wehub-resource-sync
83 lines
2.6 KiB
C++
83 lines
2.6 KiB
C++
/* Copyright (c) 2022 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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/*
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* This file contains a simple demo for how to take a model for inference with
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* IPUs.
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* Model: wget -q
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* http://paddle-inference-dist.bj.bcebos.com/word2vec.inference.model.tar.gz
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*/
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#include <iostream>
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#include <numeric>
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#include <string>
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#include <vector>
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#include "glog/logging.h"
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#include "paddle/common/flags.h"
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#include "paddle/fluid/inference/api/paddle_inference_api.h"
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PD_DEFINE_string(infer_model, "", "Directory of the inference model.");
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using paddle_infer::Config;
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using paddle_infer::CreatePredictor;
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using paddle_infer::Predictor;
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void inference(std::string model_path,
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bool use_ipu,
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std::vector<float> *out_data) {
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//# 1. Create Predictor with a config.
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Config config;
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config.SetModel(FLAGS_infer_model);
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if (use_ipu) {
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// ipu_device_num, ipu_micro_batch_size
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config.EnableIpu(1, 4);
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}
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auto predictor = CreatePredictor(config);
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//# 2. Prepare input/output tensor.
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auto input_names = predictor->GetInputNames();
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std::vector<int64_t> data{1, 2, 3, 4};
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// For simplicity, we set all the slots with the same data.
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for (auto input_name : input_names) {
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auto input_tensor = predictor->GetInputHandle(input_name);
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input_tensor->Reshape({4, 1});
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input_tensor->CopyFromCpu(data.data());
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}
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//# 3. Run
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predictor->Run();
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//# 4. Get output.
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auto output_names = predictor->GetOutputNames();
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auto output_tensor = predictor->GetOutputHandle(output_names[0]);
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std::vector<int> output_shape = output_tensor->shape();
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int out_num = std::accumulate(
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output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
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out_data->resize(out_num);
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output_tensor->CopyToCpu(out_data->data());
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}
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int main(int argc, char *argv[]) {
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::paddle::flags::ParseCommandLineFlags(&argc, &argv);
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std::vector<float> ipu_result;
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std::vector<float> cpu_result;
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inference(FLAGS_infer_model, true, &ipu_result);
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inference(FLAGS_infer_model, false, &cpu_result);
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for (size_t i = 0; i < ipu_result.size(); i++) {
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CHECK_NEAR(ipu_result[i], cpu_result[i], 1e-6);
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}
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LOG(INFO) << "Finished";
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}
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