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109 lines
3.1 KiB
109 lines
3.1 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include <NvInfer.h>
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#include <cuda.h>
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#include <glog/logging.h>
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#include <string>
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#include <utility>
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#include <vector>
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#include "paddle/fluid/platform/dynload/tensorrt.h"
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#include "paddle/fluid/platform/enforce.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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namespace dy = paddle::platform::dynload;
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// TensorRT data type to size
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const int kDataTypeSize[] = {
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4, // kFLOAT
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2, // kHALF
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1, // kINT8
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4 // kINT32
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};
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// The following two API are implemented in TensorRT's header file, cannot load
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// from the dynamic library. So create our own implementation and directly
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// trigger the method from the dynamic library.
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static nvinfer1::IBuilder* createInferBuilder(nvinfer1::ILogger* logger) {
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return static_cast<nvinfer1::IBuilder*>(
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dy::createInferBuilder_INTERNAL(logger, NV_TENSORRT_VERSION));
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}
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static nvinfer1::IRuntime* createInferRuntime(nvinfer1::ILogger* logger) {
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return static_cast<nvinfer1::IRuntime*>(
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dy::createInferRuntime_INTERNAL(logger, NV_TENSORRT_VERSION));
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}
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// A logger for create TensorRT infer builder.
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class NaiveLogger : public nvinfer1::ILogger {
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public:
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void log(nvinfer1::ILogger::Severity severity, const char* msg) override {
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switch (severity) {
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case Severity::kINFO:
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VLOG(3) << msg;
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break;
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case Severity::kWARNING:
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LOG(WARNING) << msg;
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break;
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case Severity::kINTERNAL_ERROR:
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case Severity::kERROR:
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LOG(ERROR) << msg;
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break;
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default:
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break;
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}
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}
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static nvinfer1::ILogger& Global() {
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static nvinfer1::ILogger* x = new NaiveLogger;
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return *x;
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}
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~NaiveLogger() override {}
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};
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class NaiveProfiler : public nvinfer1::IProfiler {
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public:
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typedef std::pair<std::string, float> Record;
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std::vector<Record> mProfile;
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virtual void reportLayerTime(const char* layerName, float ms) {
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auto record =
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std::find_if(mProfile.begin(), mProfile.end(),
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[&](const Record& r) { return r.first == layerName; });
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if (record == mProfile.end())
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mProfile.push_back(std::make_pair(layerName, ms));
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else
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record->second += ms;
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}
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void printLayerTimes() {
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float totalTime = 0;
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for (size_t i = 0; i < mProfile.size(); i++) {
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printf("%-40.40s %4.3fms\n", mProfile[i].first.c_str(),
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mProfile[i].second);
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totalTime += mProfile[i].second;
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}
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printf("Time over all layers: %4.3f\n", totalTime);
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}
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};
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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