mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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5955b8afba
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
886 lines
32 KiB
C++
886 lines
32 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 "tensorrt_llm/batch_manager/GptManager.h"
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#include "tensorrt_llm/batch_manager/inferenceRequest.h"
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#include "tensorrt_llm/batch_manager/namedTensor.h"
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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/common/mpiUtils.h"
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#include "tensorrt_llm/common/stringUtils.h"
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#include "tensorrt_llm/plugins/api/tllmPlugin.h"
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#include "tensorrt_llm/runtime/tllmLogger.h"
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#include "tensorrt_llm/runtime/worldConfig.h"
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#include <chrono>
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#include <cstdint>
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#include <cxxopts.hpp>
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#include <iostream>
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#include <nlohmann/json.hpp>
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#include <string>
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#include <thread>
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#include <utility>
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using namespace tensorrt_llm::batch_manager;
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using namespace tensorrt_llm::runtime;
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namespace tc = tensorrt_llm::common;
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namespace mpi = tensorrt_llm::mpi;
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namespace trt = nvinfer1;
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// Class holding all infos regarding a single work item.
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// This includes the original request, associated response factor
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// and state.
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class WorkItem
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{
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public:
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WorkItem(std::shared_ptr<InferenceRequest> ir, uint64_t requestId)
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: mInferenceRequest(ir)
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, mRequestId(requestId)
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{
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}
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~WorkItem() {}
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uint64_t requestId() const
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{
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return mRequestId;
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}
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std::shared_ptr<InferenceRequest> getInferenceRequest() const
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{
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return mInferenceRequest;
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}
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private:
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std::shared_ptr<InferenceRequest> mInferenceRequest;
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uint64_t mRequestId;
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};
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/// @brief Thread-safe queue of work items
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class WorkItemsQueue
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{
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public:
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void clear()
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{
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std::lock_guard<std::mutex> lk(mMutex);
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mPendingWorkItems.clear();
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mPendingWorkItemsReqIds.clear();
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mInProgressWorkItems.clear();
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}
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// Note: this function only be called under a lock
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bool hasInProgressReqId(const uint64_t reqId) const
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{
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return (mInProgressWorkItems.find(reqId) != mInProgressWorkItems.end());
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}
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// Note: this function only be called under a lock
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bool hasPendingReqId(const uint64_t reqId) const
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{
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return (mPendingWorkItemsReqIds.find(reqId) != mPendingWorkItemsReqIds.end());
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}
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bool empty() const
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{
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return mPendingWorkItems.empty() && mInProgressWorkItems.empty() && mPendingWorkItemsReqIds.empty();
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}
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/// @brief Add a new work item to the queue
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/// Throws an error if requestId already exists
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void push(std::shared_ptr<InferenceRequest> request, uint64_t requestId)
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{
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std::lock_guard<std::mutex> lock(mMutex);
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TLLM_CHECK_WITH_INFO(!hasInProgressReqId(requestId) && !hasPendingReqId(requestId),
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"requestId %lu is already in progress, request is ignored.", requestId);
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auto workItem = std::make_shared<WorkItem>(request, requestId);
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mPendingWorkItems.push_back(workItem);
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mPendingWorkItemsReqIds.insert(workItem->requestId());
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}
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/// @brief Get a new work item from the queue, and move it to the list of
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/// in progress work items if it hasn't been stopped
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/// @return A tuple of the workItem and a boolean flag indicating if the work item
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/// has been marked in progress
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std::tuple<std::shared_ptr<WorkItem>, bool> pop()
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{
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std::lock_guard<std::mutex> lock(mMutex);
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auto workItem = mPendingWorkItems.front();
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mPendingWorkItems.pop_front();
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mPendingWorkItemsReqIds.erase(workItem->requestId());
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bool markedInProgress = false;
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mInProgressWorkItems.emplace(workItem->requestId(), workItem);
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markedInProgress = true;
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return {workItem, markedInProgress};
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}
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size_t numPendingWorkItems() const
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{
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std::lock_guard<std::mutex> lock(mMutex);
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return mPendingWorkItems.size();
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}
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size_t numInProgressWorkItems() const
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{
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std::lock_guard<std::mutex> lock(mMutex);
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return mInProgressWorkItems.size();
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}
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size_t size() const
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{
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std::lock_guard<std::mutex> lock(mMutex);
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return mPendingWorkItems.size() + mInProgressWorkItems.size();
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}
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/// @brief Mark a request as being finished
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/// @param requestId
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void markFinished(const uint64_t requestId)
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{
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std::lock_guard<std::mutex> lock(mMutex);
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if (hasInProgressReqId(requestId))
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{
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mInProgressWorkItems.erase(requestId);
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}
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}
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private:
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/// Queue of work items
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std::list<std::shared_ptr<WorkItem>> mPendingWorkItems;
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/// requestIds of work items in the queue
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std::set<uint64_t> mPendingWorkItemsReqIds;
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/// work items currently in progress
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std::unordered_map<uint64_t, std::shared_ptr<WorkItem>> mInProgressWorkItems;
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mutable std::mutex mMutex;
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};
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struct BenchInfo
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{
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BenchInfo() = default;
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BenchInfo(int _inputLength, int _outputLength, std::chrono::time_point<std::chrono::steady_clock> _start)
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: inputLength(_inputLength)
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, outputLength(_outputLength)
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, start(_start)
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, latency()
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{
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}
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int inputLength;
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int outputLength;
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std::chrono::time_point<std::chrono::steady_clock> start;
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std::chrono::time_point<std::chrono::steady_clock> end;
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float latency; // millisecond
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};
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class Recorder
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{
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public:
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explicit Recorder(std::string opCsvFile)
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: mOpCsvFile(std::move(opCsvFile))
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{
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}
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void initialize()
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{
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mStart = std::chrono::steady_clock::now();
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}
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void finalize()
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{
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mEnd = std::chrono::steady_clock::now();
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}
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void recordStart(std::shared_ptr<InferenceRequest> request, uint64_t requestId)
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{
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auto const inputLength = request->getInputIds()->getSize();
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auto const maxNewTokens = request->getMaxNewTokensNamed();
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auto const& outputLengthTensor = maxNewTokens.tensor;
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TLLM_CHECK_WITH_INFO(outputLengthTensor != nullptr && outputLengthTensor->getSize() > 0,
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"Undefined scalar vector for %s", maxNewTokens.name.c_str());
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auto const outputLength = *bufferCast<SizeType>(*outputLengthTensor);
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auto const start = std::chrono::steady_clock::now();
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mRequestBenchInfos[requestId] = BenchInfo(inputLength, outputLength, start);
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}
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void recordEnd(uint64_t requestId)
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{
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mRequestBenchInfos[requestId].end = std::chrono::steady_clock::now();
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mRequestBenchInfos[requestId].latency = std::chrono::duration<float, std::milli>(
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mRequestBenchInfos[requestId].end - mRequestBenchInfos[requestId].start)
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.count();
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}
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void calculateMetrics()
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{
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mNumSamples = mRequestBenchInfos.size();
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mTotalLatency = std::chrono::duration<float, std::milli>(mEnd - mStart).count();
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mSeqThroughput = mNumSamples / (mTotalLatency / 1000);
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mAvgSeqLatency = 0;
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int totalOutputTokens = 0;
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for (auto reqInfo : mRequestBenchInfos)
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{
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mAvgSeqLatency += reqInfo.second.latency;
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totalOutputTokens += reqInfo.second.outputLength;
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}
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mAvgSeqLatency /= mNumSamples;
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mTokenThroughput = totalOutputTokens / (mTotalLatency / 1000);
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}
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void report()
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{
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printf("[BENCHMARK] num_samples %d\n", mNumSamples);
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printf("[BENCHMARK] total_latency(ms) %.2f\n", mTotalLatency);
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printf("[BENCHMARK] seq_throughput(seq/sec) %.2f\n", mSeqThroughput);
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printf("[BENCHMARK] avg_sequence_latency(ms) %.2f\n", mAvgSeqLatency);
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printf("[BENCHMARK] token_throughput(token/sec) %.2f\n", mTokenThroughput);
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}
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void writeOpMetricsToCsv()
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{
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if (!mOpCsvFile.empty())
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{
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std::vector<std::string> headers = {"num_samples", "total_latency(ms)", "seq_throughput(seq/sec)",
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"avg_sequence_latency(ms)", "token_throughput(token/sec)"};
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std::ofstream outputFile(mOpCsvFile);
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if (outputFile.is_open())
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{
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for (const auto& header : headers)
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{
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outputFile << header << ",";
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}
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outputFile << "\n";
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outputFile << mNumSamples << "," << mTotalLatency << "," << mSeqThroughput << "," << mAvgSeqLatency
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<< "," << mTokenThroughput;
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outputFile << "\n";
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}
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else
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{
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std::cerr << "Error opening file '" << mOpCsvFile << "' for writing.\n";
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}
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}
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}
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private:
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std::unordered_map<uint64_t, BenchInfo> mRequestBenchInfos;
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std::chrono::time_point<std::chrono::steady_clock> mStart;
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std::chrono::time_point<std::chrono::steady_clock> mEnd;
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int mNumSamples{};
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float mTotalLatency{};
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float mSeqThroughput{};
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float mAvgSeqLatency{};
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float mTokenThroughput{};
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std::string mOpCsvFile;
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}; // class Recorder
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class GptServer
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{
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public:
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GptServer(std::filesystem::path const& trtEnginePath, TrtGptModelType modelType, int32_t maxBeamWidth,
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batch_scheduler::SchedulerPolicy schedulerPolicy, TrtGptModelOptionalParams const& optionalParams,
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std::shared_ptr<Recorder> recorder, std::optional<uint64_t> terminateReqId, std::chrono::milliseconds waitSleep,
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std::optional<uint64_t> const staticEmulatedBatchSize, int const staticEmulatedTimeoutMs)
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: mRecorder(std::move(recorder))
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, mTerminateReqId(terminateReqId)
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, mWaitSleep(waitSleep)
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, mStaticEmulatedBatchSize(staticEmulatedBatchSize)
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, mEmulatedBatchEndTimestamp(
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std::chrono::steady_clock::now() + std::chrono::milliseconds(staticEmulatedTimeoutMs))
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, mStaticEmulatedTimeoutMs(staticEmulatedTimeoutMs)
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, mActiveCount(0)
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{
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ReturnBatchManagerStatsCallback iterationDataCallback = [this, &optionalParams](std::string const& log)
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{
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if (optionalParams.logIterationData)
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{
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TLLM_LOG_INFO(log);
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}
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if (mStaticEmulatedBatchSize)
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{
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auto const json = nlohmann::json::parse(log);
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auto const activeRequests = json["Active Request Count"];
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TLLM_CHECK(activeRequests <= mStaticEmulatedBatchSize.value());
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}
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};
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mBatchManager = std::make_shared<GptManager>(
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trtEnginePath, modelType, maxBeamWidth, schedulerPolicy,
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[this](int max_num_requests) { return getInferenceRequests(max_num_requests); },
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[this](uint64_t requestId, std::list<NamedTensor> const& response_tensors, bool final_response,
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std::string const& errMsg)
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{ return sendResponse(requestId, response_tensors, final_response, errMsg); },
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nullptr, iterationDataCallback, optionalParams, terminateReqId);
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}
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~GptServer()
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{
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mWorkItemsQueue.clear();
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}
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void enqueue(std::shared_ptr<InferenceRequest> const& request)
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{
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TLLM_CHECK(request != nullptr);
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auto const requestId = request->getRequestId();
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if (requestId == mTerminateReqId)
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{
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mWorkItemsQueue.push(request, requestId);
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return;
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}
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// Enqueue
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try
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{
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mRecorder->recordStart(request, requestId);
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mWorkItemsQueue.push(request, requestId);
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}
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catch (const tc::TllmException& e)
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{
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throw;
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}
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catch (const std::exception& e)
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{
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TLLM_THROW("%s", e.what());
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}
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}
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void waitForEmpty() const
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{
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while (!mWorkItemsQueue.empty())
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{
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std::this_thread::sleep_for(mWaitSleep);
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}
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}
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void waitBatchManager() const
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{
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mBatchManager->waitUntilTerminate();
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}
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void shutdown() const
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{
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mBatchManager->shutdown();
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}
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// Return up to max_num_requests inference requests.
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std::list<std::shared_ptr<InferenceRequest>> getInferenceRequests(const int max_num_requests)
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{
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std::list<std::shared_ptr<InferenceRequest>> rval;
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auto& comm = COMM_SESSION;
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if (max_num_requests > 0)
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{
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auto world_size = comm.getSize();
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auto rank = comm.getRank();
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if (rank == 0)
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{
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auto const numNewWorkItems = std::min(static_cast<int64_t>(mWorkItemsQueue.numPendingWorkItems()),
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static_cast<int64_t>(max_num_requests));
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bool readyForNextBatch = numNewWorkItems > 0;
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if (mStaticEmulatedBatchSize)
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{
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if (numNewWorkItems > 0)
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{
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bool const timeout = std::chrono::steady_clock::now() > mEmulatedBatchEndTimestamp;
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bool const previousBatchFinished = mActiveCount == 0;
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bool const haveEnoughForNextBatch = numNewWorkItems >= mStaticEmulatedBatchSize.value();
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readyForNextBatch = previousBatchFinished && (timeout || haveEnoughForNextBatch);
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}
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if (numNewWorkItems == 0 || readyForNextBatch)
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{
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// Timeout should only begin once we have at least 1 pending request.
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// Reset timeout when no requests are pending or we submit a new batch.
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mEmulatedBatchEndTimestamp
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= std::chrono::steady_clock::now() + std::chrono::milliseconds(mStaticEmulatedTimeoutMs);
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}
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}
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if (readyForNextBatch)
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{
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int count = 0;
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// Only add a single batch at a time when emulating static batching
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auto const numItemsToAdd = std::min(
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numNewWorkItems, static_cast<int64_t>(mStaticEmulatedBatchSize.value_or(numNewWorkItems)));
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mActiveCount += numItemsToAdd;
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while (count < numItemsToAdd)
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{
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auto [workItem, markedInProgress] = mWorkItemsQueue.pop();
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if (markedInProgress)
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{
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rval.emplace_back(workItem->getInferenceRequest());
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count++;
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}
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else
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{
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auto warnStr = tc::fmtstr(
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"request Id %lu has been stopped. Request is ignored.", workItem->requestId());
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TLLM_LOG_WARNING(warnStr);
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sendResponse(workItem->requestId(), {}, true, warnStr);
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}
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}
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}
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if (world_size > 1)
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{
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auto numNewWorkItems = static_cast<int64_t>(rval.size());
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comm.bcast(&numNewWorkItems, 1, mpi::MpiType::kINT64, 0);
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if (numNewWorkItems > 0)
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{
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std::vector<int64_t> packed;
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for (auto const& ir : rval)
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{
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auto vpacked = ir->serialize();
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packed.push_back(static_cast<int64_t>(vpacked.size()));
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packed.insert(
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packed.end(), std::move_iterator(vpacked.begin()), std::move_iterator(vpacked.end()));
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}
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comm.bcast(packed, 0);
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}
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}
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}
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else
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{
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// subordinate ranks hang until master rank sends work
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int64_t numNewWorkItems = 0;
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comm.bcast(&numNewWorkItems, 1, mpi::MpiType::kINT64, 0);
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if (numNewWorkItems > 0)
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{
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std::vector<int64_t> packed;
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comm.bcast(packed, 0);
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int64_t* packed_ptr = packed.data();
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for (int64_t count = 0; count < numNewWorkItems; ++count)
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{
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int64_t n = *(packed_ptr++);
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auto ir = InferenceRequest::deserialize(packed_ptr);
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packed_ptr += n;
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rval.emplace_back(ir);
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}
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}
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}
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}
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return rval;
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}
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void sendResponse(uint64_t requestId, [[maybe_unused]] std::list<NamedTensor> const& response_tensors,
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bool final_response, [[maybe_unused]] const std::string& errMsg)
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{
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// `response_tensors` contains `outputIds, sequenceLength, [contextLogits, generationLogits], logProbs,
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// cumLogProbs`. `contextLogits, generationLogits` are optional, only contained when `gather_context_logits` and
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// `gather_generation_logits` are enabled respectively. Or enable 'gather_all_token_logits' to enable both of
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// them.
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try
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{
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if (final_response)
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{
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mWorkItemsQueue.markFinished(requestId);
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mRecorder->recordEnd(requestId);
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mActiveCount--;
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}
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}
|
|
catch (const std::exception& e)
|
|
{
|
|
TLLM_LOG_ERROR("Failed to send response for requestId %lu\n%s", requestId, e.what());
|
|
}
|
|
}
|
|
|
|
private:
|
|
std::shared_ptr<GptManager> mBatchManager;
|
|
std::shared_ptr<Recorder> mRecorder;
|
|
WorkItemsQueue mWorkItemsQueue;
|
|
std::optional<uint64_t> mTerminateReqId;
|
|
std::chrono::milliseconds mWaitSleep;
|
|
std::optional<int> mStaticEmulatedBatchSize;
|
|
std::chrono::time_point<std::chrono::steady_clock> mEmulatedBatchEndTimestamp;
|
|
int32_t mStaticEmulatedTimeoutMs;
|
|
std::atomic<uint64_t> mActiveCount;
|
|
|
|
}; // class GptServer
|
|
|
|
namespace
|
|
{
|
|
|
|
struct Sample
|
|
{
|
|
std::vector<int32_t> inputIds;
|
|
int32_t outputLen;
|
|
float delay;
|
|
};
|
|
|
|
using Samples = std::vector<Sample>;
|
|
|
|
Samples parseWorkloadJson(std::filesystem::path const& datasetPath, int maxNumSamples)
|
|
{
|
|
auto constexpr allowExceptions = true;
|
|
auto constexpr ignoreComments = true;
|
|
TLLM_CHECK_WITH_INFO(std::filesystem::exists(datasetPath), "File does not exist: %s", datasetPath.c_str());
|
|
std::ifstream jsonStream(datasetPath);
|
|
auto json = nlohmann::json::parse(jsonStream, nullptr, allowExceptions, ignoreComments);
|
|
|
|
Samples samples;
|
|
|
|
for (auto const& sample : json["samples"])
|
|
{
|
|
if (samples.size() >= maxNumSamples)
|
|
break;
|
|
samples.emplace_back(Sample{sample["input_ids"], sample["output_len"], sample["delay"]});
|
|
}
|
|
return samples;
|
|
}
|
|
|
|
std::shared_ptr<InferenceRequest> makeRequest(std::uint64_t reqId, Sample const& sample,
|
|
ITensor::SharedPtr const& beamWidthTensor, ITensor::SharedPtr const& eosId, ITensor::SharedPtr const& padId,
|
|
BufferManager const& bufferManager, ITensor::SharedPtr const& returnContextLogits = nullptr,
|
|
ITensor::SharedPtr const& returnGenerationLogits = nullptr)
|
|
{
|
|
auto request = std::make_shared<InferenceRequest>(reqId);
|
|
auto const& inputIds = sample.inputIds;
|
|
request->setInputIds(bufferManager.copyFrom(
|
|
inputIds, ITensor::makeShape({static_cast<SizeType>(inputIds.size())}), MemoryType::kPINNED));
|
|
auto const requestOutputLen = sample.outputLen;
|
|
request->setMaxNewTokens(
|
|
bufferManager.copyFrom(&requestOutputLen, ITensor::makeShape({1, 1}), MemoryType::kPINNED));
|
|
request->setBeamWidth(beamWidthTensor);
|
|
if (eosId != nullptr)
|
|
{
|
|
request->setEndId(eosId);
|
|
}
|
|
if (padId != nullptr)
|
|
{
|
|
request->setPadId(padId);
|
|
}
|
|
if (returnContextLogits)
|
|
{
|
|
request->setReturnContextLogits(returnContextLogits);
|
|
}
|
|
if (returnGenerationLogits)
|
|
{
|
|
request->setReturnGenerationLogits(returnGenerationLogits);
|
|
}
|
|
return request;
|
|
}
|
|
|
|
void benchmarkGptManager(std::filesystem::path const& engineDir, TrtGptModelType modelType,
|
|
std::string const& datasetPath, std::string const& opCsvFile, int maxNumSamples, int beamWidth, int warmUp,
|
|
const std::optional<int32_t>& eosId, const std::optional<int32_t>& padId,
|
|
TrtGptModelOptionalParams const& optionalParams, batch_scheduler::SchedulerPolicy schedulerPolicy,
|
|
std::chrono::milliseconds waitSleep, bool returnContextLogits, bool returnGenerationLogits,
|
|
std::optional<int> const staticEmulatedBatchSize, int const staticEmulatedTimeoutMs)
|
|
{
|
|
auto const worldConfig = WorldConfig::mpi();
|
|
|
|
BufferManager bufferManager{std::make_shared<CudaStream>()}; // the stream is not used
|
|
|
|
ITensor::SharedPtr beamWidthTensor{
|
|
bufferManager.copyFrom(&beamWidth, ITensor::makeShape({1}), MemoryType::kPINNED)};
|
|
|
|
// Load dataset
|
|
const auto samples = parseWorkloadJson(datasetPath, maxNumSamples);
|
|
const auto numSamples = samples.size();
|
|
|
|
const int maxBeamWidth = beamWidth;
|
|
auto recorder = std::make_shared<Recorder>(opCsvFile);
|
|
uint64_t terminateReqId = numSamples + 1;
|
|
auto gptServer = std::make_shared<GptServer>(engineDir, modelType, maxBeamWidth, schedulerPolicy, optionalParams,
|
|
recorder, terminateReqId, waitSleep, staticEmulatedBatchSize, staticEmulatedTimeoutMs);
|
|
|
|
ITensor::SharedPtr eosIdTensor{
|
|
eosId ? bufferManager.copyFrom(&eosId.value(), ITensor::makeShape({1}), MemoryType::kPINNED) : nullptr};
|
|
ITensor::SharedPtr padIdTensor{
|
|
padId ? bufferManager.copyFrom(&padId.value(), ITensor::makeShape({1}), MemoryType::kPINNED) : nullptr};
|
|
|
|
ITensor::SharedPtr returnContextLogitsFlagTensor{returnContextLogits
|
|
? bufferManager.copyFrom(&returnContextLogits, ITensor::makeShape({1}), MemoryType::kPINNED)
|
|
: nullptr};
|
|
|
|
ITensor::SharedPtr returnGenerationLogitsFlagTensor{returnGenerationLogits
|
|
? bufferManager.copyFrom(&returnGenerationLogits, ITensor::makeShape({1}), MemoryType::kPINNED)
|
|
: nullptr};
|
|
|
|
if (worldConfig.getRank() == 0)
|
|
{
|
|
// Warm up
|
|
SizeType reqId = 0;
|
|
for (auto i = 0; i < warmUp; ++i)
|
|
{
|
|
++reqId;
|
|
if (i == terminateReqId)
|
|
++reqId;
|
|
auto request = makeRequest(reqId, samples[0], beamWidthTensor, eosIdTensor, padIdTensor, bufferManager);
|
|
gptServer->enqueue(request);
|
|
}
|
|
gptServer->waitForEmpty();
|
|
|
|
// Benchmark
|
|
recorder->initialize();
|
|
for (std::size_t i = 0; i < numSamples; ++i)
|
|
{
|
|
auto request = makeRequest(i + 1, samples[i], beamWidthTensor, eosIdTensor, padIdTensor, bufferManager,
|
|
returnContextLogitsFlagTensor, returnGenerationLogitsFlagTensor);
|
|
gptServer->enqueue(request);
|
|
auto delayInMs = static_cast<int>(samples[i].delay * 1000);
|
|
|
|
if (delayInMs != 0)
|
|
{
|
|
std::chrono::milliseconds delay(delayInMs);
|
|
std::this_thread::sleep_for(delay);
|
|
}
|
|
}
|
|
gptServer->waitForEmpty();
|
|
recorder->finalize();
|
|
recorder->calculateMetrics();
|
|
recorder->report();
|
|
recorder->writeOpMetricsToCsv();
|
|
// Send terminateReqId to terminate servers on all ranks
|
|
// Sever on rank 0 will broadcast the terminate signal to other servers on multi-GPU cases
|
|
gptServer->enqueue(std::make_shared<InferenceRequest>(terminateReqId));
|
|
}
|
|
// Wait until benchmarking is done and batch manager is terminated
|
|
gptServer->waitBatchManager();
|
|
}
|
|
|
|
} // namespace
|
|
|
|
int main(int argc, char* argv[])
|
|
{
|
|
cxxopts::Options options(
|
|
"TensorRT-LLM BatchManager Benchmark", "TensorRT-LLM BatchManager Benchmark for GPT and GPT-like models.");
|
|
options.add_options()("h,help", "Print usage");
|
|
// TODO(rkobus): remove because unused
|
|
options.add_options()(
|
|
"m,model", "Model name specified for engines.", cxxopts::value<std::string>()->default_value("gpt_350m"));
|
|
options.add_options()("engine_dir", "Directory that store the engines.", cxxopts::value<std::string>());
|
|
options.add_options()(
|
|
"type", "Batching type: IFB or V1(non-IFB) batching.", cxxopts::value<std::string>()->default_value("IFB"));
|
|
options.add_options()("dataset", "Dataset that is used for benchmarking BatchManager.",
|
|
cxxopts::value<std::string>()->default_value(""));
|
|
options.add_options()(
|
|
"output_csv", "Write output metrics to CSV", cxxopts::value<std::string>()->default_value(""));
|
|
options.add_options()("max_num_samples", "maximum number of samples to use from dataset/generate",
|
|
cxxopts::value<int>()->default_value("100000"));
|
|
options.add_options()(
|
|
"beam_width", "Specify beam width you want to benchmark.", cxxopts::value<int>()->default_value("1"));
|
|
options.add_options()(
|
|
"warm_up", "Specify warm up iterations before benchmark starts.", cxxopts::value<int>()->default_value("2"));
|
|
options.add_options()(
|
|
"eos_id", "Specify the end-of-sequence token id.", cxxopts::value<int>()->default_value("-1"));
|
|
options.add_options()("pad_id", "Specify the padding token id.", cxxopts::value<int>());
|
|
options.add_options()("max_tokens_in_paged_kvcache", "Max tokens in paged K-V Cache.", cxxopts::value<int>());
|
|
options.add_options()(
|
|
"kv_cache_free_gpu_mem_fraction", "K-V Cache Free Gpu Mem Fraction.", cxxopts::value<float>());
|
|
options.add_options()(
|
|
"enable_trt_overlap", "Overlap TRT context preparation and execution", cxxopts::value<bool>());
|
|
options.add_options()("enable_kv_cache_reuse", "Enables the KV cache reuse.", cxxopts::value<bool>());
|
|
options.add_options()("enable_chunked_context", "Whether to enable context chunking.", cxxopts::value<bool>());
|
|
options.add_options()(
|
|
"return_context_logits", "Whether to return context logits.", cxxopts::value<bool>()->default_value("0"));
|
|
options.add_options()(
|
|
"return_generation_logits", "Whether to return generation logits.", cxxopts::value<bool>()->default_value("0"));
|
|
|
|
options.add_options()("scheduler_policy", "Choose scheduler policy between max_utilization/guaranteed_no_evict.",
|
|
cxxopts::value<std::string>()->default_value("guaranteed_no_evict"));
|
|
|
|
options.add_options()("static_emulated_batch_size",
|
|
"Emulate static batching performance with the provided batch size.", cxxopts::value<int>());
|
|
options.add_options()("static_emulated_timeout",
|
|
"Timeout (ms) before launching a partial batch in emulated static batching mode",
|
|
cxxopts::value<int>()->default_value("500"));
|
|
options.add_options()("log_level", "Choose log level between verbose/info/warning/error/internal_error.",
|
|
cxxopts::value<std::string>()->default_value("error"));
|
|
options.add_options()(
|
|
"log_iteration_data", "On each decoder iteration, print batch state metadata.", cxxopts::value<bool>());
|
|
options.add_options()("wait_sleep", "Specify how many milliseconds to sleep each iteration of waitForEmpty loop.",
|
|
cxxopts::value<int>()->default_value("25"));
|
|
|
|
auto result = options.parse(argc, argv);
|
|
|
|
if (result.count("help"))
|
|
{
|
|
std::cout << options.help() << std::endl;
|
|
return 0;
|
|
}
|
|
|
|
// Argument: Engine directory
|
|
if (!result.count("engine_dir"))
|
|
{
|
|
std::cout << options.help() << std::endl;
|
|
TLLM_LOG_ERROR("Please specify engine directory.");
|
|
return 1;
|
|
}
|
|
|
|
// Argument: Batching Type
|
|
auto const type = result["type"].as<std::string>();
|
|
TrtGptModelType modelType{TrtGptModelType::V1};
|
|
if (type == "V1")
|
|
{
|
|
modelType = TrtGptModelType::V1;
|
|
}
|
|
else if (type == "IFB")
|
|
{
|
|
modelType = TrtGptModelType::InflightFusedBatching;
|
|
}
|
|
else
|
|
{
|
|
TLLM_LOG_ERROR("Unexpected batching type: %s", type.c_str());
|
|
return 1;
|
|
}
|
|
|
|
// Argument: Dataset
|
|
auto const datasetPath = result["dataset"].as<std::string>();
|
|
auto const maxNumSamples = result["max_num_samples"].as<int>();
|
|
|
|
// Argument: Output metrics CSV
|
|
auto const opCsvFile = result["output_csv"].as<std::string>();
|
|
|
|
// Argument: beam width
|
|
auto const beamWidth = result["beam_width"].as<int>();
|
|
|
|
// Argument: wait_sleep
|
|
auto const waitSleep = std::chrono::milliseconds(result["wait_sleep"].as<int>());
|
|
|
|
TrtGptModelOptionalParams optionalParams;
|
|
// Argument: Max tokens in paged K-V Cache
|
|
if (result.count("max_tokens_in_paged_kvcache"))
|
|
{
|
|
optionalParams.kvCacheConfig.maxTokens = result["max_tokens_in_paged_kvcache"].as<int>();
|
|
}
|
|
// Argument: K-V Cache Free Gpu Mem Fraction
|
|
if (result.count("kv_cache_free_gpu_mem_fraction"))
|
|
{
|
|
optionalParams.kvCacheConfig.freeGpuMemoryFraction = result["kv_cache_free_gpu_mem_fraction"].as<float>();
|
|
}
|
|
// Argument: Enable TRT overlap
|
|
if (result.count("enable_trt_overlap"))
|
|
{
|
|
optionalParams.enableTrtOverlap = result["enable_trt_overlap"].as<bool>();
|
|
}
|
|
// Argument: Enable KV cache reuse
|
|
if (result.count("enable_kv_cache_reuse"))
|
|
{
|
|
optionalParams.kvCacheConfig.enableBlockReuse = result["enable_kv_cache_reuse"].as<bool>();
|
|
}
|
|
// Argument: Enable batch stats output
|
|
if (result.count("log_iteration_data"))
|
|
{
|
|
optionalParams.logIterationData = result["log_iteration_data"].as<bool>();
|
|
}
|
|
// Argument: Enable chunked context
|
|
if (result.count("enable_chunked_context"))
|
|
{
|
|
optionalParams.enableChunkedContext = result["enable_chunked_context"].as<bool>();
|
|
}
|
|
// Argument: Enable return context logits
|
|
bool returnContextLogits = false;
|
|
if (result.count("return_context_logits"))
|
|
{
|
|
returnContextLogits = result["return_context_logits"].as<bool>();
|
|
}
|
|
// Argument: Enable return context logits
|
|
bool returnGenerationLogits = false;
|
|
if (result.count("return_generation_logits"))
|
|
{
|
|
returnGenerationLogits = result["return_generation_logits"].as<bool>();
|
|
}
|
|
|
|
std::optional<int32_t> padId;
|
|
// Argument: Padding token id
|
|
if (result.count("pad_id"))
|
|
{
|
|
padId = result["pad_id"].as<int>();
|
|
}
|
|
|
|
// Argument: End-of-sentence token id
|
|
std::optional<int32_t> eosId = result["eos_id"].as<int>();
|
|
|
|
std::optional<int> staticEmulatedBatchSize;
|
|
// Argument: Static emulated batch size
|
|
if (result.count("static_emulated_batch_size"))
|
|
{
|
|
staticEmulatedBatchSize = result["static_emulated_batch_size"].as<int>();
|
|
}
|
|
auto const staticEmulatedTimeout = result["static_emulated_timeout"].as<int>();
|
|
|
|
// Argument: Scheduler policy
|
|
batch_scheduler::SchedulerPolicy schedulerPolicy;
|
|
auto const schedulerPolicyArg = result["scheduler_policy"].as<std::string>();
|
|
if (schedulerPolicyArg == "max_utilization")
|
|
{
|
|
schedulerPolicy = batch_scheduler::SchedulerPolicy::MAX_UTILIZATION;
|
|
}
|
|
else if (schedulerPolicyArg == "guaranteed_no_evict")
|
|
{
|
|
schedulerPolicy = batch_scheduler::SchedulerPolicy::GUARANTEED_NO_EVICT;
|
|
}
|
|
else
|
|
{
|
|
TLLM_LOG_ERROR("Unexpected scheduler policy: " + schedulerPolicyArg);
|
|
return 1;
|
|
}
|
|
|
|
// Argument: Log level
|
|
auto logger = std::make_shared<TllmLogger>();
|
|
auto const logLevel = result["log_level"].as<std::string>();
|
|
if (logLevel == "verbose")
|
|
{
|
|
logger->setLevel(trt::ILogger::Severity::kVERBOSE);
|
|
}
|
|
else if (logLevel == "info")
|
|
{
|
|
logger->setLevel(trt::ILogger::Severity::kINFO);
|
|
}
|
|
else if (logLevel == "warning")
|
|
{
|
|
logger->setLevel(trt::ILogger::Severity::kWARNING);
|
|
}
|
|
else if (logLevel == "error")
|
|
{
|
|
logger->setLevel(trt::ILogger::Severity::kERROR);
|
|
}
|
|
else if (logLevel == "internal_error")
|
|
{
|
|
logger->setLevel(trt::ILogger::Severity::kINTERNAL_ERROR);
|
|
}
|
|
else
|
|
{
|
|
TLLM_LOG_ERROR("Unexpected log level: " + logLevel);
|
|
return 1;
|
|
}
|
|
|
|
initTrtLlmPlugins(logger.get());
|
|
|
|
try
|
|
{
|
|
benchmarkGptManager(result["engine_dir"].as<std::string>(), modelType, datasetPath, opCsvFile, maxNumSamples,
|
|
beamWidth, result["warm_up"].as<int>(), eosId, padId, optionalParams, schedulerPolicy, waitSleep,
|
|
returnContextLogits, returnGenerationLogits, staticEmulatedBatchSize, staticEmulatedTimeout);
|
|
}
|
|
catch (const std::exception& e)
|
|
{
|
|
TLLM_LOG_ERROR(e.what());
|
|
return 1;
|
|
}
|
|
return 0;
|
|
}
|