72 lines
2.7 KiB
C++
72 lines
2.7 KiB
C++
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/* 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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