57 lines
2.0 KiB
Plaintext
57 lines
2.0 KiB
Plaintext
/* 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 "kernels/cuda/cuda_ops_api.h"
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#include "moe_topk_sigmoid_kernels.cuh"
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#include "moe_topk_softmax_kernels.cuh"
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namespace xllm::kernel::cuda {
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std::tuple<torch::Tensor, torch::Tensor> moe_fused_topk(
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torch::Tensor& gating_output,
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int64_t topk,
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bool renormalize,
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const std::optional<torch::Tensor>& correction_bias,
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const std::string& scoring_func) {
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int64_t num_tokens = gating_output.size(0);
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torch::Tensor topk_weights = torch::empty(
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{num_tokens, topk},
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torch::dtype(torch::kFloat32).device(gating_output.device()));
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torch::Tensor topk_ids =
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torch::empty({num_tokens, topk},
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torch::dtype(torch::kInt32).device(gating_output.device()));
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if (scoring_func == "softmax") {
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std::optional<torch::Tensor> none_correction_bias = std::nullopt;
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topk_softmax(topk_weights,
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topk_ids,
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gating_output,
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renormalize,
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/*moe_softcapping=*/0.0,
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none_correction_bias);
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} else if (scoring_func == "sigmoid") {
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topk_sigmoid(
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topk_weights, topk_ids, gating_output, renormalize, correction_bias);
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} else {
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LOG(FATAL) << "Unsupported scoring function for moe topk: " << scoring_func
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<< "only softmax and sigmoid are supported";
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}
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return std::make_tuple(topk_weights, topk_ids);
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}
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} // namespace xllm::kernel::cuda
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