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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
View File

@ -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;
}

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);

4
c10/core/TensorImpl.h
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@ -196,8 +196,8 @@ C10_API AutogradMetaFactory* GetAutogradMetaFactory();
struct C10_API AutogradMetaFactoryRegisterer{
explicit AutogradMetaFactoryRegisterer(AutogradMetaFactory * factory){
SetAutogradMetaFactory(factory);
}
};
} // namespace impl
}; // namespace c10
} // namespace impl

3
c10/test/util/Metaprogramming_test.cpp
View File

@ -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;

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
View File

@ -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>;

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": {

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 <>

8
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

10
torch/csrc/distributed/c10d/CUDASymmetricMemoryUtils.cpp
View File

@ -89,10 +89,12 @@ void IpcChannel::send_fd(int dst_pid, int fd) {
// 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

44
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), \
@ -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/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",

6
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){
getAOTIModelRunnerRegistry()[name] = create_aoti_model_runner_fn;
} // namespace torch::inductor
}
};
;
} // namespace torch::inductor
#endif

6
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){
registerFusionBackend(backend_type, std::move(ctor));
} // namespace torch::jit::fuser
}
};
;
} // namespace torch::jit::fuser

9
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.

10
torch/csrc/profiler/collection.cpp
View File

@ -818,10 +818,12 @@ void generateForwardBackwardLinks(
// 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;
std::unordered_map<uint64_t, libkineto::GenericTraceActivity*>
tidSeq2activity;
uint64_t fwd_bwd_link_id = 1;
using result_activity_t = std::pair<Result*, libkineto::GenericTraceActivity*>;
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())) {
@ -848,8 +850,8 @@ std::sort(
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;
});

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",

3
torch/csrc/utils.h
View File

@ -104,8 +104,7 @@
(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)