/* Copyright 2026 The xLLM Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    https://github.com/jd-opensource/xllm/blob/main/LICENSE

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "moe_fused_topk.h"

#include "kernels/ops_api.h"

namespace xllm {
namespace layer {

MoEFusedTopkImpl::MoEFusedTopkImpl(const ModelArgs& model_args,
                                   const QuantArgs& quant_args,
                                   const torch::TensorOptions& options)
    : topk_(model_args.num_experts_per_tok()),
      num_expert_group_(model_args.n_group()),
      topk_group_(model_args.topk_group()),
      route_scale_(model_args.routed_scaling_factor()),
      hidden_size_(model_args.hidden_size()),
      renormalize_(model_args.norm_topk_prob()),
      scoring_func_(model_args.scoring_func()) {
  const std::string& topk_method = model_args.topk_method();
  if (topk_method == "noaux_tc") {
    e_score_correction_bias_ = register_parameter(
        "e_score_correction_bias",
        torch::empty({model_args.n_routed_experts()}, options),
        false);
  }
}

// select the experts and return the reduce_weight and expert_id
std::tuple<torch::Tensor, torch::Tensor> MoEFusedTopkImpl::forward(
    torch::Tensor& router_logits) {
  std::optional<torch::Tensor> e_score_correction_bias = std::nullopt;
  if (e_score_correction_bias_.defined()) {
    e_score_correction_bias = e_score_correction_bias_;
  }

  xllm::kernel::MoeFusedTopkParams moe_active_topk_params;
  moe_active_topk_params.input = router_logits;
  moe_active_topk_params.topk = topk_;
  moe_active_topk_params.num_expert_group = num_expert_group_;
  moe_active_topk_params.topk_group = topk_group_;
  moe_active_topk_params.normalize = renormalize_;
  moe_active_topk_params.normed_by = "topk_logit";
  moe_active_topk_params.scoring_func = scoring_func_;
  moe_active_topk_params.route_scale = route_scale_;
  moe_active_topk_params.e_score_correction_bias = e_score_correction_bias;

  return xllm::kernel::moe_active_topk(moe_active_topk_params);
}

void MoEFusedTopkImpl::load_state_dict(const StateDict& state_dict) {
  if (e_score_correction_bias_.defined() &&
      !e_score_correction_bias_is_loaded_) {
    LOAD_WEIGHT(e_score_correction_bias);
  }
}
}  // namespace layer
}  // namespace xllm