wehub-resource-sync
102 lines
3.4 KiB
C++
102 lines
3.4 KiB
C++
/* Copyright (c) 2023 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 <gtest/gtest.h>
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#include <fstream>
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/framework/naive_executor.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/framework/scope.h"
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#include "paddle/phi/common/place.h"
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#include "paddle/phi/core/enforce.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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template <typename DataType>
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void AddVarToScope(const std::string var_name,
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paddle::framework::Scope* scope,
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const phi::DDim& dims) {
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std::random_device seed;
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std::default_random_engine engine(seed());
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std::uniform_real_distribution<float> dist(0, 100);
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phi::DenseTensor tmp_tensor;
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auto* tmp_data = tmp_tensor.mutable_data<DataType>(dims, phi::CPUPlace());
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auto* tensor = scope->Var(var_name)->GetMutable<phi::DenseTensor>();
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tensor->mutable_data<DataType>(dims, phi::CPUPlace());
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for (auto i = 0; i < tensor->numel(); ++i) {
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tmp_data[i] = static_cast<DataType>(dist(engine));
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}
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paddle::framework::TensorCopySync(tmp_tensor, phi::CPUPlace(), tensor);
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}
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void test_squeeze() {
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framework::Scope scope;
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phi::CPUPlace cpu_place;
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// Prepare Op description
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framework::OpDesc desc;
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// We assume it is NHWC, so that can use this transformation
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phi::OneDNNContext::tls().set_cur_paddle_data_layout(DataLayout::NHWC);
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desc.SetType("squeeze2");
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desc.SetInput("X", {"squeeze-X"});
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desc.SetOutput("Out", {"squeeze-Out"});
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// DataLayout::NHWC will make it become {2, 3, 2, 1}
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AddVarToScope<float>("squeeze-X", &scope, {2, 2, 1, 3});
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AddVarToScope<float>("squeeze-Out", &scope, {2, 3, 2});
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// transform will make it become -1
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std::vector<int> axes({-2});
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desc.SetAttr("axes", axes);
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desc.SetAttr("use_onednn", true);
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auto op = paddle::framework::OpRegistry::CreateOp(desc);
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op->Run(scope, cpu_place);
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}
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void test_squeeze2() {
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framework::Scope scope;
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phi::CPUPlace cpu_place;
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// Prepare Op description
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framework::OpDesc desc;
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// We assume it is HNWC, so that can use this transformation
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phi::OneDNNContext::tls().set_cur_paddle_data_layout(DataLayout::NHWC);
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desc.SetType("squeeze2");
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desc.SetInput("X", {"squeeze-X"});
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desc.SetOutput("Out", {"squeeze-Out"});
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// DataLayout::NHWC will make it become {2, 1, 3, 2}
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AddVarToScope<float>("squeeze-X", &scope, {2, 3, 2, 1});
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AddVarToScope<float>("squeeze-Out", &scope, {2, 3, 2});
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// transform will make it become -3(1)
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std::vector<int> axes({-1});
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desc.SetAttr("axes", axes);
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desc.SetAttr("use_onednn", true);
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auto op = paddle::framework::OpRegistry::CreateOp(desc);
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op->Run(scope, cpu_place);
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}
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TEST(SqueezeOpConverter, normal) { test_squeeze(); }
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TEST(SqueezeOpConverter_2, normal) { test_squeeze2(); }
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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