f4e2264a83
Copied from upstream_ref (NOT rewritten — exact upstream code):
ixformer C++ API (the authoritative header):
include/ixformer.h — ixformer::infer namespace: topk_softmax,
moe_compute_token_index_api, moe_w16a16_group_gemm, moe_expand_input,
moe_output_reduce_sum, silu_and_mul, rms_norm, xllm_paged_attention, etc.
include/ilu_ops_api.h — xllm::kernel::ilu namespace: moe_active_topk,
moe_gen_idx, moe_expand_input, group_gemm, moe_combine_result,
batch_prefill, batch_decode, rms_norm, matmul, act_and_mul, etc.
ILU kernel wrappers (call ixformer::infer directly):
csrc/ilu_kernel_fused_moe.cpp — topk routing + gen_idx + expand + combine
csrc/ilu_kernel_group_gemm.cpp — batched expert GEMM
csrc/ilu_kernel_{activation,norm,rope,matmul,attention}.cpp
ILU layer implementations (full pipeline):
csrc/ilu_layer_fused_moe.{cpp,h} — 797 lines, the complete MoE pipeline
that competitor 168 ran as corex_moe.py
csrc/ilu_layer_attention.{cpp,h} — prefill/decode attention dispatch
CUDA MoE kernels (from xllm + ds_vllm):
csrc/moe/moe_topk_softmax_kernels.cuh — CUB BlockReduce + warp topk
csrc/moe/moe_topk_sigmoid_kernels.cuh — sigmoid scoring variant
csrc/moe/moe_topk.cuh + moe_fused_topk.cu — entry points
csrc/moe/moeTopKFuncs.cuh — TRT-LLM derived vllm-compatible topk
csrc/moe/moe_ops.h + moe_align_sum_kernels.cu — alignment kernels
Common layer headers:
csrc/common_fused_moe{,_base}.h + common_moe_fused_topk.{cpp,h}
72 lines
2.7 KiB
C++
72 lines
2.7 KiB
C++
/* Copyright 2026 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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#include "moe_fused_topk.h"
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#include "kernels/ops_api.h"
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namespace xllm {
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namespace layer {
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MoEFusedTopkImpl::MoEFusedTopkImpl(const ModelArgs& model_args,
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const QuantArgs& quant_args,
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const torch::TensorOptions& options)
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: topk_(model_args.num_experts_per_tok()),
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num_expert_group_(model_args.n_group()),
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topk_group_(model_args.topk_group()),
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route_scale_(model_args.routed_scaling_factor()),
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hidden_size_(model_args.hidden_size()),
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renormalize_(model_args.norm_topk_prob()),
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scoring_func_(model_args.scoring_func()) {
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const std::string& topk_method = model_args.topk_method();
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if (topk_method == "noaux_tc") {
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e_score_correction_bias_ = register_parameter(
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"e_score_correction_bias",
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torch::empty({model_args.n_routed_experts()}, options),
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false);
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}
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}
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// select the experts and return the reduce_weight and expert_id
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std::tuple<torch::Tensor, torch::Tensor> MoEFusedTopkImpl::forward(
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torch::Tensor& router_logits) {
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std::optional<torch::Tensor> e_score_correction_bias = std::nullopt;
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if (e_score_correction_bias_.defined()) {
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e_score_correction_bias = e_score_correction_bias_;
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}
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xllm::kernel::MoeFusedTopkParams moe_active_topk_params;
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moe_active_topk_params.input = router_logits;
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moe_active_topk_params.topk = topk_;
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moe_active_topk_params.num_expert_group = num_expert_group_;
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moe_active_topk_params.topk_group = topk_group_;
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moe_active_topk_params.normalize = renormalize_;
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moe_active_topk_params.normed_by = "topk_logit";
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moe_active_topk_params.scoring_func = scoring_func_;
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moe_active_topk_params.route_scale = route_scale_;
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moe_active_topk_params.e_score_correction_bias = e_score_correction_bias;
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return xllm::kernel::moe_active_topk(moe_active_topk_params);
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}
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void MoEFusedTopkImpl::load_state_dict(const StateDict& state_dict) {
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if (e_score_correction_bias_.defined() &&
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!e_score_correction_bias_is_loaded_) {
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LOAD_WEIGHT(e_score_correction_bias);
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}
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}
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} // namespace layer
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} // namespace xllm
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