chore: import upstream snapshot with attribution
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wehub-resource-sync
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// 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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#include <ATen/Functions.h>
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#include <ATen/Utils.h>
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#include <ATen/core/TensorBody.h>
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#include <ATen/cuda/CUDAContext.h>
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#include <ATen/cuda/EmptyTensor.h>
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#include <ATen/native/cuda/Resize.h>
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#include <ATen/ops/tensor.h>
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#include <c10/core/ScalarType.h>
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#include <c10/core/TensorOptions.h>
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#include <c10/util/ArrayRef.h>
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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#include <c10/cuda/CUDAFunctions.h>
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#include <c10/cuda/CUDAGuard.h>
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#endif
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#include "ATen/ATen.h"
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#include "gtest/gtest.h"
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#include "paddle/phi/common/float16.h"
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#include "torch/all.h"
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// ============================================================
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// Tests for at::detail::tensor_cpu / tensor_backend / complex variants
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// and the at::tensor() factory macro-generated overloads (ATen/Utils.h)
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// ============================================================
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// ---- tensor_cpu (via at::tensor public API) ----
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TEST(ATenUtilsTest, TensorCPU_Float) {
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std::vector<float> data = {1.0f, 2.0f, 3.0f};
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at::Tensor t = at::tensor(c10::ArrayRef<float>(data),
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at::TensorOptions().dtype(at::kFloat));
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ASSERT_EQ(t.scalar_type(), at::kFloat);
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ASSERT_EQ(t.numel(), 3);
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ASSERT_NEAR(t[0].item<float>(), 1.0f, 1e-6f);
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ASSERT_NEAR(t[2].item<float>(), 3.0f, 1e-6f);
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}
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TEST(ATenUtilsTest, TensorCPU_Double) {
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std::vector<double> data = {1.1, 2.2, 3.3};
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at::Tensor t = at::tensor(c10::ArrayRef<double>(data),
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at::TensorOptions().dtype(at::kDouble));
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ASSERT_EQ(t.scalar_type(), at::kDouble);
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ASSERT_NEAR(t[1].item<double>(), 2.2, 1e-10);
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}
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TEST(ATenUtilsTest, TensorCPU_Int32) {
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std::vector<int32_t> data = {10, 20, 30};
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at::Tensor t = at::tensor(c10::ArrayRef<int32_t>(data),
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at::TensorOptions().dtype(at::kInt));
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ASSERT_EQ(t.scalar_type(), at::kInt);
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ASSERT_EQ(t[0].item<int32_t>(), 10);
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ASSERT_EQ(t[2].item<int32_t>(), 30);
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}
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TEST(ATenUtilsTest, TensorCPU_Int64) {
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std::vector<int64_t> data = {100LL, 200LL};
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at::Tensor t = at::tensor(c10::ArrayRef<int64_t>(data),
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at::TensorOptions().dtype(at::kLong));
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ASSERT_EQ(t.scalar_type(), at::kLong);
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ASSERT_EQ(t[1].item<int64_t>(), 200LL);
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}
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TEST(ATenUtilsTest, TensorCPU_Int8) {
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std::vector<int8_t> data = {-1, 0, 1};
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at::Tensor t = at::tensor(c10::ArrayRef<int8_t>(data),
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at::TensorOptions().dtype(at::kChar));
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ASSERT_EQ(t.scalar_type(), at::kChar);
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ASSERT_EQ(t[0].item<int8_t>(), static_cast<int8_t>(-1));
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}
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TEST(ATenUtilsTest, TensorCPU_Int16) {
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std::vector<int16_t> data = {256, 512};
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at::Tensor t = at::tensor(c10::ArrayRef<int16_t>(data),
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at::TensorOptions().dtype(at::kShort));
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ASSERT_EQ(t.scalar_type(), at::kShort);
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ASSERT_EQ(t[0].item<int16_t>(), static_cast<int16_t>(256));
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}
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TEST(ATenUtilsTest, TensorCPU_UInt8) {
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std::vector<uint8_t> data = {200, 255};
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at::Tensor t = at::tensor(c10::ArrayRef<uint8_t>(data),
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at::TensorOptions().dtype(at::kByte));
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ASSERT_EQ(t.scalar_type(), at::kByte);
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ASSERT_EQ(t[1].item<uint8_t>(), static_cast<uint8_t>(255));
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}
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TEST(ATenUtilsTest, TensorCPU_Bool) {
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// std::vector<bool> is a bitfield specialization without data(), so use a
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// plain C array to construct c10::ArrayRef<bool>.
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bool data[] = {true, false, true};
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at::Tensor t = at::tensor(c10::ArrayRef<bool>(data),
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at::TensorOptions().dtype(at::kBool));
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ASSERT_EQ(t.scalar_type(), at::kBool);
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ASSERT_TRUE(t[0].item<bool>());
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ASSERT_FALSE(t[1].item<bool>());
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}
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// ---- dtype promotion: values stored as native type then cast ----
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TEST(ATenUtilsTest, TensorCPU_DtypePromotion_IntToFloat) {
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// Store int32 values, but request float32 output – should auto-cast.
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std::vector<int32_t> data = {1, 2, 3};
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at::Tensor t = at::tensor(c10::ArrayRef<int32_t>(data),
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at::TensorOptions().dtype(at::kFloat));
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ASSERT_EQ(t.scalar_type(), at::kFloat);
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ASSERT_NEAR(t[0].item<float>(), 1.0f, 1e-6f);
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}
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// ---- contiguity ----
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TEST(ATenUtilsTest, TensorCPU_IsContiguous) {
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std::vector<float> data = {1.0f, 2.0f, 3.0f, 4.0f};
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at::Tensor t = at::tensor(c10::ArrayRef<float>(data),
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at::TensorOptions().dtype(at::kFloat));
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ASSERT_TRUE(t.is_contiguous());
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}
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// ---- tensor_backend (CPU -> same result since default is CPU in tests) ----
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TEST(ATenUtilsTest, TensorBackend_CPUDevice_MatchesTensorCPU) {
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std::vector<float> data = {5.0f, 6.0f};
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at::TensorOptions opts =
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at::TensorOptions().dtype(at::kFloat).device(c10::Device(c10::kCPU));
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at::Tensor t = at::tensor(c10::ArrayRef<float>(data), opts);
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ASSERT_EQ(t.scalar_type(), at::kFloat);
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ASSERT_EQ(t.device().type(), c10::DeviceType::CPU);
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ASSERT_NEAR(t[0].item<float>(), 5.0f, 1e-6f);
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}
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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TEST(ATenUtilsTest, TensorBackend_GPUDevice) {
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if (!at::cuda::is_available()) {
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return;
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}
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std::vector<float> data = {7.0f, 8.0f};
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at::TensorOptions opts =
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at::TensorOptions().dtype(at::kFloat).device(c10::Device(c10::kCUDA, 0));
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at::Tensor t = at::tensor(c10::ArrayRef<float>(data), opts);
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ASSERT_EQ(t.scalar_type(), at::kFloat);
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ASSERT_EQ(t.device().type(), c10::DeviceType::CUDA);
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}
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TEST(ATenUtilsTest, TensorComplexBackend_GPUDevice) {
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if (!at::cuda::is_available()) {
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return;
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}
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std::vector<c10::complex<float>> data = {{1.0f, 0.0f}};
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at::TensorOptions opts = at::TensorOptions()
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.dtype(at::kComplexFloat)
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.device(c10::Device(c10::kCUDA, 0));
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at::Tensor t = at::tensor(c10::ArrayRef<c10::complex<float>>(data), opts);
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ASSERT_EQ(t.scalar_type(), at::kComplexFloat);
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ASSERT_EQ(t.device().type(), c10::DeviceType::CUDA);
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}
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#endif
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