chore: import upstream snapshot with attribution

test/cpp/compat/ATen_cuda_test.cc

@@ -0,0 +1,116 @@
+// Copyright (c) 2026 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.
+
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+
+#include <ATen/Functions.h>
+#include <ATen/core/TensorBody.h>
+#include <ATen/ops/tensor.h>
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/ScalarType.h>
+#include <c10/cuda/CUDAFunctions.h>
+#include <c10/cuda/CUDAGuard.h>
+
+#include "ATen/ATen.h"
+#include "gtest/gtest.h"
+#include "torch/all.h"
+
+// ============================================================
+// Tests for at::Tensor::cuda()
+// ============================================================
+
+// After cuda(), the tensor should reside on a GPU device.
+TEST(TensorCudaTest, CpuTensorMovesToCuda) {
+  at::Tensor cpu_t = at::tensor({1.0f, 2.0f, 3.0f}, at::kFloat);
+  ASSERT_TRUE(cpu_t.is_cpu());
+
+  at::Tensor cuda_t = cpu_t.cuda();
+  ASSERT_TRUE(cuda_t.is_cuda());
+  ASSERT_FALSE(cuda_t.is_cpu());
+}
+
+// dtype and numel must be preserved.
+TEST(TensorCudaTest, DtypeAndNumelPreserved) {
+  at::Tensor cpu_t = at::tensor({1, 2, 3, 4}, at::kInt);
+  at::Tensor cuda_t = cpu_t.cuda();
+
+  ASSERT_EQ(cuda_t.scalar_type(), at::kInt);
+  ASSERT_EQ(cuda_t.numel(), 4);
+}
+
+// Values should round-trip back to CPU intact.
+TEST(TensorCudaTest, ValuesPreservedAfterRoundTrip) {
+  std::vector<float> data = {1.0f, 2.5f, -3.0f, 4.75f};
+  at::Tensor cpu_t = at::tensor(data, at::kFloat);
+  at::Tensor cuda_t = cpu_t.cuda();
+  at::Tensor back = cuda_t.cpu();
+
+  ASSERT_EQ(back.numel(), static_cast<int64_t>(data.size()));
+  for (int64_t i = 0; i < back.numel(); ++i) {
+    ASSERT_NEAR(back[i].item<float>(), data[static_cast<size_t>(i)], 1e-5f);
+  }
+}
+
+// shape (sizes) should be preserved.
+TEST(TensorCudaTest, ShapePreserved) {
+  at::Tensor cpu_t = at::zeros({2, 3, 4}, at::kFloat);
+  at::Tensor cuda_t = cpu_t.cuda();
+
+  ASSERT_EQ(cuda_t.dim(), 3);
+  ASSERT_EQ(cuda_t.size(0), 2);
+  ASSERT_EQ(cuda_t.size(1), 3);
+  ASSERT_EQ(cuda_t.size(2), 4);
+}
+
+// An already-CUDA tensor should still be CUDA after another cuda() call.
+TEST(TensorCudaTest, AlreadyCudaTensorStaysCuda) {
+  at::Tensor cpu_t = at::tensor({7.0f}, at::kFloat);
+  at::Tensor cuda_t = cpu_t.cuda();
+  at::Tensor cuda_t2 = cuda_t.cuda();
+
+  ASSERT_TRUE(cuda_t2.is_cuda());
+  ASSERT_NEAR(cuda_t2.cpu().item<float>(), 7.0f, 1e-6f);
+}
+
+// device() should report a CUDA device.
+TEST(TensorCudaTest, DeviceIsCuda) {
+  at::Tensor cpu_t = at::tensor({0.0f}, at::kFloat);
+  at::Tensor cuda_t = cpu_t.cuda();
+
+  ASSERT_EQ(cuda_t.device().type(), c10::DeviceType::CUDA);
+}
+
+TEST(TensorCudaTest, DefaultCudaUsesCurrentDevice) {
+  if (c10::cuda::device_count() < 2) {
+    return;
+  }
+  c10::cuda::CUDAGuard guard(1);
+  at::Tensor cpu_t = at::tensor({1.0f}, at::kFloat);
+  at::Tensor cuda_t = cpu_t.cuda();
+
+  ASSERT_EQ(cuda_t.device().type(), c10::DeviceType::CUDA);
+  ASSERT_EQ(cuda_t.device().index(), 1);
+}
+
+// is_cuda() / is_cpu() are mutually exclusive.
+TEST(TensorCudaTest, IsCudaAndIsCpuMutuallyExclusive) {
+  at::Tensor cpu_t = at::tensor({1.0f, 2.0f}, at::kFloat);
+  at::Tensor cuda_t = cpu_t.cuda();
+
+  ASSERT_TRUE(cuda_t.is_cuda());
+  ASSERT_FALSE(cuda_t.is_cpu());
+}
+
+#endif