wehub-resource-sync
155 lines
5.3 KiB
C++
155 lines
5.3 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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#include "gtest/gtest.h"
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#include "paddle/phi/api/include/api.h"
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#include "paddle/phi/api/include/context_pool.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/common/int_array.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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PD_DECLARE_KERNEL(full, CPU, ALL_LAYOUT);
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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PD_DECLARE_KERNEL(full, GPU, ALL_LAYOUT);
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#endif
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namespace phi {
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namespace tests {
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TEST(IntArray, ConstructFromCPUDenseTensor) {
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auto& pool = paddle::experimental::DeviceContextPool::Instance();
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const auto* dev_ctx = static_cast<const CPUContext*>(pool.Get(CPUPlace()));
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DenseTensor shape = Full<int>(*dev_ctx, {2}, 3);
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DenseTensor out = Full<int>(*dev_ctx, shape, 1);
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ASSERT_EQ(out.dims().size(), 2);
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ASSERT_EQ(out.dims()[0], 3);
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ASSERT_EQ(out.dims()[1], 3);
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ASSERT_EQ(out.numel(), 9);
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}
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TEST(IntArray, ConstructFromCPUDenseTensorVector) {
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auto& pool = paddle::experimental::DeviceContextPool::Instance();
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const auto* dev_ctx = static_cast<const CPUContext*>(pool.Get(CPUPlace()));
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DenseTensor shape0 = Full<int>(*dev_ctx, {1}, 3);
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DenseTensor shape1 = Full<int64_t>(*dev_ctx, {1}, 3);
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std::vector<DenseTensor> shape{shape0, shape1};
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DenseTensor out = Full<int>(*dev_ctx, shape, 1);
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ASSERT_EQ(out.dims().size(), 2);
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ASSERT_EQ(out.dims()[0], 3);
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ASSERT_EQ(out.dims()[1], 3);
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ASSERT_EQ(out.numel(), 9);
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}
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TEST(IntArray, ConstructFromCPUTensor) {
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auto shape = paddle::experimental::full({2}, 3, DataType::INT64);
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auto out = paddle::experimental::full(shape, 1);
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ASSERT_EQ(out.dims().size(), 2);
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ASSERT_EQ(out.dims()[0], 3);
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ASSERT_EQ(out.dims()[1], 3);
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ASSERT_EQ(out.numel(), 9);
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}
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TEST(IntArray, ConstructFromCPUTensorVector) {
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auto shape0 = paddle::experimental::full({2}, 3, DataType::INT64);
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auto shape1 = paddle::experimental::full({2}, 3, DataType::INT32);
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std::vector<paddle::Tensor> shape{shape0, shape0};
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auto out = paddle::experimental::full(shape, 1);
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std::vector<paddle::Tensor> shape_new{shape0, shape1};
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auto out1 = paddle::experimental::full(shape_new, 1);
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ASSERT_EQ(out.dims().size(), 2);
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ASSERT_EQ(out.dims()[0], 3);
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ASSERT_EQ(out.dims()[1], 3);
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ASSERT_EQ(out.numel(), 9);
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ASSERT_EQ(out1.dims().size(), 2);
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ASSERT_EQ(out1.dims()[0], 3);
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ASSERT_EQ(out1.dims()[1], 3);
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ASSERT_EQ(out1.numel(), 9);
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}
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TEST(IntArray, ThrowException) {
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auto shape = paddle::experimental::full({2}, 3, DataType::FLOAT32);
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auto create_int_array = [&shape]() -> paddle::experimental::IntArray {
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paddle::experimental::IntArray int_array{shape};
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return int_array;
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};
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ASSERT_ANY_THROW(create_int_array());
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}
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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TEST(IntArray, ConstructFromGPUDenseTensor) {
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auto& pool = paddle::experimental::DeviceContextPool::Instance();
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const auto* dev_ctx =
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static_cast<const phi::GPUContext*>(pool.Get(GPUPlace()));
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DenseTensor shape = Full<int>(*dev_ctx, {2}, 3);
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DenseTensor out = Full<int>(*dev_ctx, shape, 1);
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ASSERT_EQ(out.dims().size(), 2);
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ASSERT_EQ(out.dims()[0], 3);
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ASSERT_EQ(out.dims()[1], 3);
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ASSERT_EQ(out.numel(), 9);
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}
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TEST(IntArray, ConstructFromGPUDenseTensorVector) {
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auto& pool = paddle::experimental::DeviceContextPool::Instance();
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const auto* dev_ctx =
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static_cast<const phi::GPUContext*>(pool.Get(GPUPlace()));
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DenseTensor shape0 = Full<int>(*dev_ctx, {1}, 3);
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DenseTensor shape1 = Full<int64_t>(*dev_ctx, {1}, 3);
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std::vector<DenseTensor> shape{shape0, shape1};
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DenseTensor out = Full<int>(*dev_ctx, shape, 1);
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ASSERT_EQ(out.dims().size(), 2);
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ASSERT_EQ(out.dims()[0], 3);
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ASSERT_EQ(out.dims()[1], 3);
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ASSERT_EQ(out.numel(), 9);
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}
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TEST(IntArray, ConstructFromGPUTensor) {
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auto shape = paddle::experimental::full({2}, 3, DataType::INT64, GPUPlace());
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auto out = paddle::experimental::full(shape, 1);
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ASSERT_EQ(out.dims().size(), 2);
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ASSERT_EQ(out.dims()[0], 3);
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ASSERT_EQ(out.dims()[1], 3);
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ASSERT_EQ(out.numel(), 9);
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}
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TEST(IntArray, ConstructFromGPUTensorVector) {
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auto shape0 = paddle::experimental::full({2}, 3, DataType::INT64, GPUPlace());
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auto shape1 = paddle::experimental::full({2}, 3, DataType::INT32, GPUPlace());
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std::vector<paddle::Tensor> shape{shape0, shape0};
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auto out = paddle::experimental::full(shape, 1);
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std::vector<paddle::Tensor> shape_new{shape0, shape1};
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auto out1 = paddle::experimental::full(shape_new, 1);
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ASSERT_EQ(out.dims().size(), 2);
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ASSERT_EQ(out.dims()[0], 3);
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ASSERT_EQ(out.dims()[1], 3);
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ASSERT_EQ(out.numel(), 9);
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ASSERT_EQ(out1.dims().size(), 2);
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ASSERT_EQ(out1.dims()[0], 3);
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ASSERT_EQ(out1.dims()[1], 3);
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ASSERT_EQ(out1.numel(), 9);
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}
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#endif
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} // namespace tests
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} // namespace phi
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