wehub-resource-sync
117 lines
3.6 KiB
C++
117 lines
3.6 KiB
C++
// Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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#include <ATen/Functions.h>
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#include <ATen/core/TensorBody.h>
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#include <ATen/ops/tensor.h>
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#include <c10/core/Device.h>
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#include <c10/core/DeviceType.h>
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#include <c10/core/ScalarType.h>
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#include <c10/cuda/CUDAFunctions.h>
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#include <c10/cuda/CUDAGuard.h>
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#include "ATen/ATen.h"
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#include "gtest/gtest.h"
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#include "torch/all.h"
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// ============================================================
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// Tests for at::Tensor::cuda()
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// ============================================================
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// After cuda(), the tensor should reside on a GPU device.
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TEST(TensorCudaTest, CpuTensorMovesToCuda) {
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at::Tensor cpu_t = at::tensor({1.0f, 2.0f, 3.0f}, at::kFloat);
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ASSERT_TRUE(cpu_t.is_cpu());
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at::Tensor cuda_t = cpu_t.cuda();
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ASSERT_TRUE(cuda_t.is_cuda());
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ASSERT_FALSE(cuda_t.is_cpu());
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}
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// dtype and numel must be preserved.
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TEST(TensorCudaTest, DtypeAndNumelPreserved) {
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at::Tensor cpu_t = at::tensor({1, 2, 3, 4}, at::kInt);
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at::Tensor cuda_t = cpu_t.cuda();
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ASSERT_EQ(cuda_t.scalar_type(), at::kInt);
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ASSERT_EQ(cuda_t.numel(), 4);
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}
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// Values should round-trip back to CPU intact.
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TEST(TensorCudaTest, ValuesPreservedAfterRoundTrip) {
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std::vector<float> data = {1.0f, 2.5f, -3.0f, 4.75f};
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at::Tensor cpu_t = at::tensor(data, at::kFloat);
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at::Tensor cuda_t = cpu_t.cuda();
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at::Tensor back = cuda_t.cpu();
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ASSERT_EQ(back.numel(), static_cast<int64_t>(data.size()));
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for (int64_t i = 0; i < back.numel(); ++i) {
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ASSERT_NEAR(back[i].item<float>(), data[static_cast<size_t>(i)], 1e-5f);
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}
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}
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// shape (sizes) should be preserved.
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TEST(TensorCudaTest, ShapePreserved) {
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at::Tensor cpu_t = at::zeros({2, 3, 4}, at::kFloat);
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at::Tensor cuda_t = cpu_t.cuda();
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ASSERT_EQ(cuda_t.dim(), 3);
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ASSERT_EQ(cuda_t.size(0), 2);
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ASSERT_EQ(cuda_t.size(1), 3);
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ASSERT_EQ(cuda_t.size(2), 4);
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}
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// An already-CUDA tensor should still be CUDA after another cuda() call.
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TEST(TensorCudaTest, AlreadyCudaTensorStaysCuda) {
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at::Tensor cpu_t = at::tensor({7.0f}, at::kFloat);
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at::Tensor cuda_t = cpu_t.cuda();
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at::Tensor cuda_t2 = cuda_t.cuda();
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ASSERT_TRUE(cuda_t2.is_cuda());
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ASSERT_NEAR(cuda_t2.cpu().item<float>(), 7.0f, 1e-6f);
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}
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// device() should report a CUDA device.
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TEST(TensorCudaTest, DeviceIsCuda) {
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at::Tensor cpu_t = at::tensor({0.0f}, at::kFloat);
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at::Tensor cuda_t = cpu_t.cuda();
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ASSERT_EQ(cuda_t.device().type(), c10::DeviceType::CUDA);
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}
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TEST(TensorCudaTest, DefaultCudaUsesCurrentDevice) {
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if (c10::cuda::device_count() < 2) {
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return;
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}
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c10::cuda::CUDAGuard guard(1);
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at::Tensor cpu_t = at::tensor({1.0f}, at::kFloat);
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at::Tensor cuda_t = cpu_t.cuda();
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ASSERT_EQ(cuda_t.device().type(), c10::DeviceType::CUDA);
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ASSERT_EQ(cuda_t.device().index(), 1);
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}
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// is_cuda() / is_cpu() are mutually exclusive.
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TEST(TensorCudaTest, IsCudaAndIsCpuMutuallyExclusive) {
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at::Tensor cpu_t = at::tensor({1.0f, 2.0f}, at::kFloat);
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at::Tensor cuda_t = cpu_t.cuda();
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ASSERT_TRUE(cuda_t.is_cuda());
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ASSERT_FALSE(cuda_t.is_cpu());
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}
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#endif
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