mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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124 lines
4.4 KiB
C++
124 lines
4.4 KiB
C++
/*
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* Copyright (c) 2022-2024, 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 "tensorrt_llm/runtime/bufferManager.h"
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#include "tensorrt_llm/runtime/cudaEvent.h"
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#include "tensorrt_llm/runtime/decodingMode.h"
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#include "tensorrt_llm/runtime/gptDecoder.h"
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#include "tensorrt_llm/runtime/iStatefulGptDecoder.h"
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#include "tensorrt_llm/runtime/iTensor.h"
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#include <memory>
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namespace tensorrt_llm::runtime
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{
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//! GPT decoder class with support for in-flight batching
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class StatefulGptDecoder : public IStatefulGptDecoder
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{
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public:
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StatefulGptDecoder(std::size_t vocabSize, std::size_t vocabSizePadded, CudaStreamPtr stream);
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//! Setup the decoder before calling `forward()`
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void setup(DecodingMode const& mode, SizeType32 maxBatchSize, SizeType32 maxBeamWidth,
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SizeType32 maxAttentionWindow, SizeType32 sinkTokenLength, SizeType32 maxSequenceLength,
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SizeType32 maxTokensPerStep, bool fusedDecoder, nvinfer1::DataType dtype,
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ModelConfig const& modelConfig) override;
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//! @brief Initialize the decoder with new batch of inputs.
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void newBatch(
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GenerationInput const& input, GenerationOutput const& output, SamplingConfig const& samplingConfig) override;
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void forwardAsync(decoder::Output& output, decoder::Input const& input) override;
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void forwardSync() override;
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//! @brief Gather final results for all requests.
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void finalize() const override;
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//! @param step index within tokens generated in one step
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//! @returns [batchSize, maxBeamWidth, maxInputLength + maxNewTokens], contains input token ids and generated token
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//! ids without padding, on gpu
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[[nodiscard]] TensorPtr getOutputIds() const override
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{
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return mDecodingOutput->ids;
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}
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//! @returns [batchSize, maxBeamWidth], cumulative log probabilities (per beam), on gpu
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[[nodiscard]] TensorPtr getCumLogProbs() const override
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{
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return mDecodingOutput->cumLogProbs;
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}
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//! @returns [batchSize, maxBeamWidth], cumulative log probabilities (per beam), on gpu
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[[nodiscard]] TensorPtr getLogProbs() const override
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{
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return mDecodingOutput->logProbs;
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}
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//! @brief Get tokens generated in one step of last forward pass
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//! @param iter The iteration within [0; maxTokensPerStep) for which to get the tokens
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//! @returns [batchSize, beamWidth], tokens generated in `iter` (per beam), on gpu
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[[nodiscard]] TensorPtr getNewTokens(SizeType32 iter = 0) const override
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{
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TLLM_CHECK(iter == 0);
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return mDecodingOutput->newTokens;
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}
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//! @brief Get tokens generated in the last forward pass
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//! @returns [batchSize, maxBeamWidth], tokens generated in last forward pass, on gpu
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[[nodiscard]] TensorPtr getAllNewTokens() const override
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{
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TensorPtr newTokens = ITensor::view(mDecodingOutput->newTokensSteps);
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newTokens->unsqueeze(0);
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return newTokens;
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}
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//! @returns [1], number of finished sequences, in pinned host memory
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[[nodiscard]] TensorPtr getNbFinished() const override
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{
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return mFinishedSum;
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}
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private:
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void reshapeBuffers(SizeType32 batchSize, SizeType32 beamWidth, SizeType32 mMaxAttentionWindow,
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SizeType32 mSinkTokenLength, SizeType32 maxSequenceLength);
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private:
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std::size_t const mVocabSize;
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std::size_t const mVocabSizePadded;
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CudaStreamPtr mStream;
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BufferManager mBufferManager;
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using GptDecoderPtr = std::unique_ptr<IGptDecoder>;
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GptDecoderPtr mDecoder;
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using DecodingInputPtr = std::unique_ptr<DecodingInput>;
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DecodingInputPtr mDecodingInput;
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using DecodingOutputPtr = std::unique_ptr<DecodingOutput>;
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DecodingOutputPtr mDecodingOutput;
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CudaEvent mDecodedEvent{};
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TensorPtr mFinishedSum;
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SizeType32 mNbSteps;
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SizeType32 mMaxSequenceLength{};
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SizeType32 mMaxAttentionWindow{};
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SizeType32 mSinkTokenLength{};
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};
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} // namespace tensorrt_llm::runtime
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