name,
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const std::string& down_name);
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// Get FP8 input scale from gate_up_proj for fused RMSNorm+FP8 quantization
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std::optional<torch::Tensor> get_fp8_input_scale() const;
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private:
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bool is_gated_;
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int64_t intermediate_size_;
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ProcessGroup* process_group_;
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ColumnParallelLinear gate_up_proj_{nullptr};
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RowParallelLinear down_proj_{nullptr};
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Activation act_{nullptr};
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bool is_smoothquant_;
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std::string hidden_act_;
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};
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TORCH_MODULE(DenseMLP);
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} // namespace layer
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} // namespace xllm
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