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Remove some NOLINT (#146610)

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Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/146610
Approved by: https://github.com/Skylion007, https://github.com/malfet
This commit is contained in:
cyy
2025-02-07 01:50:06 +00:00
committed by PyTorch MergeBot
parent 624d94bdb8
commit fa0592b568
5 changed files with 25 additions and 27 deletions
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2
aten/src/ATen/native/ao_sparse/quantized/cpu/qlinear.cpp
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@ -100,7 +100,7 @@ at::Tensor PackedLinearWeight::apply_impl(
// 2. If the input tensor is {x, batch_size, K}, the output tensor is {x,
// batch_size, out_channels}.
std::vector<int64_t> out_sizes = input.sizes().vec();
out_sizes.back() = out_channels; // NOLINT
out_sizes.back() = out_channels;
// Allocate output Tensor and a buffer for fbgemmPacked to use
auto output_tr = at::_empty_affine_quantized(
out_sizes,

24
aten/src/ATen/test/legacy_vmap_test.cpp
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@ -740,7 +740,7 @@ TEST(VmapTest, TestBatchedTensorExpand) {
TEST(VmapTest, TestBatchedTensorUnsqueeze) {
{
// Basic test
auto tensor = at::randn({2, 3, 5}); // NOLINT
auto tensor = at::randn({2, 3, 5});
auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
auto batched_out = batched.unsqueeze(0);
@ -750,7 +750,7 @@ TEST(VmapTest, TestBatchedTensorUnsqueeze) {
}
{
// Test with multiple levels
auto tensor = at::randn({2, 3, 5}); // NOLINT
auto tensor = at::randn({2, 3, 5});
auto batched = makeBatched(tensor, {{0, 0}, {1, 1}});
auto batched_out = batched.unsqueeze(0);
@ -760,7 +760,7 @@ TEST(VmapTest, TestBatchedTensorUnsqueeze) {
}
{
// Negative dim
auto tensor = at::randn({2, 3, 5}); // NOLINT
auto tensor = at::randn({2, 3, 5});
auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
auto batched_out = batched.unsqueeze(-1);
@ -773,7 +773,7 @@ TEST(VmapTest, TestBatchedTensorUnsqueeze) {
TEST(VmapTest, TestBatchedTensorSqueeze) {
{
// Basic test
auto tensor = at::randn({2, 1, 5}); // NOLINT
auto tensor = at::randn({2, 1, 5});
auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
auto batched_out = batched.squeeze(0);
@ -783,7 +783,7 @@ TEST(VmapTest, TestBatchedTensorSqueeze) {
}
{
// Test with multiple levels
auto tensor = at::randn({2, 3, 1}); // NOLINT
auto tensor = at::randn({2, 3, 1});
auto batched = makeBatched(tensor, {{0, 0}, {1, 1}});
auto batched_out = batched.squeeze(0);
@ -793,7 +793,7 @@ TEST(VmapTest, TestBatchedTensorSqueeze) {
}
{
// Negative dim
auto tensor = at::randn({2, 3, 1}); // NOLINT
auto tensor = at::randn({2, 3, 1});
auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
auto batched_out = batched.squeeze(-1);
@ -806,7 +806,7 @@ TEST(VmapTest, TestBatchedTensorSqueeze) {
TEST(VmapTest, TestBatchedTensorTranspose) {
{
// Basic test
auto tensor = at::randn({2, 3, 5}); // NOLINT
auto tensor = at::randn({2, 3, 5});
auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
auto batched_out = batched.transpose(0, 1);
@ -816,7 +816,7 @@ TEST(VmapTest, TestBatchedTensorTranspose) {
}
{
// Test with multiple levels
auto tensor = at::randn({2, 3, 5, 7, 11}); // NOLINT
auto tensor = at::randn({2, 3, 5, 7, 11});
auto batched = makeBatched(tensor, {{0, 0}, {1, 1}});
auto batched_out = batched.transpose(0, 2);
@ -826,7 +826,7 @@ TEST(VmapTest, TestBatchedTensorTranspose) {
}
{
// Negative dims
auto tensor = at::randn({2, 3, 5, 7}); // NOLINT
auto tensor = at::randn({2, 3, 5, 7});
auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
auto batched_out = batched.mT();
@ -840,7 +840,7 @@ TEST(VmapTest, TestBatchedTensorTranspose) {
TEST(VmapTest, TestBatchedTensorPermute) {
{
// Basic test
auto tensor = at::randn({2, 3, 5}); // NOLINT
auto tensor = at::randn({2, 3, 5});
auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
auto batched_out = batched.permute({1, 0});
@ -850,7 +850,7 @@ TEST(VmapTest, TestBatchedTensorPermute) {
}
{
// Test with multiple levels
auto tensor = at::randn({2, 3, 5, 7, 11}); // NOLINT
auto tensor = at::randn({2, 3, 5, 7, 11});
auto batched = makeBatched(tensor, {{0, 0}, {1, 1}});
auto batched_out = batched.permute({2, 1, 0});
@ -860,7 +860,7 @@ TEST(VmapTest, TestBatchedTensorPermute) {
}
{
// Negative dims
auto tensor = at::randn({2, 3, 5, 7}); // NOLINT
auto tensor = at::randn({2, 3, 5, 7});
auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
auto batched_out = batched.permute({-1, -2, -3});

18
torch/csrc/autograd/python_engine.cpp
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@ -194,15 +194,15 @@ PyObject* THPEngine_run_backward(
unsigned char allow_unreachable = 0;
unsigned char accumulate_grad =
0; // Indicate whether to accumulate grad into leaf Tensors or capture
constexpr const char* accepted_kwargs[] = {// NOLINT
"tensors",
"grad_tensors",
"keep_graph",
"create_graph",
"inputs",
"allow_unreachable",
"accumulate_grad",
nullptr};
constexpr const char* accepted_kwargs[] = {
"tensors",
"grad_tensors",
"keep_graph",
"create_graph",
"inputs",
"allow_unreachable",
"accumulate_grad",
nullptr};
if (!PyArg_ParseTupleAndKeywords(
args,
kwargs,

6
torch/csrc/distributed/c10d/GroupRegistry.cpp
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@ -10,12 +10,10 @@ namespace {
class GroupRegistry {
public:
void register_group(
std::string group_name,
// NOLINTNEXTLINE(performance-unnecessary-value-param)
const std::string& group_name,
c10::intrusive_ptr<c10d::ProcessGroup> group) {
std::unique_lock write_lock(lock_);
auto [_, inserted] =
registry_.try_emplace(std::move(group_name), std::move(group));
auto [_, inserted] = registry_.try_emplace(group_name, std::move(group));
TORCH_CHECK(
inserted,
"A process group is already registered under the name",

2
torch/csrc/distributed/c10d/reducer.cpp
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@ -38,7 +38,7 @@ constexpr int kUnsetDivFactor = -1;
} // namespace
C10_DEFINE_TYPED_REGISTRY( // NOLINT
C10_DEFINE_TYPED_REGISTRY(
TimerRegistry,
c10::DeviceType,
Timer,