mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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113 lines
3.5 KiB
C++
113 lines
3.5 KiB
C++
/*
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* Copyright (c) 2022-2023, 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/cudaStream.h"
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#include "tensorrt_llm/runtime/generationInput.h"
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#include "tensorrt_llm/runtime/iTensor.h"
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#include "tensorrt_llm/runtime/samplingConfig.h"
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#include <cstdint>
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#include <memory>
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#include <utility>
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#include <vector>
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#include <NvInferRuntime.h>
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namespace tensorrt_llm::runtime
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{
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namespace decoder
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{
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class Input
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{
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public:
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using TensorPtr = std::shared_ptr<ITensor const>;
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explicit Input(TensorPtr logits)
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: logits{std::move(logits)}
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{
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TLLM_CHECK_WITH_INFO(static_cast<bool>(this->logits), "Invalid logits tensor");
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}
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// mandatory parameters
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TensorPtr logits; // [batchSize, maxBeamWidth, vocabSizePadded], on gpu
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// parameters for beam search
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TensorPtr cacheIndirection; // [batchSize, maxBeamWidth, maxSeqLen] - the k/v cache index for beam search, on gpu
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};
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class Output
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{
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public:
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using TensorPtr = std::shared_ptr<ITensor>;
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Output() = default;
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// parameters for beam search
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TensorPtr cacheIndirection; // [batchSize, maxBeamWidth, maxSeqLen], mandatory in beam search, on gpu
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TensorPtr sequenceLengths; // [batchSize, maxBeamWidth], mandatory, on gpu
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};
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} // namespace decoder
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//! GPT decoder class with support for in-flight batching
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class IStatefulGptDecoder
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{
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public:
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using CudaStreamPtr = std::shared_ptr<CudaStream>;
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using TensorPtr = std::shared_ptr<ITensor>;
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//! Setup the decoder before calling `forward()`, also calls reshapeBuffers
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virtual void setup(
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SizeType maxBatchSize, SizeType maxBeamWidth, SizeType maxSequenceLength, nvinfer1::DataType dtype)
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= 0;
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//! @brief Initialize the decoder with new batch of inputs.
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virtual void newBatch(GenerationInput const& inputs, SamplingConfig const& samplingConfig) = 0;
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//! @brief Run one step for all requests without blocking the host thread.
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virtual void forwardAsync(decoder::Output& output, decoder::Input const& input) = 0;
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//! @brief Wait for the last call to `forwardAsync` to complete and return whether all sequences have finished.
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virtual bool isFinishedSync() = 0;
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//! @brief Run one step for all requests.
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virtual bool forward(decoder::Output& output, decoder::Input const& input)
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{
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forwardAsync(output, input);
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return isFinishedSync();
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}
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//! @brief Gather final results for all requests.
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virtual TensorPtr getFinalOutputIds() const = 0;
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//! @returns [batchSize, beamWidth, maxSequenceLength], all token ids, on gpu
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virtual TensorPtr getOutputIds() const = 0;
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//! @returns [batchSize, beamWidth], latests generated tokens (per beam), on gpu
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virtual TensorPtr getNewTokens() const = 0;
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//! @returns [1], number of finished sequences, in pinned host memory
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virtual TensorPtr getNbFinished() const = 0;
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protected:
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IStatefulGptDecoder() = default;
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};
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} // namespace tensorrt_llm::runtime
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