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Paddle/paddle/fluid/inference/tests/api/analyzer_mmp_tester.cc

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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/transfer_scope_cache.h"
#include "paddle/fluid/inference/tests/api/tester_helper.h"
#include <random>
// Here add missing commands
DEFINE_string(infer_model2, "", "model path");
DEFINE_string(infer_model3, "", "model path");
namespace paddle {
namespace inference {
// Shape of Input to models
const int N = 1, C = 3, H = 224, W = 224;
void SetConfig(AnalysisConfig* config, const std::string& infer_model) {
config->SetModel(infer_model + "/__model__", infer_model + "/__params__");
config->DisableFCPadding();
config->SwitchUseFeedFetchOps(false);
config->SwitchSpecifyInputNames(true);
}
std::unique_ptr<PaddlePredictor> InitializePredictor(
const std::string& infer_model, std::vector<float>& data, bool use_mkldnn) {
AnalysisConfig cfg;
SetConfig(&cfg, infer_model);
if (use_mkldnn) {
cfg.EnableMKLDNN();
}
auto predictor = ::paddle::CreatePaddlePredictor<AnalysisConfig>(cfg);
auto input_name = predictor->GetInputNames()[0];
auto input = predictor->GetInputTensor(input_name);
std::vector<int> shape{N, C, H, W};
input->Reshape(std::move(shape));
input->copy_from_cpu(data.data());
return predictor;
}
// Compare result of NativeConfig and AnalysisConfig
void compare(bool use_mkldnn = false) {
// Create Input to models
std::vector<float> data(N * C * H * W);
std::default_random_engine re{1234};
std::uniform_real_distribution<float> sampler{0.0, 1.0};
for (auto& v : data) {
v = sampler(re);
}
// Initialize Models predictors
auto predictor_1 = InitializePredictor(FLAGS_infer_model, data, use_mkldnn);
auto predictor_xx = InitializePredictor(FLAGS_infer_model2, data, use_mkldnn);
auto predictor_3 = InitializePredictor(FLAGS_infer_model3, data, use_mkldnn);
// Run single xx model
predictor_xx->ZeroCopyRun();
auto output =
predictor_xx->GetOutputTensor(predictor_xx->GetOutputNames()[0]);
auto output_shape = output->shape();
int numel = std::accumulate(output_shape.begin(), output_shape.end(), 1,
std::multiplies<int>());
std::vector<float> xx_output(numel);
output->copy_to_cpu(xx_output.data());
// Initialize xx model's predictor to trigger oneDNN cache clearing
predictor_xx =
std::move(InitializePredictor(FLAGS_infer_model2, data, use_mkldnn));
// Run sequence of models
predictor_1->ZeroCopyRun();
predictor_xx->ZeroCopyRun();
predictor_3->ZeroCopyRun();
// Get again output of xx model , but when all three models were executed
std::vector<float> xx2_output(numel);
output = predictor_xx->GetOutputTensor(predictor_xx->GetOutputNames()[0]);
output->copy_to_cpu(xx2_output.data());
// compare results
auto result = std::equal(
xx_output.begin(), xx_output.end(), xx2_output.begin(),
[](const float& l, const float& r) { return fabs(l - r) < 1e-4; });
PADDLE_ENFORCE_EQ(result, true, paddle::platform::errors::Fatal(
"Results of model run independently "
"differs from results of the same model "
"run as a sequence of models"));
}
TEST(Analyzer_mmp, compare) { compare(); }
#ifdef PADDLE_WITH_MKLDNN
TEST(Analyzer_mmp, compare_mkldnn) { compare(true /* use_mkldnn */); }
#endif
} // namespace inference
} // namespace paddle