mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-21 05:34:18 +08:00
Make PyTorch code-base clang-tidy compliant (#56892)
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
This commit is contained in:
Nikita Shulga
committed by
Facebook GitHub Bot
Facebook GitHub Bot
parent
5a10ee71d6
commit
4cb534f92e
@ -29,6 +29,7 @@ bool almost_equal(at::Tensor left, T right, double tolerance = 1e-4) {
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ASSERT_EQ(tensor.dtype(), (type_)); \
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ASSERT_TRUE(tensor.layout() == (layout_))
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, ToDtype) {
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auto tensor = at::empty({3, 4});
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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@ -52,6 +53,7 @@ TEST(TensorTest, ToDtype) {
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kDouble, at::kStrided);
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, ToTensorAndTensorAttributes) {
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auto tensor = at::empty({3, 4});
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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@ -87,6 +89,7 @@ TEST(TensorTest, ToTensorAndTensorAttributes) {
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// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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// }
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, ToOptionsWithRequiresGrad) {
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{
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// Respects requires_grad
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@ -97,6 +100,7 @@ TEST(TensorTest, ToOptionsWithRequiresGrad) {
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ASSERT_TRUE(tensor.requires_grad());
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// Throws if requires_grad is set in TensorOptions
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto)
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ASSERT_THROW(
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tensor.to(at::TensorOptions().requires_grad(true)), c10::Error);
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@ -113,6 +117,7 @@ TEST(TensorTest, ToOptionsWithRequiresGrad) {
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ASSERT_FALSE(tensor.requires_grad());
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// Throws if requires_grad is set in TensorOptions
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto)
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ASSERT_THROW(
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tensor.to(at::TensorOptions().requires_grad(true)), c10::Error);
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@ -122,6 +127,7 @@ TEST(TensorTest, ToOptionsWithRequiresGrad) {
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}
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, ToDoesNotCopyWhenOptionsAreAllTheSame) {
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{
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auto tensor = at::empty({3, 4}, at::kFloat);
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@ -150,48 +156,58 @@ TEST(TensorTest, ToDoesNotCopyWhenOptionsAreAllTheSame) {
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}
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, AtTensorCtorScalar) {
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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auto tensor = at::tensor(123);
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kInt);
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ASSERT_EQ(tensor[0].item<int32_t>(), 123);
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor(123.456f);
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kFloat);
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ASSERT_TRUE(almost_equal(tensor[0], 123.456f));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor(123.456);
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kDouble);
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ASSERT_TRUE(almost_equal(tensor[0], 123.456));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor(123, at::dtype(at::kFloat)) + 0.5;
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kFloat);
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ASSERT_TRUE(almost_equal(tensor[0], 123.5));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor(c10::complex<float>(1.0, 2.0)) + 0.5;
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kComplexFloat);
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ASSERT_TRUE(almost_equal(tensor[0], c10::complex<float>(1.5, 2.0)));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor(c10::complex<float>(1.0, 2.0), at::dtype(at::kComplexFloat)) + 0.5;
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kComplexFloat);
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ASSERT_TRUE(almost_equal(tensor[0], c10::complex<float>(1.5, 2.0)));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor(c10::complex<double>(1.0, 2.0)) + 0.5;
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kComplexDouble);
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ASSERT_TRUE(almost_equal(tensor[0], c10::complex<double>(1.5, 2.0)));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor(c10::complex<float>(1.0, 2.0), at::dtype(at::kComplexDouble)) + 0.5;
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kComplexDouble);
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ASSERT_TRUE(almost_equal(tensor[0], c10::complex<double>(1.5, 2.0)));
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, AtTensorCtorSingleDim) {
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auto tensor = at::tensor({1, 2, 3});
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ASSERT_EQ(tensor.numel(), 3);
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@ -207,6 +223,7 @@ TEST(TensorTest, AtTensorCtorSingleDim) {
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ASSERT_TRUE(exactly_equal(tensor[1], 2));
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ASSERT_TRUE(exactly_equal(tensor[2], 3));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor({1.5, 2.25, 3.125});
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kDouble);
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@ -214,6 +231,7 @@ TEST(TensorTest, AtTensorCtorSingleDim) {
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ASSERT_TRUE(almost_equal(tensor[1], 2.25));
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ASSERT_TRUE(almost_equal(tensor[2], 3.125));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor({c10::complex<float>(1.5, 0.15), c10::complex<float>(1.5, 0.15), c10::complex<float>(3.125, 0.3125)});
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kComplexFloat);
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@ -221,6 +239,7 @@ TEST(TensorTest, AtTensorCtorSingleDim) {
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ASSERT_TRUE(almost_equal(tensor[1], c10::complex<float>(1.5, 0.15)));
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ASSERT_TRUE(almost_equal(tensor[2], c10::complex<float>(3.125, 0.3125)));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor({c10::complex<double>(1.5, 0.15), c10::complex<double>(1.5, 0.15), c10::complex<double>(3.125, 0.3125)});
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kComplexDouble);
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@ -228,6 +247,7 @@ TEST(TensorTest, AtTensorCtorSingleDim) {
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ASSERT_TRUE(almost_equal(tensor[1], c10::complex<double>(1.5, 0.15)));
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ASSERT_TRUE(almost_equal(tensor[2], c10::complex<double>(3.125, 0.3125)));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor({1.1, 2.2, 3.3}, at::dtype(at::kInt));
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kInt);
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@ -236,6 +256,7 @@ TEST(TensorTest, AtTensorCtorSingleDim) {
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ASSERT_TRUE(exactly_equal(tensor[1], 2));
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ASSERT_TRUE(exactly_equal(tensor[2], 3));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor(std::vector<double>({1.5, 2.25, 3.125}));
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kDouble);
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@ -243,6 +264,7 @@ TEST(TensorTest, AtTensorCtorSingleDim) {
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ASSERT_TRUE(almost_equal(tensor[1], 2.25));
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ASSERT_TRUE(almost_equal(tensor[2], 3.125));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor(std::vector<c10::complex<float>>({c10::complex<float>(1.5, 0.15), c10::complex<float>(1.5, 0.15), c10::complex<float>(3.125, 0.3125)}));
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kComplexFloat);
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@ -250,6 +272,7 @@ TEST(TensorTest, AtTensorCtorSingleDim) {
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ASSERT_TRUE(almost_equal(tensor[1], c10::complex<float>(1.5, 0.15)));
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ASSERT_TRUE(almost_equal(tensor[2], c10::complex<float>(3.125, 0.3125)));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor(std::vector<c10::complex<double>>({c10::complex<double>(1.5, 0.15), c10::complex<double>(1.5, 0.15), c10::complex<double>(3.125, 0.3125)}));
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kComplexDouble);
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@ -257,6 +280,7 @@ TEST(TensorTest, AtTensorCtorSingleDim) {
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ASSERT_TRUE(almost_equal(tensor[1], c10::complex<double>(1.5, 0.15)));
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ASSERT_TRUE(almost_equal(tensor[2], c10::complex<double>(3.125, 0.3125)));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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std::vector<int> v = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
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tensor = at::tensor(v);
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ASSERT_EQ(tensor.numel(), v.size());
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@ -265,6 +289,7 @@ TEST(TensorTest, AtTensorCtorSingleDim) {
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ASSERT_TRUE(exactly_equal(tensor[i], v.at(i)));
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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std::vector<double> w = {1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.0};
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tensor = at::tensor(w);
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ASSERT_EQ(tensor.numel(), w.size());
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@ -274,7 +299,9 @@ TEST(TensorTest, AtTensorCtorSingleDim) {
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}
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std::vector<c10::complex<double>> x = {
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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{1.1, -1.1}, {2.2, -2.2}, {3.3, -3.3}, {4.4, -4.4}, {5.5, -5.5},
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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{6.6, -6.6}, {7.7, -7.7}, {8.8, -8.8}, {9.9, -9.9}, {10.0, -10.0}
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};
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tensor = at::tensor(x);
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@ -285,7 +312,9 @@ TEST(TensorTest, AtTensorCtorSingleDim) {
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}
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, AtTensorCastRealToComplex) {
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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auto tensor = at::tensor(std::vector<double>({1.5, 2.5, 3.5}), at::kComplexDouble);
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kComplexDouble);
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@ -293,6 +322,7 @@ TEST(TensorTest, AtTensorCastRealToComplex) {
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ASSERT_TRUE(almost_equal(tensor[1], c10::complex<double>(2.5)));
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ASSERT_TRUE(almost_equal(tensor[2], c10::complex<double>(3.5)));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor({1.5, 2.5, 3.5}, at::kComplexDouble);
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kComplexDouble);
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@ -300,28 +330,35 @@ TEST(TensorTest, AtTensorCastRealToComplex) {
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ASSERT_TRUE(almost_equal(tensor[1], c10::complex<double>(2.5)));
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ASSERT_TRUE(almost_equal(tensor[2], c10::complex<double>(3.5)));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = at::tensor(1.5, at::kComplexDouble);
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kComplexDouble);
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ASSERT_TRUE(almost_equal(tensor[0], c10::complex<double>(1.5)));
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, AtTensorCastComplexToRealErrorChecks) {
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{
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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ASSERT_THROWS_WITH(at::tensor(c10::complex<float>(0.1, 0.2), at::kFloat),
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"\"tensor_cpu\" not implemented for 'Float'");
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}
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{
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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ASSERT_THROWS_WITH(at::tensor({c10::complex<float>(0.1, 0.2)}, at::kFloat),
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"\"tensor_cpu\" not implemented for 'Float'");
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}
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{
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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ASSERT_THROWS_WITH(at::tensor(std::vector<c10::complex<float>>{c10::complex<float>(0.1, 0.2)}, at::kFloat),
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"\"tensor_cpu\" not implemented for 'Float'");
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}
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, TorchTensorCtorScalarIntegralType) {
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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auto tensor = torch::tensor(123);
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({}));
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@ -332,18 +369,21 @@ TEST(TensorTest, TorchTensorCtorScalarIntegralType) {
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void test_TorchTensorCtorScalarFloatingType_expected_dtype(c10::ScalarType default_dtype) {
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AutoDefaultDtypeMode dtype_mode(default_dtype);
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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auto tensor = torch::tensor(123.456f);
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({}));
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ASSERT_EQ(tensor.dtype(), default_dtype);
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ASSERT_TRUE(almost_equal(tensor, 123.456f));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = torch::tensor(123.456);
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({}));
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ASSERT_EQ(tensor.dtype(), default_dtype);
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ASSERT_TRUE(almost_equal(tensor, 123.456));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = torch::tensor({123.456});
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1}));
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@ -351,11 +391,13 @@ void test_TorchTensorCtorScalarFloatingType_expected_dtype(c10::ScalarType defau
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ASSERT_TRUE(almost_equal(tensor[0], 123.456));
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, TorchTensorCtorScalarFloatingType) {
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test_TorchTensorCtorScalarFloatingType_expected_dtype(/*default_dtype=*/torch::kFloat);
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test_TorchTensorCtorScalarFloatingType_expected_dtype(/*default_dtype=*/torch::kDouble);
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, TorchTensorCtorScalarBoolType) {
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auto tensor = torch::tensor(true);
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ASSERT_EQ(tensor.numel(), 1);
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@ -370,6 +412,7 @@ TEST(TensorTest, TorchTensorCtorScalarBoolType) {
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ASSERT_TRUE(exactly_equal(tensor[0], true));
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorTest, TorchTensorCtorSingleDimIntegralType) {
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auto tensor = torch::tensor({1, 2, 3});
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ASSERT_EQ(tensor.numel(), 3);
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@ -415,6 +458,7 @@ TEST(TensorTest, TorchTensorCtorSingleDimIntegralType) {
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void test_TorchTensorCtorSingleDimFloatingType_expected_dtype(c10::ScalarType default_dtype) {
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AutoDefaultDtypeMode dtype_mode(default_dtype);
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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auto tensor = torch::tensor({1.5, 2.25, 3.125});
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({3}));
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@ -423,6 +467,7 @@ void test_TorchTensorCtorSingleDimFloatingType_expected_dtype(c10::ScalarType de
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ASSERT_TRUE(almost_equal(tensor[1], 2.25));
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ASSERT_TRUE(almost_equal(tensor[2], 3.125));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = torch::tensor({1.5f, 2.25f, 3.125f});
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({3}));
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@ -431,6 +476,7 @@ void test_TorchTensorCtorSingleDimFloatingType_expected_dtype(c10::ScalarType de
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ASSERT_TRUE(almost_equal(tensor[1], 2.25f));
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ASSERT_TRUE(almost_equal(tensor[2], 3.125f));
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
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tensor = torch::tensor(at::ArrayRef<float>({1.5f, 2.25f, 3.125f}));
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), default_dtype);
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||||
@ -438,6 +484,7 @@ void test_TorchTensorCtorSingleDimFloatingType_expected_dtype(c10::ScalarType de
|
||||
ASSERT_TRUE(almost_equal(tensor[1], 2.25));
|
||||
ASSERT_TRUE(almost_equal(tensor[2], 3.125));
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
tensor = torch::tensor(std::vector<float>({1.5f, 2.25f, 3.125f}));
|
||||
ASSERT_EQ(tensor.numel(), 3);
|
||||
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({3}));
|
||||
@ -446,6 +493,7 @@ void test_TorchTensorCtorSingleDimFloatingType_expected_dtype(c10::ScalarType de
|
||||
ASSERT_TRUE(almost_equal(tensor[1], 2.25));
|
||||
ASSERT_TRUE(almost_equal(tensor[2], 3.125));
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
tensor = torch::tensor(at::ArrayRef<double>({1.5, 2.25, 3.125}));
|
||||
ASSERT_EQ(tensor.numel(), 3);
|
||||
ASSERT_EQ(tensor.dtype(), default_dtype);
|
||||
@ -453,6 +501,7 @@ void test_TorchTensorCtorSingleDimFloatingType_expected_dtype(c10::ScalarType de
|
||||
ASSERT_TRUE(almost_equal(tensor[1], 2.25));
|
||||
ASSERT_TRUE(almost_equal(tensor[2], 3.125));
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
tensor = torch::tensor(std::vector<double>({1.5, 2.25, 3.125}));
|
||||
ASSERT_EQ(tensor.numel(), 3);
|
||||
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({3}));
|
||||
@ -462,11 +511,13 @@ void test_TorchTensorCtorSingleDimFloatingType_expected_dtype(c10::ScalarType de
|
||||
ASSERT_TRUE(almost_equal(tensor[2], 3.125));
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCtorSingleDimFloatingType) {
|
||||
test_TorchTensorCtorSingleDimFloatingType_expected_dtype(/*default_dtype=*/torch::kFloat);
|
||||
test_TorchTensorCtorSingleDimFloatingType_expected_dtype(/*default_dtype=*/torch::kDouble);
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCtorSingleDimBoolType) {
|
||||
auto tensor = torch::tensor({true, false, true});
|
||||
ASSERT_EQ(tensor.numel(), 3);
|
||||
@ -485,6 +536,7 @@ TEST(TensorTest, TorchTensorCtorSingleDimBoolType) {
|
||||
ASSERT_TRUE(exactly_equal(tensor[2], true));
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCtorMultiDimIntegralType) {
|
||||
{
|
||||
auto tensor = torch::tensor({{1, 2}});
|
||||
@ -540,6 +592,7 @@ TEST(TensorTest, TorchTensorCtorMultiDimIntegralType) {
|
||||
void test_TorchTensorCtorMultiDimFloatingType_expected_dtype(c10::ScalarType default_dtype) {
|
||||
AutoDefaultDtypeMode dtype_mode(default_dtype);
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto tensor = torch::tensor({{1.0, 2.0}});
|
||||
ASSERT_EQ(tensor.dtype(), default_dtype);
|
||||
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 2}));
|
||||
@ -547,6 +600,7 @@ void test_TorchTensorCtorMultiDimFloatingType_expected_dtype(c10::ScalarType def
|
||||
ASSERT_FALSE(tensor.requires_grad());
|
||||
}
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto tensor = torch::tensor({{{{{{{{1.0, 2.0, 3.0}}}}}, {{{{{4.0, 5.0, 6.0}}}}}, {{{{{7.0, 8.0, 9.0}}}}}}}});
|
||||
ASSERT_EQ(tensor.dtype(), default_dtype);
|
||||
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 1, 3, 1, 1, 1, 1, 3}));
|
||||
@ -555,11 +609,13 @@ void test_TorchTensorCtorMultiDimFloatingType_expected_dtype(c10::ScalarType def
|
||||
}
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCtorMultiDimFloatingType) {
|
||||
test_TorchTensorCtorMultiDimFloatingType_expected_dtype(/*default_dtype=*/torch::kFloat);
|
||||
test_TorchTensorCtorMultiDimFloatingType_expected_dtype(/*default_dtype=*/torch::kDouble);
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCtorMultiDimBoolType) {
|
||||
{
|
||||
auto tensor = torch::tensor({{true, false}});
|
||||
@ -583,6 +639,7 @@ TEST(TensorTest, TorchTensorCtorMultiDimBoolType) {
|
||||
}
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCtorMultiDimWithOptions) {
|
||||
{
|
||||
auto tensor = torch::tensor({{1, 2}}, torch::dtype(torch::kInt));
|
||||
@ -600,16 +657,19 @@ TEST(TensorTest, TorchTensorCtorMultiDimWithOptions) {
|
||||
}
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCtorMultiDimErrorChecks) {
|
||||
{
|
||||
ASSERT_THROWS_WITH(torch::tensor({{{2, 3, 4}, {{5, 6}, {7}}}}),
|
||||
"Expected all sub-lists to have sizes: 2 (e.g. {5, 6}), but got sub-list {7} with sizes: 1");
|
||||
}
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
ASSERT_THROWS_WITH(torch::tensor({{{1, 2.0}, {1, 2.0}}}),
|
||||
"Expected all elements of the tensor to have the same scalar type: Int, but got element of scalar type: Double");
|
||||
}
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
ASSERT_THROWS_WITH(torch::tensor({{{true, 2.0, 3}, {true, 2.0, 3}}}),
|
||||
"Expected all elements of the tensor to have the same scalar type: Bool, but got element of scalar type: Double");
|
||||
}
|
||||
@ -623,7 +683,9 @@ TEST(TensorTest, TorchTensorCtorMultiDimErrorChecks) {
|
||||
}
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCastRealToComplex) {
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto tensor = torch::tensor(std::vector<double>({1.5, 2.5, 3.5}), torch::kComplexDouble);
|
||||
ASSERT_EQ(tensor.numel(), 3);
|
||||
ASSERT_EQ(tensor.dtype(), torch::kComplexDouble);
|
||||
@ -631,6 +693,7 @@ TEST(TensorTest, TorchTensorCastRealToComplex) {
|
||||
ASSERT_TRUE(almost_equal(tensor[1], c10::complex<double>(2.5)));
|
||||
ASSERT_TRUE(almost_equal(tensor[2], c10::complex<double>(3.5)));
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
tensor = torch::tensor({1.5, 2.5, 3.5}, torch::kComplexDouble);
|
||||
ASSERT_EQ(tensor.numel(), 3);
|
||||
ASSERT_EQ(tensor.dtype(), torch::kComplexDouble);
|
||||
@ -638,22 +701,27 @@ TEST(TensorTest, TorchTensorCastRealToComplex) {
|
||||
ASSERT_TRUE(almost_equal(tensor[1], c10::complex<double>(2.5)));
|
||||
ASSERT_TRUE(almost_equal(tensor[2], c10::complex<double>(3.5)));
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
tensor = torch::tensor(1.5, torch::kComplexDouble);
|
||||
ASSERT_EQ(tensor.numel(), 1);
|
||||
ASSERT_EQ(tensor.dtype(), torch::kComplexDouble);
|
||||
ASSERT_TRUE(almost_equal(tensor, c10::complex<double>(1.5)));
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCastComplexToRealErrorChecks) {
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
ASSERT_THROWS_WITH(torch::tensor(c10::complex<float>(0.1, 0.2), torch::kFloat),
|
||||
"value cannot be converted to type float without overflow");
|
||||
}
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
ASSERT_THROWS_WITH(torch::tensor({c10::complex<float>(0.1, 0.2), c10::complex<float>(0.3, 0.4)}, torch::kFloat),
|
||||
"value cannot be converted to type float without overflow");
|
||||
}
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
ASSERT_THROWS_WITH(torch::tensor(std::vector<c10::complex<float>>{c10::complex<float>(0.1, 0.2), c10::complex<float>(0.3, 0.4)}, torch::kFloat),
|
||||
"can not do torch::tensor(complex, dtype=non-complex) because complex can not be casted to real number without loss of information");
|
||||
}
|
||||
@ -663,6 +731,7 @@ void test_TorchTensorCtorMultiDim_CUDA_expected_dtype(c10::ScalarType default_dt
|
||||
AutoDefaultDtypeMode dtype_mode(default_dtype);
|
||||
|
||||
auto tensor = torch::tensor(
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
{{{{{{{{1.0, 2.0, 3.0}}}}}, {{{{{4.0, 5.0, 6.0}}}}}, {{{{{7.0, 8.0, 9.0}}}}}}}},
|
||||
torch::dtype(default_dtype).device(torch::kCUDA));
|
||||
ASSERT_TRUE(tensor.device().is_cuda());
|
||||
@ -674,6 +743,7 @@ void test_TorchTensorCtorMultiDim_CUDA_expected_dtype(c10::ScalarType default_dt
|
||||
ASSERT_FALSE(tensor.requires_grad());
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCtorMultiDim_CUDA) {
|
||||
test_TorchTensorCtorMultiDim_CUDA_expected_dtype(/*default_dtype=*/torch::kFloat);
|
||||
test_TorchTensorCtorMultiDim_CUDA_expected_dtype(/*default_dtype=*/torch::kDouble);
|
||||
@ -732,6 +802,7 @@ void test_TorchTensorCtorZeroSizedDim_expected_dtype(c10::ScalarType default_dty
|
||||
}
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCtorZeroSizedDim) {
|
||||
test_TorchTensorCtorZeroSizedDim_expected_dtype(/*default_dtype=*/torch::kFloat);
|
||||
test_TorchTensorCtorZeroSizedDim_expected_dtype(/*default_dtype=*/torch::kDouble);
|
||||
@ -746,6 +817,7 @@ void test_TorchTensorCtorWithoutSpecifyingDtype_expected_dtype(c10::ScalarType d
|
||||
ASSERT_EQ(torch::tensor({{1., 2., 3.}}, torch::TensorOptions()).dtype(), default_dtype);
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCtorWithoutSpecifyingDtype) {
|
||||
ASSERT_EQ(torch::tensor({1, 2, 3}).dtype(), torch::kLong);
|
||||
ASSERT_EQ(torch::tensor({{1, 2, 3}}).dtype(), torch::kLong);
|
||||
@ -772,6 +844,7 @@ void test_TorchTensorCtorWithNonDtypeOptions_expected_dtype(c10::ScalarType defa
|
||||
ASSERT_EQ(torch::tensor(std::vector<float>({1.f, 2.f, 3.f}), torch::TensorOptions()).dtype(), default_dtype);
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TorchTensorCtorWithNonDtypeOptions) {
|
||||
test_TorchTensorCtorWithNonDtypeOptions_expected_dtype(/*default_dtype=*/torch::kFloat);
|
||||
test_TorchTensorCtorWithNonDtypeOptions_expected_dtype(/*default_dtype=*/torch::kDouble);
|
||||
@ -783,8 +856,10 @@ void test_Arange_expected_dtype(c10::ScalarType default_dtype) {
|
||||
ASSERT_EQ(torch::arange(0., 5).dtype(), default_dtype);
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, Arange) {
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto x = torch::arange(0, 5);
|
||||
ASSERT_EQ(x.dtype(), torch::kLong);
|
||||
}
|
||||
@ -792,6 +867,7 @@ TEST(TensorTest, Arange) {
|
||||
test_Arange_expected_dtype(torch::kDouble);
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, PrettyPrintTensorDataContainer) {
|
||||
{
|
||||
ASSERT_EQ(
|
||||
@ -840,34 +916,43 @@ TEST(TensorTest, PrettyPrintTensorDataContainer) {
|
||||
}
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, TensorDataContainerCallingAccessorOfWrongType) {
|
||||
{
|
||||
ASSERT_THROWS_WITH(
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
torch::detail::TensorDataContainer(1.1).init_list(),
|
||||
"Can only call `init_list()` on a TensorDataContainer that has `is_init_list() == true`");
|
||||
ASSERT_THROWS_WITH(
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
torch::detail::TensorDataContainer(1.1).tensor(),
|
||||
"Can only call `tensor()` on a TensorDataContainer that has `is_tensor() == true`");
|
||||
}
|
||||
{
|
||||
ASSERT_THROWS_WITH(
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
torch::detail::TensorDataContainer({1.1, 2.2}).scalar(),
|
||||
"Can only call `scalar()` on a TensorDataContainer that has `is_scalar() == true`");
|
||||
ASSERT_THROWS_WITH(
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
torch::detail::TensorDataContainer({1.1, 2.2}).tensor(),
|
||||
"Can only call `tensor()` on a TensorDataContainer that has `is_tensor() == true`");
|
||||
}
|
||||
{
|
||||
ASSERT_THROWS_WITH(
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
torch::detail::TensorDataContainer(at::ArrayRef<double>({1.1, 2.2})).scalar(),
|
||||
"Can only call `scalar()` on a TensorDataContainer that has `is_scalar() == true`");
|
||||
ASSERT_THROWS_WITH(
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
torch::detail::TensorDataContainer(at::ArrayRef<double>({1.1, 2.2})).init_list(),
|
||||
"Can only call `init_list()` on a TensorDataContainer that has `is_init_list() == true`");
|
||||
}
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, FromBlob) {
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
std::vector<double> v = {1.0, 2.0, 3.0};
|
||||
auto tensor = torch::from_blob(
|
||||
v.data(), v.size(), torch::dtype(torch::kFloat64).requires_grad(true));
|
||||
@ -881,6 +966,7 @@ TEST(TensorTest, FromBlob) {
|
||||
ASSERT_EQ(tensor.storage().data_ptr().get_context(), nullptr);
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, FromBlobUsesDeleter) {
|
||||
bool called = false;
|
||||
{
|
||||
@ -894,11 +980,14 @@ TEST(TensorTest, FromBlobUsesDeleter) {
|
||||
ASSERT_TRUE(called);
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, FromBlobWithStrides) {
|
||||
// clang-format off
|
||||
std::vector<int32_t> v = {
|
||||
1, 2, 3,
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
4, 5, 6,
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
7, 8, 9
|
||||
};
|
||||
// clang-format on
|
||||
@ -919,32 +1008,39 @@ TEST(TensorTest, FromBlobWithStrides) {
|
||||
}
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, Item) {
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
torch::Tensor tensor = torch::tensor(3.14);
|
||||
torch::Scalar scalar = tensor.item();
|
||||
ASSERT_NEAR(scalar.to<float>(), 3.14, 1e-5);
|
||||
}
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
torch::Tensor tensor = torch::tensor(123);
|
||||
torch::Scalar scalar = tensor.item();
|
||||
ASSERT_EQ(scalar.to<int>(), 123);
|
||||
}
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, Item_CUDA) {
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
torch::Tensor tensor = torch::tensor(3.14, torch::kCUDA);
|
||||
torch::Scalar scalar = tensor.item();
|
||||
ASSERT_NEAR(scalar.to<float>(), 3.14, 1e-5);
|
||||
}
|
||||
{
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
torch::Tensor tensor = torch::tensor(123, torch::kCUDA);
|
||||
torch::Scalar scalar = tensor.item();
|
||||
ASSERT_EQ(scalar.to<int>(), 123);
|
||||
}
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, DataPtr) {
|
||||
auto tensor = at::empty({3, 4}, at::kFloat);
|
||||
auto tensor_not_copy = tensor.to(tensor.options());
|
||||
@ -952,18 +1048,22 @@ TEST(TensorTest, DataPtr) {
|
||||
ASSERT_EQ(tensor_not_copy.data_ptr(), tensor.data_ptr());
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, Data) {
|
||||
const auto tensor = torch::rand({3, 3});
|
||||
ASSERT_TRUE(torch::equal(tensor, tensor.data()));
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, BackwardAndGrad) {
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
|
||||
auto y = x * x;
|
||||
y.backward();
|
||||
ASSERT_EQ(x.grad().item<float>(), 10.0);
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, BackwardCreatesOnesGrad) {
|
||||
const auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
|
||||
x.backward();
|
||||
@ -971,27 +1071,34 @@ TEST(TensorTest, BackwardCreatesOnesGrad) {
|
||||
torch::ones_like(x)));
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, BackwardNonScalarOutputs) {
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto x = torch::randn({5, 5}, torch::requires_grad());
|
||||
auto y = x * x;
|
||||
ASSERT_THROWS_WITH(y.backward(),
|
||||
"grad can be implicitly created only for scalar outputs");
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, IsLeaf) {
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
|
||||
auto y = x * x;
|
||||
ASSERT_TRUE(x.is_leaf());
|
||||
ASSERT_FALSE(y.is_leaf());
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, OutputNr) {
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
|
||||
auto y = x * x;
|
||||
ASSERT_EQ(x.output_nr(), 0);
|
||||
ASSERT_EQ(y.output_nr(), 0);
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, Version) {
|
||||
auto x = torch::ones(3);
|
||||
ASSERT_EQ(x._version(), 0);
|
||||
@ -1001,7 +1108,9 @@ TEST(TensorTest, Version) {
|
||||
ASSERT_EQ(x._version(), 2);
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, Detach) {
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
|
||||
auto y = x * x;
|
||||
const auto y_detached = y.detach();
|
||||
@ -1010,7 +1119,9 @@ TEST(TensorTest, Detach) {
|
||||
ASSERT_FALSE(y_detached.requires_grad());
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, DetachInplace) {
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
|
||||
auto y = x * x;
|
||||
auto y_detached = y.detach_();
|
||||
@ -1020,8 +1131,11 @@ TEST(TensorTest, DetachInplace) {
|
||||
ASSERT_FALSE(y_detached.requires_grad());
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, SetData) {
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto x = torch::randn({5});
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto y = torch::randn({5});
|
||||
ASSERT_FALSE(torch::equal(x, y));
|
||||
ASSERT_NE(x.data_ptr<float>(), y.data_ptr<float>());
|
||||
@ -1031,7 +1145,9 @@ TEST(TensorTest, SetData) {
|
||||
ASSERT_EQ(x.data_ptr<float>(), y.data_ptr<float>());
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, RequiresGradInplace) {
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
|
||||
auto x = torch::tensor({5.0});
|
||||
x.requires_grad_(true);
|
||||
ASSERT_TRUE(x.requires_grad());
|
||||
@ -1048,6 +1164,7 @@ TEST(TensorTest, RequiresGradInplace) {
|
||||
"Only Tensors of floating point and complex dtype can require gradients");
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
|
||||
TEST(TensorTest, StdDimension) {
|
||||
// Test that std(0) doesn't select the std(unbiased=False) overload (gh-40287)
|
||||
auto x = torch::randn({4, 3});
|
||||
|
||||
Reference in New Issue
Block a user