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test(cholesky): align with Paddle compat cholesky behavior
新增 CholeskyTest 跨框架对比测试，覆盖: - Shape: 2x2, 3x3, 8x8, batch 2x3x3 - Dtype: kFloat, kDouble - API 变体: at::cholesky, Tensor::cholesky, upper=true/false - 异常: 非正定矩阵 更新 tensor_body.md 中 cholesky 状态为已对齐。 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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‎doc/ATen/core/tensor_body.md‎

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| `ccol_indices` | ⏳ | ⏳ | P2 | |
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| `ceil` | ⏳ | ⏳ | P2 | |
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| `chalf` | ⏳ | ⏳ | P2 | |
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| `cholesky` | ⏳ | ⏳ | P2 | |
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| `cholesky` | ✅ | ✅ | P2 | Tensor 方法与 at::cholesky 均已实现 |
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| `cholesky_inverse` | ⏳ | ⏳ | P2 | |
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| `cholesky_solve` | ⏳ | ⏳ | P2 | |
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| `clamp` | ✅ | ✅ | P2 | 已实现 Scalar 和 Tensor 版本 |

‎test/ATen/ops/CholeskyTest.cpp‎

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#include <ATen/ATen.h>
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#include <ATen/core/Tensor.h>
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#include <ATen/ops/cholesky.h>
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#include <gtest/gtest.h>
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#include <cmath>
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#include <limits>
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#include <string>
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#include <vector>
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#include "src/file_manager.h"
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extern paddle_api_test::ThreadSafeParam g_custom_param;
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namespace at {
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namespace test {
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using paddle_api_test::FileManerger;
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using paddle_api_test::ThreadSafeParam;
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// 使用 stride 按逻辑行优先顺序写出 tensor 元素，兼容不同内存布局
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static void write_cholesky_result_to_file(FileManerger* file,
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const at::Tensor& result) {
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*file << std::to_string(result.dim()) << " ";
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*file << std::to_string(result.numel()) << " ";
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for (int64_t i = 0; i < result.dim(); ++i) {
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*file << std::to_string(result.sizes()[i]) << " ";
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}
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// 使用 stride 按逻辑顺序访问元素
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int64_t ndim = result.dim();
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std::vector<int64_t> strides(ndim);
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for (int64_t i = 0; i < ndim; ++i) {
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strides[i] = result.stride(i);
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}
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switch (result.scalar_type()) {
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case at::kFloat: {
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float* data = result.data_ptr<float>();
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if (ndim == 2) {
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for (int64_t i = 0; i < result.size(0); ++i) {
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for (int64_t j = 0; j < result.size(1); ++j) {
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*file << std::to_string(data[i * strides[0] + j * strides[1]])
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<< " ";
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}
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}
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} else if (ndim == 3) {
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for (int64_t b = 0; b < result.size(0); ++b) {
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for (int64_t i = 0; i < result.size(1); ++i) {
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for (int64_t j = 0; j < result.size(2); ++j) {
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*file
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<< std::to_string(
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data[b * strides[0] + i * strides[1] + j * strides[2]])
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<< " ";
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}
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}
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}
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}
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break;
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}
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case at::kDouble: {
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double* data = result.data_ptr<double>();
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if (ndim == 2) {
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for (int64_t i = 0; i < result.size(0); ++i) {
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for (int64_t j = 0; j < result.size(1); ++j) {
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*file << std::to_string(data[i * strides[0] + j * strides[1]])
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<< " ";
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}
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}
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} else if (ndim == 3) {
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for (int64_t b = 0; b < result.size(0); ++b) {
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for (int64_t i = 0; i < result.size(1); ++i) {
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for (int64_t j = 0; j < result.size(2); ++j) {
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*file
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<< std::to_string(
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data[b * strides[0] + i * strides[1] + j * strides[2]])
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<< " ";
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}
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}
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}
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}
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break;
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}
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default: {
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*file << "unsupported_dtype ";
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break;
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}
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}
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}
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// 构建对角占优的对称正定矩阵
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static at::Tensor make_spd_matrix(const std::vector<int64_t>& shape,
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at::ScalarType dtype) {
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at::Tensor A = at::zeros(shape, dtype);
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int64_t n = shape[shape.size() - 1];
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int64_t m = shape[shape.size() - 2];
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int64_t batch = 1;
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for (size_t i = 0; i + 2 < shape.size(); ++i) {
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batch *= shape[i];
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}
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if (dtype == at::kFloat) {
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float* data = A.data_ptr<float>();
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for (int64_t b = 0; b < batch; ++b) {
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for (int64_t i = 0; i < m; ++i) {
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for (int64_t j = 0; j < n; ++j) {
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int64_t idx = b * m * n + i * n + j;
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if (i == j) {
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data[idx] = static_cast<float>(n);
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} else {
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data[idx] = 0.5f;
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}
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}
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}
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}
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} else if (dtype == at::kDouble) {
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double* data = A.data_ptr<double>();
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for (int64_t b = 0; b < batch; ++b) {
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for (int64_t i = 0; i < m; ++i) {
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for (int64_t j = 0; j < n; ++j) {
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int64_t idx = b * m * n + i * n + j;
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if (i == j) {
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data[idx] = static_cast<double>(n);
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} else {
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data[idx] = 0.5;
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}
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}
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}
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}
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}
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return A;
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}
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class CholeskyTest : public ::testing::Test {
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protected:
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void SetUp() override {}
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};
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// ========== 基础功能 ==========
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TEST_F(CholeskyTest, BasicCholesky) {
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auto file_name = g_custom_param.get();
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FileManerger file(file_name);
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file.createFile();
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file << "BasicCholesky ";
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at::Tensor A = make_spd_matrix({3, 3}, at::kFloat);
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at::Tensor result = at::cholesky(A);
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write_cholesky_result_to_file(&file, result);
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file << "\n";
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file.saveFile();
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}
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TEST_F(CholeskyTest, UpperTrue) {
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auto file_name = g_custom_param.get();
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FileManerger file(file_name);
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file.openAppend();
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file << "UpperTrue ";
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at::Tensor A = make_spd_matrix({3, 3}, at::kFloat);
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at::Tensor result = at::cholesky(A, /*upper=*/true);
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write_cholesky_result_to_file(&file, result);
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file << "\n";
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file.saveFile();
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}
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// ========== Shape 覆盖 ==========
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// 小矩阵 {2, 2}
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TEST_F(CholeskyTest, SmallMatrix) {
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auto file_name = g_custom_param.get();
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FileManerger file(file_name);
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file.openAppend();
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file << "SmallMatrix ";
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at::Tensor A = make_spd_matrix({2, 2}, at::kFloat);
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at::Tensor result = at::cholesky(A);
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write_cholesky_result_to_file(&file, result);
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file << "\n";
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file.saveFile();
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}
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// 大矩阵 {8, 8}
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TEST_F(CholeskyTest, LargeMatrix) {
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auto file_name = g_custom_param.get();
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FileManerger file(file_name);
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file.openAppend();
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file << "LargeMatrix ";
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at::Tensor A = make_spd_matrix({8, 8}, at::kFloat);
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at::Tensor result = at::cholesky(A);
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write_cholesky_result_to_file(&file, result);
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file << "\n";
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file.saveFile();
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}
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// batch 矩阵 {2, 3, 3}
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TEST_F(CholeskyTest, BatchMatrix) {
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auto file_name = g_custom_param.get();
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FileManerger file(file_name);
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file.openAppend();
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file << "BatchMatrix ";
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at::Tensor A = make_spd_matrix({2, 3, 3}, at::kFloat);
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at::Tensor result = at::cholesky(A);
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write_cholesky_result_to_file(&file, result);
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file << "\n";
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file.saveFile();
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}
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// ========== Dtype 覆盖 ==========
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// float64
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TEST_F(CholeskyTest, Float64Dtype) {
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auto file_name = g_custom_param.get();
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FileManerger file(file_name);
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file.openAppend();
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file << "Float64Dtype ";
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at::Tensor A = make_spd_matrix({3, 3}, at::kDouble);
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at::Tensor result = at::cholesky(A);
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write_cholesky_result_to_file(&file, result);
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file << "\n";
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file.saveFile();
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}
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// ========== API 变体 ==========
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// 方法调用 t.cholesky()
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TEST_F(CholeskyTest, MethodCholesky) {
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auto file_name = g_custom_param.get();
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FileManerger file(file_name);
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file.openAppend();
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file << "MethodCholesky ";
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at::Tensor A = make_spd_matrix({3, 3}, at::kFloat);
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at::Tensor result = A.cholesky();
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write_cholesky_result_to_file(&file, result);
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file << "\n";
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file.saveFile();
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}
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// 方法调用 t.cholesky(true)
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TEST_F(CholeskyTest, MethodCholeskyUpper) {
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auto file_name = g_custom_param.get();
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FileManerger file(file_name);
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file.openAppend();
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file << "MethodCholeskyUpper ";
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at::Tensor A = make_spd_matrix({3, 3}, at::kFloat);
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at::Tensor result = A.cholesky(/*upper=*/true);
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write_cholesky_result_to_file(&file, result);
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file << "\n";
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file.saveFile();
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}
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// ========== 异常测试 ==========
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// 非正定矩阵应抛出异常
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TEST_F(CholeskyTest, NonPositiveDefinite) {
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auto file_name = g_custom_param.get();
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FileManerger file(file_name);
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file.openAppend();
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file << "NonPositiveDefinite ";
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at::Tensor A = at::zeros({3, 3}, at::kFloat);
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try {
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at::Tensor result = at::cholesky(A);
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write_cholesky_result_to_file(&file, result);
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} catch (const std::exception&) {
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file << "exception ";
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}
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file << "\n";
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file.saveFile();
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}
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} // namespace test
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} // namespace at

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