wehub-resource-sync
112 lines
3.9 KiB
C++
112 lines
3.9 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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#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/_values.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/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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// ============================================================
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// Tests for at::Tensor::_values()
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// ============================================================
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// Helper: build 2-D sparse COO tensor from dense indices + float values.
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static at::Tensor make_sparse_coo(at::Tensor indices,
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at::Tensor values,
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c10::IntArrayRef size) {
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return at::sparse_coo_tensor(indices, values, size);
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}
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// ---- COO sparse ----
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TEST(TensorValuesTest, SparseCOO_ValuesHasCorrectNumel) {
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// 3x3 matrix, 2 non-zeros at (0,1)=1.5 and (2,0)=3.0
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at::Tensor indices =
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at::tensor({0, 2, 1, 0}, at::kLong).reshape({2, 2}); // [sparse_dim, nnz]
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at::Tensor values = at::tensor({1.5f, 3.0f}, at::kFloat);
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at::Tensor sparse = make_sparse_coo(indices, values, {3, 3});
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at::Tensor vals = sparse._values();
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ASSERT_EQ(vals.numel(), 2);
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}
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TEST(TensorValuesTest, SparseCOO_ValuesCorrectContent) {
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at::Tensor indices = at::tensor({0, 2, 1, 0}, at::kLong).reshape({2, 2});
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at::Tensor values = at::tensor({1.5f, 3.0f}, at::kFloat);
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at::Tensor sparse = make_sparse_coo(indices, values, {3, 3});
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at::Tensor vals = sparse.coalesce()._values();
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ASSERT_NEAR(vals[0].item<float>(), 1.5f, 1e-5f);
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ASSERT_NEAR(vals[1].item<float>(), 3.0f, 1e-5f);
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}
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TEST(TensorValuesTest, SparseCOO_ValuesIsDense) {
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// The values() tensor of a sparse tensor is itself a dense (strided) tensor.
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at::Tensor indices = at::tensor({0, 0, 1, 1}, at::kLong).reshape({2, 2});
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at::Tensor values = at::tensor({7.0f, 8.0f}, at::kFloat);
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at::Tensor sparse = make_sparse_coo(indices, values, {3, 3});
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at::Tensor vals = sparse._values();
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ASSERT_EQ(vals.layout(), c10::kStrided);
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}
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TEST(TensorValuesTest, SparseCOO_ValuesScalarType) {
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at::Tensor indices = at::tensor({0, 0, 1, 2}, at::kLong).reshape({2, 2});
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at::Tensor values = at::tensor({1, 2}, at::kInt);
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at::Tensor sparse = make_sparse_coo(indices, values, {3, 3});
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at::Tensor vals = sparse._values();
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ASSERT_EQ(vals.scalar_type(), at::kInt);
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}
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// ---- Dense tensor must throw ----
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TEST(TensorValuesTest, DenseTensor_Throws) {
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at::Tensor dense = at::ones({3, 3}, at::kFloat);
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ASSERT_THROW(dense._values(), std::exception);
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}
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// ---- CSR sparse ----
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TEST(TensorValuesTest, SparseCsr_ValuesCorrect) {
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// 3x3 identity in CSR: values=[1,1,1], col_indices=[0,1,2],
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// row_ptrs=[0,1,2,3]
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at::Tensor crow = at::tensor({0, 1, 2, 3}, at::kInt);
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at::Tensor col = at::tensor({0, 1, 2}, at::kInt);
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at::Tensor vals_in = at::tensor({1.0f, 1.0f, 1.0f}, at::kFloat);
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at::Tensor sparse_csr =
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at::sparse_csr_tensor(crow, col, vals_in, {3, 3}, at::TensorOptions());
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// PyTorch does not dispatch _values for SparseCsr tensors
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ASSERT_THROW(sparse_csr._values(), std::exception);
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}
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