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https://github.com/NVIDIA/TensorRT-LLM.git
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79 lines
3.8 KiB
C++
79 lines
3.8 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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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/batch_manager/llmRequest.h"
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#include <ATen/ATen.h>
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#include <ATen/ops/tensor.h>
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#include <memory>
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#include <optional>
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#include <pybind11/pybind11.h>
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namespace tensorrt_llm::pybind::batch_manager
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{
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namespace tb = tensorrt_llm::batch_manager;
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/* Unfortunately, torch's default pybind bindings don't know about c10::cuda::CUDAStream,
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* so we have to pass the more generic c10::Stream, and convert it back to a full-fledged
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* torch.cuda.Stream in python. See example in test/bindings/test_gpt_manager.py
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*/
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class LlmRequest : public tb::GenericLlmRequest<at::Tensor, c10::Stream>
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{
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public:
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using Base = GenericLlmRequest<at::Tensor, c10::Stream>;
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using TensorPtr = Base::TensorPtr;
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using SizeType = Base::SizeType;
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using TokenIdType = Base::TokenIdType;
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using RequestIdType = Base::RequestIdType;
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using LoraTaskIdType = Base::LoraTaskIdType;
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using VecLogProbs = Base::VecLogProbs;
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using BeamTokens = Base::BeamTokens;
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using VecTokens = Base::VecTokens;
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using LogitsPostProcessor = Base::LogitsPostProcessor;
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LlmRequest(RequestIdType requestId, SizeType maxNewTokens, std::vector<TokenIdType> inputTokens,
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runtime::SamplingConfig samplingConfig, bool isStreaming, std::optional<SizeType> endId = std::nullopt,
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std::optional<SizeType> padId = std::nullopt, std::optional<TensorPtr> embeddingBias = std::nullopt,
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std::optional<TensorPtr> badWordsList = std::nullopt, std::optional<TensorPtr> stopWordsList = std::nullopt,
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std::optional<TensorPtr> promptEmbeddingTable = std::nullopt,
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std::optional<SizeType> promptVocabSize = std::nullopt, std::optional<LoraTaskIdType> loraTaskId = std::nullopt,
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std::optional<TensorPtr> loraWeights = std::nullopt, std::optional<TensorPtr> loraConfig = std::nullopt,
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bool returnLogProbs = false, bool returnContextLogits = false, bool returnGenerationLogits = false,
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std::optional<VecTokens> draftTokens = std::nullopt, std::optional<TensorPtr> draftLogits = std::nullopt,
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bool excludeInputFromOutput = false, std::optional<LogitsPostProcessor> logitsPostProcessor = std::nullopt)
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: Base(requestId, maxNewTokens, std::make_shared<std::vector<TokenIdType>>(std::move(inputTokens)),
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samplingConfig, isStreaming, endId, padId, embeddingBias, badWordsList, stopWordsList, promptEmbeddingTable,
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promptVocabSize, loraTaskId, loraWeights, loraConfig, returnLogProbs, returnContextLogits,
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returnGenerationLogits,
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draftTokens.has_value() ? std::make_shared<VecTokens>(std::move(draftTokens.value()))
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: std::make_shared<VecTokens>(),
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draftLogits, excludeInputFromOutput, logitsPostProcessor)
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{
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}
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static std::optional<tb::LlmRequest::LogitsPostProcessor> callbackAdapter(
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std::optional<LlmRequest::LogitsPostProcessor> callback);
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[[nodiscard]] std::shared_ptr<tensorrt_llm::batch_manager::LlmRequest> toTrtLlm() const;
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static void initBindings(pybind11::module_& m);
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};
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} // namespace tensorrt_llm::pybind::batch_manager
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