mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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32ed92e449
Co-authored-by: Rong Zhou <130957722+ReginaZh@users.noreply.github.com> Co-authored-by: Onur Galoglu <33498883+ogaloglu@users.noreply.github.com> Co-authored-by: Fabian Joswig <fjosw@users.noreply.github.com>
92 lines
3.1 KiB
C++
92 lines
3.1 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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#include <string>
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#include "tensorrt_llm/common/assert.h"
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#include "tensorrt_llm/common/logger.h"
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#include "tensorrt_llm/executor/executor.h"
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#include "tensorrt_llm/plugins/api/tllmPlugin.h"
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namespace tlc = tensorrt_llm::common;
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namespace tle = tensorrt_llm::executor;
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int main(int argc, char* argv[])
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{
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// Register the TRT-LLM plugins
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initTrtLlmPlugins();
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if (argc != 2)
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{
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TLLM_LOG_ERROR("Usage: %s <dir_with_engine_files>", argv[0]);
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return 1;
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}
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int constexpr sentinels[] = {42, 29};
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int step = 0;
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auto logitsPostProcessorFn
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= [&step, &sentinels](tle::IdType reqId, tle::Tensor& logits, tle::BeamTokens const& tokens,
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tle::StreamPtr const& streamPtr, std::optional<tle::IdType> clientId)
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{
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auto logitsDataType = logits.getDataType();
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auto logitsCpu = tensorrt_llm::executor::Tensor::cpu(logitsDataType, logits.getShape());
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auto* dataPtr = logitsCpu.getData();
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auto* dataPtrFloat = static_cast<float*>(dataPtr);
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for (size_t i = 0; i < logitsCpu.getSize(); ++i)
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{
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dataPtrFloat[i] = -1.0e20;
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}
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dataPtrFloat[sentinels[step]] = 0.0f;
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logits.setFrom(logitsCpu, streamPtr);
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step = (1 - step);
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};
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std::string logitsPostProcessorName = "MyLogitsPP";
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// Create the executor for this engine
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tle::SizeType32 beamWidth = 1;
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auto executorConfig = tle::ExecutorConfig(beamWidth);
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auto logitsProcConfig = tle::LogitsPostProcessorConfig();
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logitsProcConfig.setProcessorMap(std::unordered_map<std::string, tensorrt_llm::executor::LogitsPostProcessor>{
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{logitsPostProcessorName, logitsPostProcessorFn}});
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executorConfig.setLogitsPostProcessorConfig(logitsProcConfig);
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auto trtEnginePath = argv[1];
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auto executor = tle::Executor(trtEnginePath, tle::ModelType::kDECODER_ONLY, executorConfig);
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// Create the request
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tle::SizeType32 maxNewTokens = 5;
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tle::VecTokens inputTokens{1, 2, 3, 4};
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auto request = tle::Request(inputTokens, maxNewTokens);
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request.setLogitsPostProcessorName(logitsPostProcessorName);
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// Enqueue the request
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auto requestId = executor.enqueueRequest(std::move(request));
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// Wait for the response
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auto responses = executor.awaitResponses(requestId);
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// Get outputTokens
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auto outputTokens = responses.at(0).getResult().outputTokenIds.at(beamWidth - 1);
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TLLM_LOG_INFO("Output tokens: %s", tlc::vec2str(outputTokens).c_str());
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return 0;
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}
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