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[Lint] Update clang-format to 19.1.4 (#153889)

Browse Source 
All changes other than the one to `tools/linter/adapters/s3_init_config.json` are generated by newer clang-format
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153889
Approved by: https://github.com/cyyever, https://github.com/atalman
This commit is contained in:
Nikita Shulga
2025-05-19 15:36:11 -07:00
committed by PyTorch MergeBot
parent d869ea11e0
commit c4d1ff02f8
59 changed files with 550 additions and 522 deletions
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3
aten/src/ATen/cpu/vec/sve/vec_double.h
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@ -395,8 +395,7 @@ class Vectorized<double> {
})} // Comparison using the _CMP_**_OQ predicate.
// `O`: get false if an operand is NaN
// `Q`: do not raise if an operand is NaN
Vectorized<double>
operator==(const Vectorized<double>& other) const {
Vectorized<double> operator==(const Vectorized<double>& other) const {
svbool_t mask = svcmpeq_f64(ptrue, values, other);
return svsel_f64(mask, ALL_F64_TRUE_MASK, ALL_F64_FALSE_MASK);
}

3
aten/src/ATen/cpu/vec/sve/vec_float.h
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@ -497,8 +497,7 @@ class Vectorized<float> {
})} // Comparison using the _CMP_**_OQ predicate.
// `O`: get false if an operand is NaN
// `Q`: do not raise if an operand is NaN
Vectorized<float>
operator==(const Vectorized<float>& other) const {
Vectorized<float> operator==(const Vectorized<float>& other) const {
svbool_t mask = svcmpeq_f32(ptrue, values, other);
return svsel_f32(mask, ALL_F32_TRUE_MASK, ALL_F32_FALSE_MASK);
}

16
aten/src/ATen/cpu/vec/vec128/vec128_float_neon.h
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@ -97,14 +97,14 @@ class Vectorized<float> {
const Vectorized<float>& a,
const Vectorized<float>& b) {
Vectorized<float> vec;
vec.values =
BlendRegs<0, (mask & 0x01) != 0>::impl(a.values, b.values, vec.values);
vec.values =
BlendRegs<1, (mask & 0x02) != 0>::impl(a.values, b.values, vec.values);
vec.values =
BlendRegs<2, (mask & 0x04) != 0>::impl(a.values, b.values, vec.values);
vec.values =
BlendRegs<3, (mask & 0x08) != 0>::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 0,
(mask & 0x01) != 0 > ::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 1,
(mask & 0x02) != 0 > ::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 2,
(mask & 0x04) != 0 > ::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 3,
(mask & 0x08) != 0 > ::impl(a.values, b.values, vec.values);
return vec;
}
static Vectorized<float> blendv(

35
aten/src/ATen/cpu/vec/vec128/vec128_reduced_precision_common_neon.h
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@ -13,8 +13,7 @@ inline namespace CPU_CAPABILITY {
template <
typename VecT,
typename ValueT,
template <int, bool>
typename BlendRegs,
template <int, bool> typename BlendRegs,
typename Derived>
struct Vectorized16 {
protected:
@ -54,23 +53,23 @@ struct Vectorized16 {
template <int64_t mask>
static Derived blend(const Derived& a, const Derived& b) {
Derived vec;
vec.values =
BlendRegs<0, (mask & 0x01) != 0>::impl(a.values, b.values, vec.values);
vec.values =
BlendRegs<1, (mask & 0x02) != 0>::impl(a.values, b.values, vec.values);
vec.values =
BlendRegs<2, (mask & 0x04) != 0>::impl(a.values, b.values, vec.values);
vec.values =
BlendRegs<3, (mask & 0x08) != 0>::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 0,
(mask & 0x01) != 0 > ::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 1,
(mask & 0x02) != 0 > ::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 2,
(mask & 0x04) != 0 > ::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 3,
(mask & 0x08) != 0 > ::impl(a.values, b.values, vec.values);
vec.values =
BlendRegs<4, (mask & 0x10) != 0>::impl(a.values, b.values, vec.values);
vec.values =
BlendRegs<5, (mask & 0x20) != 0>::impl(a.values, b.values, vec.values);
vec.values =
BlendRegs<6, (mask & 0x40) != 0>::impl(a.values, b.values, vec.values);
vec.values =
BlendRegs<7, (mask & 0x80) != 0>::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 4,
(mask & 0x10) != 0 > ::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 5,
(mask & 0x20) != 0 > ::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 6,
(mask & 0x40) != 0 > ::impl(a.values, b.values, vec.values);
vec.values = BlendRegs < 7,
(mask & 0x80) != 0 > ::impl(a.values, b.values, vec.values);
return vec;
}

4
aten/src/ATen/mps/MPSProfiler.mm
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@ -780,8 +780,8 @@ void MPSProfiler::handleIntSignal(int signal) {
}
// used to capture sigint signal to log profiling stats
struct sigaction MPSProfiler::currentSigint {};
struct sigaction MPSProfiler::previousSigint {};
struct sigaction MPSProfiler::currentSigint{};
struct sigaction MPSProfiler::previousSigint{};
bool MPSProfiler::isCapturing() const {
return [captureManager isCapturing];

2
aten/src/ATen/native/cudnn/RNN.cpp
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@ -216,7 +216,7 @@ struct RNNDescriptorParams {
cudnnDataType_t datatype,
cudnnDataType_t input_datatype) {
#endif
this->set_mode(mode);
this -> set_mode(mode);
#ifdef USE_CUDNN_RNN_V8_API
this->input_size = input_size;
this->packed = packed;

22
aten/src/ATen/native/mps/kernels/Bucketization.metal
View File

@ -153,12 +153,12 @@ kernel void searchsorted(
constant INPUT_T * data_in [[buffer(0)]], \
constant INPUT_T * data_bd [[buffer(1)]], \
device OUTPUT_T * data_out [[buffer(2)]], \
constant int64_t & idim_in [[buffer(3)]], \
constant int64_t & idim_bd [[buffer(4)]], \
constant int64_t & numel_in [[buffer(5)]], \
constant int64_t & right [[buffer(6)]], \
constant int64_t & is_1d_boundaries [[buffer(7)]], \
constant int64_t * data_sort [[buffer(8)]], \
constant int64_t& idim_in [[buffer(3)]], \
constant int64_t& idim_bd [[buffer(4)]], \
constant int64_t& numel_in [[buffer(5)]], \
constant int64_t& right [[buffer(6)]], \
constant int64_t& is_1d_boundaries [[buffer(7)]], \
constant int64_t* data_sort [[buffer(8)]], \
uint2 tgid [[threadgroup_position_in_grid]], \
uint2 tid2 [[thread_position_in_threadgroup]], \
uint2 tptg [[threads_per_threadgroup]]); \
@ -167,11 +167,11 @@ kernel void searchsorted(
constant INPUT_T * data_in [[buffer(0)]], \
constant INPUT_T * data_bd [[buffer(1)]], \
device OUTPUT_T * data_out [[buffer(2)]], \
constant int64_t & idim_in [[buffer(3)]], \
constant int64_t & idim_bd [[buffer(4)]], \
constant int64_t & numel_in [[buffer(5)]], \
constant int64_t & right [[buffer(6)]], \
constant int64_t & is_1d_boundaries [[buffer(7)]], \
constant int64_t& idim_in [[buffer(3)]], \
constant int64_t& idim_bd [[buffer(4)]], \
constant int64_t& numel_in [[buffer(5)]], \
constant int64_t& right [[buffer(6)]], \
constant int64_t& is_1d_boundaries [[buffer(7)]], \
uint2 tgid [[threadgroup_position_in_grid]], \
uint2 tid2 [[thread_position_in_threadgroup]], \
uint2 tptg [[threads_per_threadgroup]]);

16
aten/src/ATen/native/mps/kernels/HistogramKernel.metal
View File

@ -101,14 +101,14 @@ kernel void histogramdd(
constant DTYPE * weight [[buffer(1)]], \
device DTYPE * local_out [[buffer(2)]], \
constant uint * offsets [[buffer(3)]], \
constant size_t & num_dims [[buffer(4)]], \
constant DTYPE * bin_seq [[buffer(5)]], \
constant int64_t * num_bin_edges [[buffer(6)]], \
constant DTYPE * leftmost_edge [[buffer(7)]], \
constant DTYPE * rightmost_edge [[buffer(8)]], \
constant int64_t * local_out_strides [[buffer(9)]], \
constant uint8_t & bin_selection_algorithm [[buffer(10)]], \
constant uint8_t & has_weight [[buffer(11)]], \
constant size_t& num_dims [[buffer(4)]], \
constant DTYPE* bin_seq [[buffer(5)]], \
constant int64_t* num_bin_edges [[buffer(6)]], \
constant DTYPE* leftmost_edge [[buffer(7)]], \
constant DTYPE* rightmost_edge [[buffer(8)]], \
constant int64_t* local_out_strides [[buffer(9)]], \
constant uint8_t& bin_selection_algorithm [[buffer(10)]], \
constant uint8_t& has_weight [[buffer(11)]], \
uint tid [[thread_position_in_grid]]);
REGISTER_HISTOGRAMDD_OP(float);

10
aten/src/ATen/native/mps/kernels/TriangularOps.metal
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@ -209,11 +209,11 @@ kernel void triu_indices(
template [[host_name(#NAME "_indices_" #DTYPE)]] kernel void \
NAME##_indices<DTYPE>( \
device DTYPE * tensor, \
constant int64_t & col_offset, \
constant int64_t & m_first_row, \
constant int64_t & col, \
constant int64_t & rectangle_size, \
constant int64_t & triu_size, \
constant int64_t& col_offset, \
constant int64_t& m_first_row, \
constant int64_t& col, \
constant int64_t& rectangle_size, \
constant int64_t& triu_size, \
uint linear_index [[thread_position_in_grid]])
INSTANTIATE_TRI_INDICES(triu, long);

6
aten/src/ATen/native/vulkan/ops/Unsqueeze.cpp
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@ -88,15 +88,13 @@ Tensor unsqueeze(const at::Tensor& self, int64_t dim) {
}
// Create the params buffer
struct Block block {
{
struct Block block{{
// Dimension to unsqueeze
static_cast<int32_t>(dim),
// Keep track of the channel in Image3D
static_cast<int32_t>(
std::ceil(static_cast<float>(output_size[channel_index]) / 4)),
}
};
}};
api::UniformParamsBuffer params(context, block);

8
c10/core/TensorImpl.h
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@ -193,11 +193,11 @@ struct C10_API AutogradMetaFactory {
C10_API void SetAutogradMetaFactory(AutogradMetaFactory* factory);
C10_API AutogradMetaFactory* GetAutogradMetaFactory();
struct C10_API AutogradMetaFactoryRegisterer {
explicit AutogradMetaFactoryRegisterer(AutogradMetaFactory* factory) {
struct C10_API AutogradMetaFactoryRegisterer{
explicit AutogradMetaFactoryRegisterer(AutogradMetaFactory * factory){
SetAutogradMetaFactory(factory);
}
};
} // namespace impl
}; // namespace c10
} // namespace impl

2
c10/macros/Macros.h
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@ -286,7 +286,7 @@ constexpr uint32_t CUDA_THREADS_PER_BLOCK_FALLBACK = 256;
#define C10_MIN_BLOCKS_PER_SM(threads_per_block, blocks_per_sm) \
((((threads_per_block) * (blocks_per_sm) <= CUDA_MAX_THREADS_PER_SM) \
? (blocks_per_sm) \
: ((CUDA_MAX_THREADS_PER_SM + (threads_per_block)-1) / \
: ((CUDA_MAX_THREADS_PER_SM + (threads_per_block) - 1) / \
(threads_per_block))))
// C10_LAUNCH_BOUNDS is analogous to __launch_bounds__
#define C10_LAUNCH_BOUNDS_0 \

3
c10/test/util/Metaprogramming_test.cpp
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@ -68,8 +68,7 @@ static_assert(
} // namespace test_function_traits
struct MovableOnly {
constexpr MovableOnly(int val_) : val(val_) { /* no default constructor */
}
constexpr MovableOnly(int val_) : val(val_) { /* no default constructor */ }
MovableOnly(const MovableOnly&) = delete;
MovableOnly(MovableOnly&&) = default;
MovableOnly& operator=(const MovableOnly&) = delete;

8
c10/test/util/tempfile_test.cpp
View File

@ -5,20 +5,20 @@
#if !defined(_WIN32)
static bool file_exists(const char* path) {
struct stat st {};
struct stat st{};
return stat(path, &st) == 0 && S_ISREG(st.st_mode);
}
static bool directory_exists(const char* path) {
struct stat st {};
struct stat st{};
return stat(path, &st) == 0 && S_ISDIR(st.st_mode);
}
#else
static bool file_exists(const char* path) {
struct _stat st {};
struct _stat st{};
return _stat(path, &st) == 0 && ((st.st_mode & _S_IFMT) == _S_IFREG);
}
static bool directory_exists(const char* path) {
struct _stat st {};
struct _stat st{};
return _stat(path, &st) == 0 && ((st.st_mode & _S_IFMT) == _S_IFDIR);
}
#endif // !defined(_WIN32)

2
c10/util/ApproximateClock.h
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@ -59,7 +59,7 @@ inline time_t getTime(bool allow_monotonic = false) {
.count();
#else
// clock_gettime is *much* faster than std::chrono implementation on Linux
struct timespec t {};
struct timespec t{};
auto mode = CLOCK_REALTIME;
if (allow_monotonic) {
mode = CLOCK_MONOTONIC;

4
c10/util/Exception.h
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@ -116,8 +116,8 @@ class C10_API Error : public std::exception {
class C10_API Warning {
public:
class C10_API UserWarning {};
class C10_API DeprecationWarning {};
class C10_API UserWarning{};
class C10_API DeprecationWarning{};
using warning_variant_t = std::variant<UserWarning, DeprecationWarning>;

4
c10/util/LeftRight.h
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@ -70,7 +70,9 @@ class LeftRight final {
~LeftRight() {
// wait until any potentially running writers are finished
{ std::unique_lock<std::mutex> lock(_writeMutex); }
{
std::unique_lock<std::mutex> lock(_writeMutex);
}
// wait until any potentially running readers are finished
while (_counters[0].load() != 0 || _counters[1].load() != 0) {

6
c10/util/SmallVector.h
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@ -370,9 +370,9 @@ class SmallVectorTemplateCommon
/// note
template <
typename T,
bool = (std::is_trivially_copy_constructible_v<T>)&&(
std::is_trivially_move_constructible_v<
T>)&&std::is_trivially_destructible_v<T>>
bool = (std::is_trivially_copy_constructible_v<T>) &&
(std::is_trivially_move_constructible_v<T>) &&
std::is_trivially_destructible_v<T>>
class SmallVectorTemplateBase : public SmallVectorTemplateCommon<T> {
friend class SmallVectorTemplateCommon<T>;

6
c10/util/signal_handler.cpp
View File

@ -59,7 +59,7 @@ void hookupHandler() {
if (hookedUpCount++) {
return;
}
struct sigaction sa {};
struct sigaction sa{};
// Setup the handler
sa.sa_handler = &handleSignal;
// Restart the system call, if at all possible
@ -80,7 +80,7 @@ void unhookHandler() {
if (--hookedUpCount > 0) {
return;
}
struct sigaction sa {};
struct sigaction sa{};
// Setup the sighub handler
sa.sa_handler = SIG_DFL;
// Restart the system call, if at all possible
@ -273,7 +273,7 @@ void FatalSignalHandler::installFatalSignalHandlers() {
return;
}
fatalSignalHandlersInstalled = true;
struct sigaction sa {};
struct sigaction sa{};
sigemptyset(&sa.sa_mask);
// Since we'll be in an exiting situation it's possible there's memory
// corruption, so make our own stack just in case.

2
c10/util/signal_handler.h
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@ -88,7 +88,7 @@ class C10_API FatalSignalHandler {
bool fatalSignalHandlersInstalled;
// We need to hold a reference to call the previous SIGUSR2 handler in case
// we didn't signal it
struct sigaction previousSigusr2 {};
struct sigaction previousSigusr2{};
// Flag dictating whether the SIGUSR2 handler falls back to previous handlers
// or is intercepted in order to print a stack trace.
std::atomic<bool> fatalSignalReceived;

22
test/cpp/api/dataloader.cpp
View File

@ -87,7 +87,7 @@ struct DummyChunkDataReader : public datasets::ChunkDataReader<int> {
return chunk_count_;
};
void reset() override{};
void reset() override {};
const static size_t chunk_count_ = 3;
// NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-magic-numbers,cppcoreguidelines-avoid-c-arrays)
@ -1479,7 +1479,7 @@ TEST(DataLoaderTest, StatefulDatasetWithNoWorkers) {
void reset() override {
counter = 0;
}
void save(torch::serialize::OutputArchive& archive) const override{};
void save(torch::serialize::OutputArchive& archive) const override {};
void load(torch::serialize::InputArchive& archive) override {}
int counter = 0;
};
@ -1517,7 +1517,7 @@ TEST(DataLoaderTest, StatefulDatasetWithManyWorkers) {
void reset() override {
counter = 0;
}
void save(torch::serialize::OutputArchive& archive) const override{};
void save(torch::serialize::OutputArchive& archive) const override {};
void load(torch::serialize::InputArchive& archive) override {}
int counter = 0;
std::mutex mutex;
@ -1556,7 +1556,7 @@ TEST(DataLoaderTest, StatefulDatasetWithMap) {
void reset() override {
counter = 0;
}
void save(torch::serialize::OutputArchive& archive) const override{};
void save(torch::serialize::OutputArchive& archive) const override {};
void load(torch::serialize::InputArchive& archive) override {}
int counter = 0;
};
@ -1605,7 +1605,7 @@ TEST(DataLoaderTest, StatefulDatasetWithCollate) {
void reset() override {
counter = 0;
}
void save(torch::serialize::OutputArchive& archive) const override{};
void save(torch::serialize::OutputArchive& archive) const override {};
void load(torch::serialize::InputArchive& archive) override {}
int counter = 0;
};
@ -1747,7 +1747,7 @@ TEST(DataLoaderTest, ChunkDataSetWithEmptyBatch) {
return 1;
};
void reset() override{};
void reset() override {};
};
const size_t prefetch_count = 1;
@ -1791,7 +1791,7 @@ TEST(DataLoaderTest, ChunkDataSetGetBatchWithUnevenBatchSize) {
return 2;
};
void reset() override{};
void reset() override {};
};
// NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays)
@ -1936,7 +1936,7 @@ TEST(DataLoaderTest, ChunkDatasetSave) {
return chunk_count_;
};
void reset() override{};
void reset() override {};
BatchType batch_data_ = BatchType(chunk_size, 0);
};
@ -2115,7 +2115,7 @@ TEST(DataLoaderTest, ChunkDatasetCrossChunkShuffle) {
class S : public samplers::Sampler<> {
public:
explicit S(size_t size) : size_(size), index_(0){};
explicit S(size_t size) : size_(size), index_(0) {};
void reset(std::optional<size_t> new_size = std::nullopt) override {
if (new_size.has_value()) {
@ -2170,7 +2170,7 @@ TEST(DataLoaderTest, ChunkDatasetCrossChunkShuffle) {
return chunk_count_;
};
void reset() override{};
void reset() override {};
size_t chunk_count_;
};
@ -2258,7 +2258,7 @@ TEST(DataLoaderTest, CustomPreprocessPolicy) {
return chunk_count_;
};
void reset() override{};
void reset() override {};
size_t chunk_count_;
};

8
test/cpp/api/modules.cpp
View File

@ -127,9 +127,7 @@ TEST_F(ModulesTest, Conv2dSameStrided) {
[&] { Conv2d model_invalid(options.stride(2)); }(),
"padding='same' is not supported for strided convolutions");
ASSERT_THROWS_WITH(
[&] {
Conv2d model_invalid(options.stride({1, 2}));
}(),
[&] { Conv2d model_invalid(options.stride({1, 2})); }(),
"padding='same' is not supported for strided convolutions");
}
@ -181,9 +179,7 @@ TEST_F(ModulesTest, Conv3dSameStrided) {
[&] { Conv3d model_invalid(options.stride(2)); }(),
"padding='same' is not supported for strided convolutions");
ASSERT_THROWS_WITH(
[&] {
Conv3d model_invalid(options.stride({1, 2, 1}));
}(),
[&] { Conv3d model_invalid(options.stride({1, 2, 1})); }(),
"padding='same' is not supported for strided convolutions");
}

4
test/cpp/api/tensor.cpp
View File

@ -920,7 +920,9 @@ TEST(TensorTest, Arange) {
}
TEST(TensorTest, PrettyPrintTensorDataContainer) {
{ ASSERT_EQ(c10::str(torch::detail::TensorDataContainer(1.1)), "1.1"); }
{
ASSERT_EQ(c10::str(torch::detail::TensorDataContainer(1.1)), "1.1");
}
{
ASSERT_EQ(
c10::str(torch::detail::TensorDataContainer({1.1, 2.2})), "{1.1, 2.2}");

50
test/cpp/jit/test_alias_analysis.cpp
View File

@ -1033,9 +1033,8 @@ TEST(ContainerAliasingTest, MovesAcrossContainedWrites) {
auto ops = torch::RegisterOperators().op(
"uses::list",
torch::RegisterOperators::options()
.catchAllKernel([](torch::List<at::Tensor> in) {
return torch::rand({2, 3});
})
.catchAllKernel(
[](torch::List<at::Tensor> in) { return torch::rand({2, 3}); })
.aliasAnalysis(AliasAnalysisKind::PURE_FUNCTION));
// Write to the inside of a list. Check that we can't reorder a
// print across it.
@ -1073,9 +1072,8 @@ TEST(ContainerAliasingTest, MovesAcrossContainedWritesNested) {
auto ops = torch::RegisterOperators().op(
"uses::list",
torch::RegisterOperators::options()
.catchAllKernel([](torch::List<at::Tensor> in) {
return torch::rand({2, 3});
})
.catchAllKernel(
[](torch::List<at::Tensor> in) { return torch::rand({2, 3}); })
.aliasAnalysis(AliasAnalysisKind::PURE_FUNCTION));
// Write to the inside of a list. Check that we can't reorder a
// print across it.
@ -1257,9 +1255,8 @@ TEST(AliasRegistrationTest, ConservativeWithInferredSchema) {
auto registry = torch::RegisterOperators().op(
"foo::rand1",
torch::RegisterOperators::options()
.catchAllKernel([](at::Tensor) -> at::Tensor {
return at::rand({2, 2});
})
.catchAllKernel(
[](at::Tensor) -> at::Tensor { return at::rand({2, 2}); })
.aliasAnalysis(AliasAnalysisKind::CONSERVATIVE));
const auto rand_op = Symbol::fromQualString("foo::rand1");
auto graph = std::make_shared<Graph>();
@ -1274,9 +1271,8 @@ TEST(AliasRegistrationTest, ConservativeWithSpecifiedSchema) {
auto registry = torch::RegisterOperators().op(
"foo::rand2(Tensor arg1) -> Tensor",
torch::RegisterOperators::options()
.catchAllKernel([](at::Tensor) -> at::Tensor {
return at::rand({2, 2});
})
.catchAllKernel(
[](at::Tensor) -> at::Tensor { return at::rand({2, 2}); })
.aliasAnalysis(AliasAnalysisKind::CONSERVATIVE));
const auto rand_op = Symbol::fromQualString("foo::rand2");
auto graph = std::make_shared<Graph>();
@ -1291,9 +1287,8 @@ TEST(AliasRegistrationTest, ConservativeWithAliasingAnnotationsShouldError) {
auto registry = torch::RegisterOperators().op(
"foo::rand3(Tensor(a) arg1) -> Tensor(b)",
torch::RegisterOperators::options()
.catchAllKernel([](at::Tensor) -> at::Tensor {
return at::rand({2, 2});
})
.catchAllKernel(
[](at::Tensor) -> at::Tensor { return at::rand({2, 2}); })
.aliasAnalysis(AliasAnalysisKind::CONSERVATIVE));
const auto rand_op = Symbol::fromQualString("foo::rand3");
@ -1312,9 +1307,8 @@ TEST(AliasRegistrationTest, ConservativeWithAliasingAnnotationsShouldError2) {
auto registry = torch::RegisterOperators().op(
"foo::rand4(Tensor(a) arg1) -> Tensor(a)",
torch::RegisterOperators::options()
.catchAllKernel([](at::Tensor) -> at::Tensor {
return at::rand({2, 2});
})
.catchAllKernel(
[](at::Tensor) -> at::Tensor { return at::rand({2, 2}); })
.aliasAnalysis(AliasAnalysisKind::CONSERVATIVE));
const auto rand_op = Symbol::fromQualString("foo::rand4");
auto graph = std::make_shared<Graph>();
@ -1334,9 +1328,8 @@ TEST(AliasRegistrationTest, FromSchemaWithInferredSchemaShouldError) {
torch::RegisterOperators().op(
"foo::rand5",
torch::RegisterOperators::options()
.catchAllKernel([](at::Tensor) -> at::Tensor {
return at::rand({2, 2});
})
.catchAllKernel(
[](at::Tensor) -> at::Tensor { return at::rand({2, 2}); })
.aliasAnalysis(AliasAnalysisKind::FROM_SCHEMA));
},
"Tried to register operator foo::rand5(Tensor _0) -> Tensor _0 with AliasAnalysisKind::FROM_SCHEMA, but the schema is inferred");
@ -1346,9 +1339,8 @@ TEST(AliasRegistrationTest, FromSchemaInferredPure) {
auto registry = torch::RegisterOperators().op(
"foo::rand6(Tensor arg1) -> Tensor",
torch::RegisterOperators::options()
.catchAllKernel([](at::Tensor) -> at::Tensor {
return at::rand({2, 2});
})
.catchAllKernel(
[](at::Tensor) -> at::Tensor { return at::rand({2, 2}); })
.aliasAnalysis(AliasAnalysisKind::FROM_SCHEMA));
const auto rand_op = Symbol::fromQualString("foo::rand6");
auto graph = std::make_shared<Graph>();
@ -1395,9 +1387,8 @@ TEST(AliasRegistrationTest, PureNoSchema) {
auto registry = torch::RegisterOperators().op(
"foo::rand9",
torch::RegisterOperators::options()
.catchAllKernel([](at::Tensor) -> at::Tensor {
return at::rand({2, 2});
})
.catchAllKernel(
[](at::Tensor) -> at::Tensor { return at::rand({2, 2}); })
.aliasAnalysis(AliasAnalysisKind::PURE_FUNCTION));
const auto rand_op = Symbol::fromQualString("foo::rand9");
auto graph = std::make_shared<Graph>();
@ -1412,9 +1403,8 @@ TEST(AliasRegistrationTest, PureWithSchema) {
auto registry = torch::RegisterOperators().op(
"foo::rand10(Tensor arg1) -> Tensor",
torch::RegisterOperators::options()
.catchAllKernel([](at::Tensor) -> at::Tensor {
return at::rand({2, 2});
})
.catchAllKernel(
[](at::Tensor) -> at::Tensor { return at::rand({2, 2}); })
.aliasAnalysis(AliasAnalysisKind::PURE_FUNCTION));
const auto rand_op = Symbol::fromQualString("foo::rand10");
auto graph = std::make_shared<Graph>();

8
test/cpp/jit/test_autodiff.cpp
View File

@ -121,14 +121,10 @@ TEST(AutodiffTest, ADFormulas) {
{"t", unary_pointwise_2d, [](const VL& v) -> VL { return {v[0].t()}; }},
{"view",
unary_pointwise_2d,
[](const VL& v) -> VL {
return {v[0].view({3, 2})};
}},
[](const VL& v) -> VL { return {v[0].view({3, 2})}; }},
{"expand",
{{2, 1}},
[](const VL& v) -> VL {
return {v[0].expand({2, 3})};
}},
[](const VL& v) -> VL { return {v[0].expand({2, 3})}; }},
{"mm",
{{10, 12}, {12, 15}},
[](const VL& v) -> VL { return {v[0].mm(v[1])}; }},

64
test/cpp/jit/test_misc.cpp
View File

@ -863,8 +863,12 @@ void checkScopeCallbacks() {
{
RECORD_TORCHSCRIPT_FUNCTION("test_method", {});
{ RECORD_FUNCTION("test_function", {}); }
{ RECORD_USER_SCOPE("test_user_scope"); }
{
RECORD_FUNCTION("test_function", {});
}
{
RECORD_USER_SCOPE("test_user_scope");
}
}
TORCH_CHECK(!bad_scope);
@ -1057,7 +1061,9 @@ TEST(RecordFunctionTest, RecordFunctionGuard) {
RECORD_USER_SCOPE("C");
}
}
{ RECORD_USER_SCOPE("D"); }
{
RECORD_USER_SCOPE("D");
}
}
}
TORCH_CHECK(fn_names.size() == 1);
@ -1084,7 +1090,9 @@ TEST(RecordFunctionTest, Callbacks) {
add_remove_test_add_cb<2>();
auto h3 = add_remove_test_add_cb<3>();
{ RECORD_USER_SCOPE("test"); }
{
RECORD_USER_SCOPE("test");
}
TORCH_CHECK(ids.size() == 3);
TORCH_CHECK(std::find(ids.begin(), ids.end(), 1) != ids.end());
@ -1094,7 +1102,9 @@ TEST(RecordFunctionTest, Callbacks) {
ids.clear();
removeCallback(h1);
{ RECORD_USER_SCOPE("test"); }
{
RECORD_USER_SCOPE("test");
}
TORCH_CHECK(ids.size() == 2);
TORCH_CHECK(std::find(ids.begin(), ids.end(), 2) != ids.end());
@ -1103,7 +1113,9 @@ TEST(RecordFunctionTest, Callbacks) {
ids.clear();
removeCallback(h3);
{ RECORD_USER_SCOPE("test"); }
{
RECORD_USER_SCOPE("test");
}
TORCH_CHECK(ids.size() == 1);
TORCH_CHECK(std::find(ids.begin(), ids.end(), 2) != ids.end());
@ -1115,7 +1127,9 @@ TEST(RecordFunctionTest, Callbacks) {
ids.clear();
add_remove_test_add_cb<1>();
{ RECORD_USER_SCOPE("test"); }
{
RECORD_USER_SCOPE("test");
}
TORCH_CHECK(ids.size() == 1);
TORCH_CHECK(ids[0] == 1);
@ -1128,7 +1142,9 @@ TEST(RecordFunctionTest, Callbacks) {
return nullptr;
}));
{ RECORD_USER_SCOPE("test_thread"); }
{
RECORD_USER_SCOPE("test_thread");
}
});
th.join();
TORCH_CHECK(ids.size() == 2);
@ -1136,7 +1152,9 @@ TEST(RecordFunctionTest, Callbacks) {
TORCH_CHECK(std::find(ids.begin(), ids.end(), 2) != ids.end());
ids.clear();
{ RECORD_USER_SCOPE("test"); }
{
RECORD_USER_SCOPE("test");
}
TORCH_CHECK(ids.size() == 1);
TORCH_CHECK(ids[0] == 1);
@ -1167,7 +1185,9 @@ TEST(RecordFunctionTest, Callbacks) {
TORCH_CHECK(ctx->b == "test_str");
}));
{ RECORD_USER_SCOPE("test"); }
{
RECORD_USER_SCOPE("test");
}
TORCH_CHECK(ids.size() == 1);
TORCH_CHECK(ids[0] == 1);
@ -1193,7 +1213,9 @@ TEST(RecordFunctionTest, Callbacks) {
}));
// Will call both global and thread local callbacks.
{ RECORD_USER_SCOPE("test_thread"); }
{
RECORD_USER_SCOPE("test_thread");
}
});
ctx_th.join();
TORCH_CHECK(ids.size() == 2);
@ -1216,21 +1238,27 @@ TEST(RecordFunctionTest, ShouldRun) {
return nullptr;
}));
{ RECORD_USER_SCOPE("test"); }
{
RECORD_USER_SCOPE("test");
}
EXPECT_TRUE(ran) << "first run didn't happen";
ran = false;
disableCallback(handle);
{ RECORD_USER_SCOPE("test"); }
{
RECORD_USER_SCOPE("test");
}
EXPECT_FALSE(ran) << "second run happened but shouldn't have";
ran = false;
reenableCallback(handle);
{ RECORD_USER_SCOPE("test"); }
{
RECORD_USER_SCOPE("test");
}
EXPECT_TRUE(ran) << "run after re-enable didn't happen";
ran = false;
@ -1273,7 +1301,9 @@ TEST(RecordFunctionTest, Basic) {
return nullptr;
})
.needsIds(true));
{ RECORD_USER_SCOPE("test"); }
{
RECORD_USER_SCOPE("test");
}
TORCH_CHECK(has_ids);
clearCallbacks();
has_ids = false;
@ -1282,7 +1312,9 @@ TEST(RecordFunctionTest, Basic) {
has_ids = fn.handle() > 0;
return nullptr;
}));
{ RECORD_USER_SCOPE("test"); }
{
RECORD_USER_SCOPE("test");
}
TORCH_CHECK(!has_ids);
clearCallbacks();
}

4
test/cpp/profiler/record_function.cpp
View File

@ -144,7 +144,9 @@ TEST(RecordFunctionTest, CallOrder) {
#undef REGISTER_CALLBACK
RECORD_FUNCTION("Outer", {});
{ RECORD_FUNCTION("Inner", {}); }
{
RECORD_FUNCTION("Inner", {});
}
at::clearCallbacks();
ASSERT_FALSE(at::hasCallbacks());

7
test/cpp/tensorexpr/test_external_calls.cpp
View File

@ -716,12 +716,11 @@ TEST(ExternalCall, UnaryFloat) {
std::string,
std::vector<ExprHandle>>;
std::vector<Test> tests = {};
tests.push_back(Test{// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
tests.push_back(Test{
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
{1, 64, 8, 9},
{1, 64, 5, 7},
[](at::Tensor x) {
return at::adaptive_avg_pool2d(x, {5, 7});
},
[](at::Tensor x) { return at::adaptive_avg_pool2d(x, {5, 7}); },
"nnc_aten_adaptive_avg_pool2d",
toExprHandleVec({5, 7})});
tests.push_back(Test{// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)

12
tools/linter/adapters/s3_init_config.json
View File

@ -8,16 +8,16 @@
],
"clang-format": {
"Darwin-arm": {
"download_url": "https://oss-clang-format.s3.us-east-2.amazonaws.com/macos-arm/17.0.6/clang-format",
"hash": "47c47f3c8275fd6e25d07128ef9a655d3f898eae6a59a7c7a801967871bdb2f7"
"download_url": "https://oss-clang-format.s3.us-east-2.amazonaws.com/macos-arm/19.1.4/clang-format",
"hash": "f0da3ecf0ab1e9b50e8c27bd2d7ca0baa619e2f4b824b35d79d46356581fa552"
},
"Darwin-i386": {
"download_url": "https://oss-clang-format.s3.us-east-2.amazonaws.com/macos-i386/17.0.6/clang-format",
"hash": "23423cbe62feb535c05c88e6f47e420ca2777603f90dff4d33d19b6f5177a79e"
"download_url": "https://oss-clang-format.s3.us-east-2.amazonaws.com/macos-i386/19.1.4/clang-format",
"hash": "f5eb5037b9aa9d1d2de650fb2e0fe1a2517768a462fae8e98791a67b698302f4"
},
"Linux": {
"download_url": "https://oss-clang-format.s3.us-east-2.amazonaws.com/linux64/17.0.6/clang-format",
"hash": "920159a0fafc7c65f6819e8a0b739ecc8e655f50f20a3a1db975a3473b86431b"
"download_url": "https://oss-clang-format.s3.us-east-2.amazonaws.com/linux64/19.1.4/clang-format",
"hash": "bfa9ef6eccb372f79ffcb6196af966fd84519ea9567f5ae7b6ad30208cd82109"
}
},
"clang-tidy": {

6
torch/csrc/DataLoader.cpp
View File

@ -37,7 +37,7 @@ using namespace torch;
auto _w = \
write(STDERR_FILENO, ERROR_MSG, sizeof(ERROR_MSG) / sizeof(char)); \
(void)_w; \
struct sigaction sa {}; \
struct sigaction sa{}; \
sa.sa_handler = SIG_DFL; \
sa.sa_flags = 0; \
if (sigemptyset(&sa.sa_mask) != 0 || \
@ -54,7 +54,7 @@ static void setSignalHandler(
int signal,
void (*handler)(int, siginfo_t*, void*),
struct sigaction* old_sa_ptr) {
struct sigaction sa {};
struct sigaction sa{};
sa.sa_sigaction = handler;
sa.sa_flags = SA_RESTART | SA_SIGINFO | SA_NOCLDSTOP | SA_NODEFER;
if (sigemptyset(&sa.sa_mask) != 0 ||
@ -92,7 +92,7 @@ static void handler_SIGTERM(int sig, siginfo_t* info, void* ctx) {
if (info->si_pid == getppid()) {
_exit(EXIT_SUCCESS);
}
struct sigaction sa {};
struct sigaction sa{};
sa.sa_handler = SIG_DFL;
sa.sa_flags = 0;
if (sigemptyset(&sa.sa_mask) != 0 || sigaction(SIGTERM, &sa, nullptr) != 0) {

7
torch/csrc/api/include/torch/detail/static.h
View File

@ -40,10 +40,9 @@ struct has_forward {
template <typename Head = void, typename... Tail>
constexpr bool check_not_lvalue_references() {
return (
!std::is_lvalue_reference_v<Head> ||
std::is_const_v<std::remove_reference_t<
Head>>)&&check_not_lvalue_references<Tail...>();
return (!std::is_lvalue_reference_v<Head> ||
std::is_const_v<std::remove_reference_t<Head>>) &&
check_not_lvalue_references<Tail...>();
}
template <>

2
torch/csrc/api/include/torch/nn/parallel/data_parallel.h
View File

@ -59,7 +59,7 @@ namespace {
// in data parallel, and should not be exposed as a user API.
struct ReduceAdd : public autograd::Node {
explicit ReduceAdd(const at::Device& destination_device)
: destination_device_(destination_device){};
: destination_device_(destination_device) {};
~ReduceAdd() override = default;
// NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)

4
torch/csrc/autograd/functions/init.cpp
View File

@ -71,7 +71,7 @@ template <
typename T,
typename ValueT,
typename ParamsT,
ValueT ParamsT::*ptr,
ValueT ParamsT::* ptr,
typename ConvertArgT,
PyObject* (*Convert)(ConvertArgT)>
static PyObject* getTupleAttr(PyObject* obj, void* _unused) {
@ -93,7 +93,7 @@ template <
typename T,
typename ValueT,
typename ParamsT,
ValueT ParamsT::*ptr,
ValueT ParamsT::* ptr,
typename ConvertArgT,
PyObject* (*Convert)(ConvertArgT)>
static PyObject* getValueAttr(PyObject* obj, void* _unused) {

10
torch/csrc/autograd/jit_decomp_interface.h
View File

@ -41,10 +41,10 @@ struct TORCH_API JitDecompInterface {
TORCH_API void setJitDecompImpl(JitDecompInterface* impl);
TORCH_API JitDecompInterface* getJitDecompImpl();
struct TORCH_API JitDecompRegisterer {
explicit JitDecompRegisterer(JitDecompInterface* impl) {
setJitDecompImpl(impl);
}
};
struct TORCH_API JitDecompRegisterer{explicit JitDecompRegisterer(
JitDecompInterface * impl){setJitDecompImpl(impl);
} // namespace torch::autograd::impl
}
;
} // namespace torch::autograd::impl

8
torch/csrc/autograd/python_function.cpp
View File

@ -1625,7 +1625,7 @@ using setter = int (*)(PyObject*, PyObject*, void*);
namespace {
template <PyObject* THPFunction::*ptr>
template <PyObject* THPFunction::* ptr>
PyObject* getObject(PyObject* obj, void* _unused) {
auto self = (THPFunction*)obj;
PyObject* value = self->*ptr;
@ -1636,7 +1636,7 @@ PyObject* getObject(PyObject* obj, void* _unused) {
return value;
}
template <PyObject* THPFunction::*ptr>
template <PyObject* THPFunction::* ptr>
int setObject(PyObject* obj, PyObject* value, void* _unused) {
auto self = (THPFunction*)obj;
if (value == Py_None) {
@ -1648,13 +1648,13 @@ int setObject(PyObject* obj, PyObject* value, void* _unused) {
return 0;
}
template <typename M, M THPFunction::*ptr, PyObject* (*Convert)(long)>
template <typename M, M THPFunction::* ptr, PyObject* (*Convert)(long)>
PyObject* getMember(PyObject* obj, void* _unused) {
auto self = (THPFunction*)obj;
return Convert(self->*ptr);
}
template <typename M, M autograd::Node::*ptr, PyObject* (*Convert)(long)>
template <typename M, M autograd::Node::* ptr, PyObject* (*Convert)(long)>
PyObject* getImplMember(PyObject* obj, void* _unused) {
auto self = (THPFunction*)obj;
return Convert(self->cdata.*ptr);

2
torch/csrc/distributed/c10d/CUDASymmetricMemory.hpp
View File

@ -37,7 +37,7 @@ class CUDASymmetricMemory : public SymmetricMemory {
int rank,
int world_size);
~CUDASymmetricMemory() override{};
~CUDASymmetricMemory() override {};
std::vector<void*> get_buffer_ptrs() override;
std::vector<void*> get_signal_pad_ptrs() override;

12
torch/csrc/distributed/c10d/CUDASymmetricMemoryUtils.cpp
View File

@ -85,14 +85,16 @@ void IpcChannel::send_fd(int dst_pid, int fd) {
memset(cbuf, 0, sizeof(cbuf));
// Create message header
struct msghdr msg {
struct msghdr msg{
// destination socket address and size of it
// message content in msg_iov and number of such structs (1 in our case)
// auxiliary data with the value of fd and size of it
.msg_name = (void*)&addr, .msg_namelen = sizeof(struct sockaddr_un),
.msg_iov = &io, .msg_iovlen = 1, .msg_control = cbuf,
.msg_controllen = sizeof(cbuf)
};
.msg_name = (void*)&addr,
.msg_namelen = sizeof(struct sockaddr_un),
.msg_iov = &io,
.msg_iovlen = 1,
.msg_control = cbuf,
.msg_controllen = sizeof(cbuf)};
// This points to the first control message header
// With SCM_RIGHTS we let the kernel know that we are passing file

46
torch/csrc/distributed/c10d/Ops.cpp
View File

@ -146,7 +146,8 @@ IMPL_REDUCE(PrivateUse1)
bool asyncOp, \
int64_t timeout) { \
auto tensor_vec = tensors.vec(); \
auto work = process_group->getBackend(c10::DeviceType::DEV) -> broadcast( \
auto work = process_group->getBackend(c10::DeviceType::DEV) \
->broadcast( \
tensor_vec, \
BroadcastOptions{ \
root_rank, \
@ -174,10 +175,13 @@ IMPL_BROADCAST(PrivateUse1)
bool asyncOp, \
int64_t timeout) { \
auto tensor_vec = tensors.vec(); \
auto work = process_group->getBackend(c10::DeviceType::DEV) -> allreduce( \
auto work = process_group->getBackend(c10::DeviceType::DEV) \
->allreduce( \
tensor_vec, \
AllreduceOptions{ \
*reduce_op.get(), std::chrono::milliseconds(timeout), asyncOp}); \
*reduce_op.get(), \
std::chrono::milliseconds(timeout), \
asyncOp}); \
return std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>>( \
std::move(tensor_vec), work); \
}
@ -217,10 +221,13 @@ IMPL_ALLREDUCE_COALESCED(PrivateUse1)
bool asyncOp, \
int64_t timeout) { \
auto input_tensors_vec = input_tensors.vec(); \
auto work = process_group->getBackend(c10::DeviceType::DEV) -> allgather( \
const_cast<std::vector<std::vector<at::Tensor>>&>(output_tensors), \
auto work = process_group->getBackend(c10::DeviceType::DEV) \
->allgather( \
const_cast<std::vector<std::vector<at::Tensor>>&>( \
output_tensors), \
input_tensors_vec, \
AllgatherOptions{std::chrono::milliseconds(timeout), asyncOp}); \
AllgatherOptions{ \
std::chrono::milliseconds(timeout), asyncOp}); \
return std:: \
tuple<std::vector<std::vector<at::Tensor>>, c10::intrusive_ptr<Work>>( \
output_tensors, work); \
@ -238,11 +245,12 @@ IMPL_ALLGATHER(PrivateUse1)
const c10::intrusive_ptr<ProcessGroup>& process_group, \
bool asyncOp, \
int64_t timeout) { \
auto work = \
process_group->getBackend(c10::DeviceType::DEV) -> _allgather_base( \
auto work = process_group->getBackend(c10::DeviceType::DEV) \
->_allgather_base( \
output_tensor, \
input_tensor, \
AllgatherOptions{std::chrono::milliseconds(timeout), asyncOp}); \
AllgatherOptions{ \
std::chrono::milliseconds(timeout), asyncOp}); \
return std::tuple<at::Tensor, c10::intrusive_ptr<Work>>( \
output_tensor, work); \
}
@ -299,10 +307,11 @@ IMPL_ALLGATHER_INTO_TENSOR_COALESCED(PrivateUse1)
bool asyncOp, \
int64_t timeout) { \
auto output_tensors_vec = output_tensors.vec(); \
auto work = \
process_group->getBackend(c10::DeviceType::DEV) -> reduce_scatter( \
auto work = process_group->getBackend(c10::DeviceType::DEV) \
->reduce_scatter( \
output_tensors_vec, \
const_cast<std::vector<std::vector<at::Tensor>>&>(input_tensors), \
const_cast<std::vector<std::vector<at::Tensor>>&>( \
input_tensors), \
ReduceScatterOptions{ \
*reduce_op.get(), \
std::chrono::milliseconds(timeout), \
@ -324,7 +333,7 @@ IMPL_REDUCE_SCATTER(PrivateUse1)
bool asyncOp, \
int64_t timeout) { \
auto work = process_group->getBackend(c10::DeviceType::DEV) \
-> _reduce_scatter_base( \
->_reduce_scatter_base( \
output_tensor, \
input_tensor, \
ReduceScatterOptions{ \
@ -393,9 +402,12 @@ IMPL_GATHER(PrivateUse1)
bool asyncOp, \
int64_t timeout) { \
auto output_tensors_vec = output_tensors.vec(); \
auto work = process_group->getBackend(c10::DeviceType::DEV) -> scatter( \
auto work = \
process_group->getBackend(c10::DeviceType::DEV) \
->scatter( \
output_tensors_vec, \
const_cast<std::vector<std::vector<at::Tensor>>&>(input_tensors), \
const_cast<std::vector<std::vector<at::Tensor>>&>( \
input_tensors), \
ScatterOptions{ \
root_rank, std::chrono::milliseconds(timeout), asyncOp}); \
return std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>>( \
@ -416,7 +428,9 @@ IMPL_SCATTER(PrivateUse1)
int64_t timeout) { \
auto output_tensors_vec = output_tensors.vec(); \
auto input_tensors_vec = input_tensors.vec(); \
auto work = process_group->getBackend(c10::DeviceType::DEV) -> alltoall( \
auto work = \
process_group->getBackend(c10::DeviceType::DEV) \
->alltoall( \
output_tensors_vec, \
input_tensors_vec, \
AllToAllOptions{std::chrono::milliseconds(timeout), asyncOp}); \

2
torch/csrc/distributed/c10d/ProcessGroupGloo.cpp
View File

@ -435,7 +435,7 @@ void socketInitialize() {
// gracefully fall back to an alternative if it doesn't.
bool doesHostnameResolveToUsableAddress(const std::string& hostname) {
socketInitialize();
struct addrinfo hints {};
struct addrinfo hints{};
hints.ai_family = AF_UNSPEC;
hints.ai_socktype = SOCK_STREAM;
struct addrinfo* result = nullptr;

4
torch/csrc/distributed/c10d/TCPStoreLibUvBackend.cpp
View File

@ -155,7 +155,7 @@ class UvTcpSocket : public UvHandle {
}
void startRead() {
struct ::sockaddr_storage addr {};
struct ::sockaddr_storage addr{};
int addrLen{sizeof(struct ::sockaddr_storage)};
if (int err = uv_tcp_getpeername(
@ -263,7 +263,7 @@ class UvTcpServer : public UvTcpSocket {
auto res = c10::make_intrusive<UvTcpServer>(loop);
res->handleReady();
try {
struct sockaddr_storage addr {};
struct sockaddr_storage addr{};
int uv_res = 0;
if (useIpv6) {
uv_res = uv_ip6_addr("::", port, (struct sockaddr_in6*)&addr);

2
torch/csrc/distributed/c10d/control_plane/PythonHandlers.cpp
View File

@ -40,5 +40,5 @@ RegisterHandler tracebackHandler{
res.setContent(std::move(file_contents), "text/plain");
}};
}
} // namespace
} // namespace c10d::control_plane

4
torch/csrc/distributed/rpc/init.cpp
View File

@ -631,8 +631,8 @@ PyObject* rpc_init(PyObject* _unused, PyObject* noargs) {
py::call_guard<py::gil_scoped_release>())
.def(
"_get_device_map",
(DeviceMap(TensorPipeAgent::*)(const WorkerInfo& dst) const) &
TensorPipeAgent::getDeviceMap,
(DeviceMap(TensorPipeAgent::*)(const WorkerInfo& dst)
const)&TensorPipeAgent::getDeviceMap,
py::call_guard<py::gil_scoped_release>())
.def(
"_get_backend_options",

2
torch/csrc/distributed/rpc/python_rpc_handler.cpp
View File

@ -14,7 +14,7 @@ constexpr auto kInternalModule = "torch.distributed.rpc.internal";
#define PROFILE_GIL_SCOPED_ACQUIRE \
std::chrono::time_point<std::chrono::high_resolution_clock> startTime; \
auto shouldProfileGIL = \
RpcAgent::getCurrentRpcAgent() -> isGILProfilingEnabled(); \
RpcAgent::getCurrentRpcAgent()->isGILProfilingEnabled(); \
if (shouldProfileGIL) { \
startTime = std::chrono::high_resolution_clock::now(); \
} \

4
torch/csrc/inductor/aoti_package/model_package_loader.cpp
View File

@ -39,7 +39,7 @@ bool file_exists(const std::string& path) {
#ifdef _WIN32
return fs::exists(path);
#else
struct stat rc {};
struct stat rc{};
return lstat(path.c_str(), &rc) == 0;
#endif
}
@ -217,7 +217,7 @@ bool recursive_rmdir(const std::string& path) {
}
struct dirent* entry = nullptr;
struct stat statbuf {};
struct stat statbuf{};
bool success = true;
// Iterate through directory entries

2
torch/csrc/inductor/aoti_runner/model_container_runner.cpp
View File

@ -17,7 +17,7 @@ bool file_exists(std::string& path) {
#ifdef _WIN32
return fs::exists(path);
#else
struct stat rc {};
struct stat rc{};
return lstat(path.c_str(), &rc) == 0;
#endif
}

10
torch/csrc/inductor/aoti_runner/model_container_runner.h
View File

@ -123,13 +123,13 @@ getAOTIModelRunnerRegistry();
// To register a new external backend in AOTI one needs to create an instance of
// this struct. It is not thread-safe. Becase it is expected to be called during
// the initialization of the program.
struct TORCH_API RegisterAOTIModelRunner {
RegisterAOTIModelRunner(
struct TORCH_API RegisterAOTIModelRunner{RegisterAOTIModelRunner(
const std::string& name,
CreateAOTIModelRunnerFunc create_aoti_model_runner_fn) {
CreateAOTIModelRunnerFunc create_aoti_model_runner_fn){
getAOTIModelRunnerRegistry()[name] = create_aoti_model_runner_fn;
}
};
} // namespace torch::inductor
}
;
} // namespace torch::inductor
#endif

2
torch/csrc/inductor/aoti_torch/shim_common.cpp
View File

@ -87,7 +87,7 @@ bool file_exists(std::string& path) {
#ifdef _WIN32
return fs::exists(path);
#else
struct stat rc {};
struct stat rc{};
return lstat(path.c_str(), &rc) == 0;
#endif
}

2
torch/csrc/inductor/cpp_prefix.h
View File

@ -358,7 +358,7 @@ struct IndexValueVec {
index = at::vec::VectorizedN<int64_t, NI>(0);
};
IndexValueVec(){};
IndexValueVec() {};
};
template <

10
torch/csrc/jit/codegen/fuser/compiler.h
View File

@ -45,12 +45,12 @@ TORCH_API void registerFusionBackend(
at::Device::Type backend_type,
FusedKernelConstructor ctor);
TORCH_API bool hasFusionBackend(at::Device::Type backend_type);
struct TORCH_API RegisterFusionBackend {
RegisterFusionBackend(
struct TORCH_API RegisterFusionBackend{RegisterFusionBackend(
at::Device::Type backend_type,
FusedKernelConstructor ctor) {
FusedKernelConstructor ctor){
registerFusionBackend(backend_type, std::move(ctor));
}
};
} // namespace torch::jit::fuser
}
;
} // namespace torch::jit::fuser

2
torch/csrc/jit/mobile/file_format.h
View File

@ -134,7 +134,7 @@ static inline std::tuple<std::shared_ptr<char>, size_t> get_file_content(
// failed to open file, chances are it's no such file or directory.
file_not_found_error();
}
struct stat statbuf {};
struct stat statbuf{};
fstat(fd, &statbuf);
size_t size = statbuf.st_size;
void* ptr = mmap(nullptr, statbuf.st_size, PROT_READ, MAP_PRIVATE, fd, 0);

11
torch/csrc/monitor/counters.h
View File

@ -36,12 +36,11 @@ enum class C10_API_ENUM Aggregation {
MIN = 6,
};
struct TORCH_API AggregationHash {
template <typename T>
std::size_t operator()(T t) const {
return static_cast<std::size_t>(t);
}
};
struct TORCH_API AggregationHash{template <typename T> std::size_t operator()(
T t) const {return static_cast<std::size_t>(t);
} // namespace torch::monitor
}
;
// aggregationName returns the human readable name corresponding to the
// aggregation.

36
torch/csrc/profiler/collection.cpp
View File

@ -808,23 +808,25 @@ void generateForwardBackwardLink(
void generateForwardBackwardLinks(
std::unique_ptr<torch::profiler::impl::kineto::trace_t>& cpu_trace,
const std::vector<std::shared_ptr<Result>>& results){
const std::vector<std::shared_ptr<Result>>& results) {
#ifndef USE_KINETO
}
#else // USE_KINETO
TORCH_INTERNAL_ASSERT(cpu_trace->activities.size() == results.size());
// startThreadId_seqNum to pointer of activity.
// Low-16bits of startThreadId and low-48bits seqNum are concatenated into
// one uint64_t variable as key.
// startThreadId_seqNum to pointer of activity.
// Low-16bits of startThreadId and low-48bits seqNum are concatenated into
// one uint64_t variable as key.
std::unordered_map<uint64_t, libkineto::GenericTraceActivity*> tidSeq2activity;
uint64_t fwd_bwd_link_id = 1;
std::unordered_map<uint64_t, libkineto::GenericTraceActivity*>
tidSeq2activity;
uint64_t fwd_bwd_link_id = 1;
using result_activity_t = std::pair<Result*, libkineto::GenericTraceActivity*>;
std::vector<result_activity_t> torch_events;
using result_activity_t =
std::pair<Result*, libkineto::GenericTraceActivity*>;
std::vector<result_activity_t> torch_events;
for (const auto idx : c10::irange(cpu_trace->activities.size())) {
for (const auto idx : c10::irange(cpu_trace->activities.size())) {
auto& profiler_result = results[idx];
auto& activity = cpu_trace->activities[idx];
@ -837,27 +839,27 @@ for (const auto idx : c10::irange(cpu_trace->activities.size())) {
torch_events.emplace_back(profiler_result.get(), activity.get());
}
});
}
}
// We need to visit the events in chronological order.
// So we sort them by end_time_ns_ before processing.
std::sort(
// We need to visit the events in chronological order.
// So we sort them by end_time_ns_ before processing.
std::sort(
torch_events.begin(),
torch_events.end(),
[](const result_activity_t& left, const result_activity_t& right) {
auto left_end_time =
std::get<ExtraFields<EventType::TorchOp>>(left.first->extra_fields_)
.end_time_ns_;
auto right_end_time =
std::get<ExtraFields<EventType::TorchOp>>(right.first->extra_fields_)
auto right_end_time = std::get<ExtraFields<EventType::TorchOp>>(
right.first->extra_fields_)
.end_time_ns_;
return left_end_time < right_end_time;
});
for (auto& [profiler_result, activity] : torch_events) {
for (auto& [profiler_result, activity] : torch_events) {
generateForwardBackwardLink(
*profiler_result, fwd_bwd_link_id, *activity, tidSeq2activity);
}
}
}
#endif // USE_KINETO

2
torch/csrc/profiler/perf.cpp
View File

@ -63,7 +63,7 @@ void PerfEvent::Init() {
TORCH_CHECK(false, "Unsupported profiler event name: ", name_);
}
struct perf_event_attr attr {};
struct perf_event_attr attr{};
attr.size = sizeof(perf_event_attr);
attr.type = it->second.first;

4
torch/csrc/profiler/python/init.cpp
View File

@ -673,8 +673,8 @@ void initPythonBindings(PyObject* module) {
{nullptr},
};
static PyTypeObject RecordFunctionFast_Type = { PyVarObject_HEAD_INIT(nullptr,
0)
static PyTypeObject RecordFunctionFast_Type = {
PyVarObject_HEAD_INIT(nullptr, 0)
};
RecordFunctionFast_Type.tp_name = "torch._C._profiler.RecordFunctionFast",

2
torch/csrc/profiler/unwind/mem_file.h
View File

@ -46,7 +46,7 @@ struct MemFile {
"failed to open {}: {}",
filename_,
c10::utils::str_error(errno));
struct stat s {};
struct stat s{};
if (-1 == fstat(fd_, &s)) {
close(fd_); // destructors don't run during exceptions
UNWIND_CHECK(

5
torch/csrc/utils.h
View File

@ -101,11 +101,10 @@
#define THPBoolUtils_newReal(value) THPUtils_newReal_BOOL(value)
#define THPBoolUtils_checkAccreal(object) THPUtils_checkReal_BOOL(object)
#define THPBoolUtils_unpackAccreal(object) \
(int64_t) THPUtils_unpackReal_BOOL(object)
(int64_t)THPUtils_unpackReal_BOOL(object)
#define THPBoolUtils_newAccreal(value) THPUtils_newReal_BOOL(value)
#define THPLongUtils_checkReal(object) THPUtils_checkReal_INT(object)
#define THPLongUtils_unpackReal(object) \
(int64_t) THPUtils_unpackReal_INT(object)
#define THPLongUtils_unpackReal(object) (int64_t)THPUtils_unpackReal_INT(object)
#define THPLongUtils_newReal(value) THPUtils_newReal_INT(value)
#define THPIntUtils_checkReal(object) THPUtils_checkReal_INT(object)
#define THPIntUtils_unpackReal(object) (int)THPUtils_unpackReal_INT(object)

2
torch/csrc/utils/byte_order.cpp
View File

@ -362,7 +362,7 @@ TORCH_API void THP_encodeBuffer<c10::complex<double>>(
#define DEFINE_ENCODE(TYPE) \
template TORCH_API void THP_encodeBuffer<TYPE>( \
uint8_t * dst, const TYPE* src, THPByteOrder order, size_t len);
uint8_t* dst, const TYPE* src, THPByteOrder order, size_t len);
DEFINE_ENCODE(int16_t)
DEFINE_ENCODE(int32_t)