mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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83 lines
3.8 KiB
C++
83 lines
3.8 KiB
C++
/*
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* Copyright (c) 2019-2025, NVIDIA CORPORATION. All rights reserved.
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*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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 "cutlass_kernels/include/moe_kernels.h"
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#include "tensorrt_llm/common/cudaUtils.h"
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#include <cuda_bf16.h>
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#include <cuda_fp16.h>
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namespace tensorrt_llm::kernels
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{
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static inline size_t pad_to_multiple_of_16(size_t const& input)
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{
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static constexpr int ALIGNMENT = 16;
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return ALIGNMENT * ((input + ALIGNMENT - 1) / ALIGNMENT);
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}
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class CubKeyValueSorter
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{
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public:
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CubKeyValueSorter();
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CubKeyValueSorter(int const num_experts_per_node);
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void updateNumExperts(int const num_experts_per_node);
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static size_t getWorkspaceSize(size_t const num_key_value_pairs, int const num_experts_per_node);
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void run(void* workspace, size_t const workspace_size, int const* keys_in, int* keys_out, int const* values_in,
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int* values_out, size_t const num_key_value_pairs, cudaStream_t stream);
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private:
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static int expertsToBits(int experts);
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int num_experts_;
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int num_bits_;
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};
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bool fusedBuildExpertMapsSortFirstToken(int const* token_selected_experts, int* unpermuted_token_selected_experts,
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int* permuted_source_token_ids, int64_t* expert_first_token_offset, int64_t const num_tokens,
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int const num_experts_per_node, int const experts_per_token, int const start_expert, int const end_expert,
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cudaStream_t stream);
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void buildExpertMaps(int const* token_selected_experts, int* unpermuted_token_selected_experts,
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int* unpermuted_source_token_ids, int64_t const num_tokens, int const num_experts_per_node,
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int const experts_per_token, int const start_expert, int const end_expert, cudaStream_t stream);
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void generateTokenPermutation(int const* unpermuted_token_selected_experts, int const* unpermuted_source_token_ids,
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int* permuted_token_selected_experts, int* permuted_source_token_ids, int64_t* expert_first_token_offset,
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int64_t num_rows, int64_t num_experts_per_node, int64_t k, CubKeyValueSorter& sorter, void* sorter_ws,
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cudaStream_t stream);
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template <class InputActivationsType, class ExpandedActivationsType>
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void expandInputRowsKernelLauncher(InputActivationsType const* unpermuted_input,
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ExpandedActivationsType* permuted_output, float const* unpermuted_scales, float* permuted_scales,
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int const* expanded_dest_row_to_expanded_source_row, int* expanded_source_row_to_expanded_dest_row,
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int64_t const num_rows, int64_t const* num_valid_tokens_ptr, int64_t const cols, int const k,
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int const num_experts_per_node, float const* fc1_act_global_scale, int64_t* expert_first_token_offset,
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cutlass_kernels::TmaWarpSpecializedGroupedGemmInput::ElementSF* fc1_act_sf_flat,
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cutlass_kernels::TmaWarpSpecializedGroupedGemmInput::ElementSF const* input_sf, cudaStream_t stream);
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template <class OutputType, class GemmOutputType, class ScaleBiasType>
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void finalizeMoeRoutingKernelLauncher(GemmOutputType const* expanded_permuted_rows,
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OutputType* reduced_unpermuted_output, ScaleBiasType const* bias, float const* final_scales,
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int const* expanded_source_row_to_expanded_dest_row, int const* expert_for_source_row, int64_t const num_rows,
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int64_t const cols, int64_t const experts_per_token, int64_t const* num_valid_ptr,
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cutlass_kernels::MOEParallelismConfig parallelism_config, cudaStream_t stream);
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} // namespace tensorrt_llm::kernels
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