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97 lines
3.1 KiB
97 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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#include <glog/logging.h>
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#include <gtest/gtest.h>
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#include <map>
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#include "framework/core/net/net.h"
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#include "framework/graph/graph.h"
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#include "framework/graph/graph_global_mem.h"
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#include "paddle/fluid/inference/anakin/engine.h"
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using anakin::graph::GraphGlobalMem;
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using anakin::AK_FLOAT;
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using anakin::Precision;
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using anakin::saber::NV;
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using anakin::saber::X86;
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using anakin::saber::Shape;
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using anakin::PBlock;
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using anakin::PTuple;
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namespace paddle {
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namespace inference {
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namespace anakin {
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class TestAnakinEngine : public ::testing::Test {
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protected:
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void SetUp() override;
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void TearDown() override {}
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protected:
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using AnakinNvEngineT = AnakinEngine<NV, Precision::FP32>;
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std::unique_ptr<AnakinNvEngineT> engine_{nullptr};
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};
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void TestAnakinEngine::SetUp() {
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engine_.reset(new AnakinEngine<NV, Precision::FP32>(true));
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}
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TEST_F(TestAnakinEngine, Execute) {
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engine_->AddOp("op1", "Dense", {"x"}, {"y"});
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engine_->AddOpAttr("op1", "out_dim", 2);
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engine_->AddOpAttr("op1", "bias_term", false);
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engine_->AddOpAttr("op1", "axis", 1);
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std::vector<int> shape = {1, 1, 1, 2};
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Shape tmp_shape(shape);
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// PBlock<NV> weight1(tmp_shape);
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auto *weight1 =
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GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(tmp_shape);
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// auto *weight1 = new PBlock<NV>(tmp_shape, AK_FLOAT);
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float *cpu_data = static_cast<float *>(weight1->h_tensor().mutable_data());
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cpu_data[0] = 2.;
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weight1->d_tensor().set_shape(tmp_shape);
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weight1->d_tensor().copy_from(weight1->h_tensor());
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engine_->AddOpAttr("op1", "weight_1", *weight1);
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engine_->Freeze();
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// PTuple<int> input_shape = {1};
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// engine_->AddOpAttr("x", "input_shape", input_shape);
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engine_->SetInputShape("x", {1, 1, 1, 1});
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engine_->Optimize();
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engine_->InitGraph();
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framework::LoDTensor x;
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framework::LoDTensor y;
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x.Resize({1, 1, 1, 1});
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y.Resize({1, 1, 1, 2});
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auto *x_data = x.mutable_data<float>(platform::CUDAPlace());
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float x_data_cpu[] = {1.};
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cudaMemcpy(x_data, x_data_cpu, sizeof(float), cudaMemcpyHostToDevice);
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std::map<std::string, framework::LoDTensor *> inputs = {{"x", &x}};
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auto *y_data = y.mutable_data<float>(platform::CUDAPlace());
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std::map<std::string, framework::LoDTensor *> outputs = {{"y", &y}};
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engine_->Execute(inputs, outputs);
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auto *y_data_gpu = y_data;
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float y_data_cpu[2];
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cudaMemcpy(y_data_cpu, y_data_gpu, sizeof(float) * 2, cudaMemcpyDeviceToHost);
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LOG(INFO) << "output value: " << y_data_cpu[0] << ", " << y_data_cpu[1];
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}
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} // namespace anakin
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} // namespace inference
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} // namespace paddle
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