wehub-resource-sync
111 lines
3.7 KiB
C++
111 lines
3.7 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/tensor.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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// ============================================================
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// Tests for at::Tensor::sum()
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// ============================================================
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TEST(TensorSumTest, SumAllElementsNoArgs) {
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// sum() without arguments: sum of all elements, keeps original dtype
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at::Tensor t = at::ones({2, 3}, at::kFloat);
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at::Tensor result = t.sum();
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ASSERT_EQ(result.numel(), 1);
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ASSERT_FLOAT_EQ(result.item<float>(), 6.0f);
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}
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TEST(TensorSumTest, SumAllElementsWithDtype) {
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// sum(dtype): reduce all elements, cast result to given dtype
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at::Tensor t = at::ones({4, 4}, at::kFloat);
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at::Tensor result = t.sum(at::kDouble);
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ASSERT_EQ(result.numel(), 1);
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ASSERT_EQ(result.scalar_type(), at::kDouble);
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ASSERT_DOUBLE_EQ(result.item<double>(), 16.0);
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}
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TEST(TensorSumTest, SumAlongDim0) {
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// sum(dim={0}): reduce along first dimension
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at::Tensor t = at::ones({3, 4}, at::kFloat);
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at::Tensor result = t.sum(at::IntArrayRef{0}, /*keepdim=*/false);
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ASSERT_EQ(result.dim(), 1);
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ASSERT_EQ(result.sizes(), c10::IntArrayRef({4}));
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for (int64_t i = 0; i < 4; ++i) {
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ASSERT_FLOAT_EQ(result[i].item<float>(), 3.0f);
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}
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}
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TEST(TensorSumTest, SumAlongDim1) {
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// sum(dim={1}): reduce along second dimension
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at::Tensor t = at::ones({3, 4}, at::kFloat);
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at::Tensor result = t.sum(at::IntArrayRef{1}, /*keepdim=*/false);
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ASSERT_EQ(result.dim(), 1);
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ASSERT_EQ(result.sizes(), c10::IntArrayRef({3}));
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for (int64_t i = 0; i < 3; ++i) {
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ASSERT_FLOAT_EQ(result[i].item<float>(), 4.0f);
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}
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}
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TEST(TensorSumTest, SumAlongDimKeepDim) {
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// sum(dim, keepdim=true): result keeps reduced dimension as size 1
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at::Tensor t = at::ones({3, 4}, at::kFloat);
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at::Tensor result = t.sum(at::IntArrayRef{1}, /*keepdim=*/true);
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ASSERT_EQ(result.dim(), 2);
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ASSERT_EQ(result.sizes(), c10::IntArrayRef({3, 1}));
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for (int64_t i = 0; i < 3; ++i) {
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ASSERT_FLOAT_EQ(result[i][0].item<float>(), 4.0f);
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}
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}
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TEST(TensorSumTest, SumAlongDimWithDtypeCast) {
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// sum(dim, keepdim, dtype): reduce and cast to specified dtype
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at::Tensor t = at::ones({2, 5}, at::kFloat);
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at::Tensor result =
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t.sum(at::IntArrayRef{0}, /*keepdim=*/false, /*dtype=*/at::kDouble);
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ASSERT_EQ(result.scalar_type(), at::kDouble);
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ASSERT_EQ(result.sizes(), c10::IntArrayRef({5}));
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for (int64_t i = 0; i < 5; ++i) {
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ASSERT_DOUBLE_EQ(result[i].item<double>(), 2.0);
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}
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}
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TEST(TensorSumTest, SumPreservesNumel) {
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// Verify that sum of known values is correct
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at::Tensor t = at::arange(6, at::kFloat).reshape({2, 3});
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// t = [[0,1,2],[3,4,5]], total = 15
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at::Tensor result = t.sum();
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ASSERT_FLOAT_EQ(result.item<float>(), 15.0f);
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}
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