mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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82 lines
2.3 KiB
C++
82 lines
2.3 KiB
C++
/*
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* Copyright (c) 2019-2024, 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 <algorithm>
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#include <optional>
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#include <utility>
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#include <vector>
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#include <cuda_runtime.h>
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#include "tensorrt_llm/common/assert.h"
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#include "tensorrt_llm/common/memoryUtils.h"
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#include "tensorrt_llm/kernels/decodingCommon.h"
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namespace tensorrt_llm
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{
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namespace layers
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{
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// Using a local lambda in beam search layers to fill buffers causes an internal compiler error on nvcc windows.
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// As a workaround and to promote DRY, the fill logic is refactored into FillBuffers below.
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struct FillBuffers
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{
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template <typename T>
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void operator()(std::optional<std::vector<T>> const& optParam, T const defaultValue, std::vector<T>& hostBuffer,
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T* deviceBuffer, int32_t const* batchSlots) const
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{
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using tensorrt_llm::common::cudaAutoCpy;
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for (size_t bi = 0; bi < batchSize; ++bi)
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{
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auto const batchSlot = batchSlots ? batchSlots[bi] : bi;
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if (!optParam)
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{
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hostBuffer[batchSlot] = defaultValue;
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}
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else if (optParam->size() == 1)
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{
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hostBuffer[batchSlot] = optParam->front();
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}
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else
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{
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TLLM_CHECK_WITH_INFO(optParam->size() == batchSize, "Argument vector size mismatch.");
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hostBuffer[batchSlot] = optParam.value()[bi];
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}
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}
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if (batchSlots)
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{
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cudaAutoCpy(deviceBuffer, hostBuffer.data(), maxBatchSize, stream);
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}
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else
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{
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cudaAutoCpy(deviceBuffer, hostBuffer.data(), batchSize, stream);
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}
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}
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size_t batchSize;
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size_t maxBatchSize;
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cudaStream_t stream;
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};
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} // namespace layers
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} // namespace tensorrt_llm
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