wehub-resource-sync
117 lines
3.9 KiB
C++
117 lines
3.9 KiB
C++
// Copyright (c) 2025 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/core/TensorBody.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/Layout.h>
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#include <c10/core/ScalarType.h>
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#include <c10/core/SymInt.h>
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#include <c10/core/TensorOptions.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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TEST(TensorBaseTest, IsSameAPI) {
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// Test is_same() API - checks if two tensors share the same underlying data
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at::Tensor tensor1 = at::ones({2, 3}, at::kFloat);
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at::TensorBase tensor2 = tensor1; // Same tensor
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at::TensorBase tensor3 = at::ones({2, 3}, at::kFloat); // Different tensor
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// tensor1 and tensor2 should point to the same underlying tensor
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ASSERT_TRUE(tensor1.is_same(tensor2));
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ASSERT_TRUE(tensor2.is_same(tensor1));
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// tensor1 and tensor3 should be different tensors
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ASSERT_FALSE(tensor1.is_same(tensor3));
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ASSERT_FALSE(tensor3.is_same(tensor1));
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// A tensor should be the same as itself
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ASSERT_TRUE(tensor1.is_same(tensor1));
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// Test with view - in Paddle, view creates a new tensor implementation
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// even though they may share underlying data storage
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at::TensorBase view_tensor = tensor1.view({6});
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// View tensor has different impl pointer in Paddle
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ASSERT_FALSE(tensor1.is_same(view_tensor));
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// Test with undefined tensors
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at::TensorBase undefined1;
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at::TensorBase undefined2;
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ASSERT_TRUE(undefined1.is_same(undefined2)); // Both undefined
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}
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TEST(TensorBaseTest, UseCountAPI) {
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// Test use_count() API - returns reference count of underlying tensor
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at::Tensor tensor1 = at::ones({2, 3}, at::kFloat);
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// Initial reference count should be 1
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ASSERT_EQ(tensor1.use_count(), 1);
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// Create a copy - reference count should increase
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at::TensorBase tensor2 = tensor1;
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ASSERT_EQ(tensor1.use_count(), 2);
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ASSERT_EQ(tensor2.use_count(), 2);
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// Create another copy
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at::TensorBase tensor3 = tensor1;
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ASSERT_EQ(tensor1.use_count(), 3);
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ASSERT_EQ(tensor2.use_count(), 3);
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ASSERT_EQ(tensor3.use_count(), 3);
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// Reset one copy - reference count should decrease
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tensor2.reset();
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ASSERT_EQ(tensor1.use_count(), 2);
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ASSERT_EQ(tensor3.use_count(), 2);
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// Reset another copy
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tensor3.reset();
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ASSERT_EQ(tensor1.use_count(), 1);
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// Test with view - in Paddle, view creates a new tensor with separate impl
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// So the use_count remains 1 for each
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{
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at::TensorBase view_tensor = tensor1.view({6});
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// Each tensor has its own impl, so use_count is 1 for each
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ASSERT_EQ(tensor1.use_count(), 1);
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ASSERT_EQ(view_tensor.use_count(), 1);
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}
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// After view goes out of scope
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ASSERT_EQ(tensor1.use_count(), 1);
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}
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TEST(TensorBaseTest, WeakUseCountAPI) {
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// Test weak_use_count() API
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// Compat exposes PyTorch-visible semantics: live TensorImpl wrappers report
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// the self weak-reference count as 1.
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at::TensorBase tensor1 = at::ones({2, 3}, at::kFloat);
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ASSERT_EQ(tensor1.weak_use_count(), 1);
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at::TensorBase tensor2 = tensor1;
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ASSERT_EQ(tensor1.weak_use_count(), 1);
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ASSERT_EQ(tensor2.weak_use_count(), 1);
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// Test with undefined tensor
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at::TensorBase undefined_tensor;
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ASSERT_EQ(undefined_tensor.weak_use_count(), 0);
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}
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