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Fix NOLINTNEXTLINE (#141794)

Browse source 
Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/141794
Approved by: https://github.com/Skylion007

Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>
This commit is contained in:
cyyever 2024-11-29 16:23:59 +00:00 committed by PyTorch MergeBot
parent 9e98b3d73c
commit 7dd9b5fc43
11 changed files with 17 additions and 83 deletions
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4
aten/src/ATen/quantized/Quantizer.cpp
View file

@ -365,9 +365,7 @@ Tensor from_blob_quantized_per_tensor_affine(
const auto ndim = sizes.size();
if (ndim > 0) {
strides.resize(ndim);
// NOLINTNEXTLINE
int32_t i = ndim - 1;
// NOLINTNEXTLINE
auto i = ndim - 1;
strides[i] = 1;
while (--i >= 0) {
strides[i] = sizes[i + 1] * strides[i + 1];

1
torch/csrc/jit/frontend/tracer.cpp
View file

@ -73,7 +73,6 @@ std::atomic<bool>& getTracerStateWarnMode() {
}
std::function<void()> pauseTracing() {
// NOLINTNEXTLINE
std::shared_ptr<tracer::TracingState> state = getTracingState();
tracer::setTracingState(nullptr);

81
torch/csrc/jit/tensorexpr/external_functions.cpp
View file

@ -103,23 +103,15 @@ std::vector<at::Tensor> constructTensors(
if (!qdataArg.has_value()) {
for (const auto i : c10::irange(buf_data_vec.size())) {
auto options = at::TensorOptions()
// NOLINTNEXTLINE
.dtype(buf_dtypes_vec[i])
.layout(at::kStrided)
.device(at::kCPU) // TODO: support GPUs too
.memory_format(deduce_memory_format(
// NOLINTNEXTLINE
buf_strides_vec[i],
// NOLINTNEXTLINE
buf_dims_vec[i]))
buf_strides_vec[i], buf_dims_vec[i]))
.requires_grad(false);
auto tensor = at::from_blob(
// NOLINTNEXTLINE
buf_data_vec[i],
buf_dims_vec[i],
buf_strides_vec[i],
options);
tensors.emplace_back(tensor);
buf_data_vec[i], buf_dims_vec[i], buf_strides_vec[i], options);
tensors.emplace_back(std::move(tensor));
}
} else {
// handle quantized
@ -129,35 +121,26 @@ std::vector<at::Tensor> constructTensors(
}
for (const auto i : c10::irange(buf_data_vec.size())) {
auto options = at::TensorOptions()
// NOLINTNEXTLINE
.dtype(buf_dtypes_vec[i])
.layout(at::kStrided)
.device(at::kCPU) // TODO: support GPUs too
.memory_format(deduce_memory_format(
// NOLINTNEXTLINE
buf_strides_vec[i],
// NOLINTNEXTLINE
buf_dims_vec[i]))
buf_strides_vec[i], buf_dims_vec[i]))
.requires_grad(false);
if (auto qd = qdata[i]) {
// inplace tensor
auto tensor = from_blob_quantized(
// NOLINTNEXTLINE
buf_data_vec[i],
buf_dims_vec[i],
buf_strides_vec[i],
qd->scale,
qd->zero,
qd->scalarType);
tensors.emplace_back(tensor);
tensors.emplace_back(std::move(tensor));
} else {
auto tensor = at::from_blob(
// NOLINTNEXTLINE
buf_data_vec[i],
buf_dims_vec[i],
buf_strides_vec[i],
options);
tensors.emplace_back(tensor);
buf_data_vec[i], buf_dims_vec[i], buf_strides_vec[i], options);
tensors.emplace_back(std::move(tensor));
}
}
}
@ -213,23 +196,15 @@ std::vector<at::Tensor> constructTensors2(
if (!qdataArg.has_value()) {
for (const auto i : c10::irange(buf_data_vec.size())) {
auto options = at::TensorOptions()
// NOLINTNEXTLINE
.dtype(buf_dtypes_vec[i])
.layout(at::kStrided)
.device(at::kCPU) // TODO: support GPUs too
.memory_format(deduce_memory_format(
// NOLINTNEXTLINE
buf_strides_vec[i],
// NOLINTNEXTLINE
buf_dims_vec[i]))
buf_strides_vec[i], buf_dims_vec[i]))
.requires_grad(false);
auto tensor = at::from_blob(
// NOLINTNEXTLINE
buf_data_vec[i],
buf_dims_vec[i],
buf_strides_vec[i],
options);
tensors.emplace_back(tensor);
buf_data_vec[i], buf_dims_vec[i], buf_strides_vec[i], options);
tensors.emplace_back(std::move(tensor));
}
} else {
// handle quantized
@ -239,35 +214,26 @@ std::vector<at::Tensor> constructTensors2(
}
for (const auto i : c10::irange(buf_data_vec.size())) {
auto options = at::TensorOptions()
// NOLINTNEXTLINE
.dtype(buf_dtypes_vec[i])
.layout(at::kStrided)
.device(at::kCPU) // TODO: support GPUs too
.memory_format(deduce_memory_format(
// NOLINTNEXTLINE
buf_strides_vec[i],
// NOLINTNEXTLINE
buf_dims_vec[i]))
buf_strides_vec[i], buf_dims_vec[i]))
.requires_grad(false);
if (auto qd = qdata[i]) {
// inplace tensor
auto tensor = from_blob_quantized(
// NOLINTNEXTLINE
buf_data_vec[i],
buf_dims_vec[i],
buf_strides_vec[i],
qd->scale,
qd->zero,
qd->scalarType);
tensors.emplace_back(tensor);
tensors.emplace_back(std::move(tensor));
} else {
auto tensor = at::from_blob(
// NOLINTNEXTLINE
buf_data_vec[i],
buf_dims_vec[i],
buf_strides_vec[i],
options);
tensors.emplace_back(tensor);
buf_data_vec[i], buf_dims_vec[i], buf_strides_vec[i], options);
tensors.emplace_back(std::move(tensor));
}
}
}
@ -429,7 +395,6 @@ void nnc_aten_quantized_conv1d(
reinterpret_cast<ConvPackedParamsBase<2>*>(buf_data[2]);
const double out_qscale = ((double*)extra_args)[3];
const int64_t out_qzero = extra_args[4];
// NOLINTNEXTLINE
auto qx = tensors[1].unsqueeze(quant_utils::kConv1dSqueezeDim + 2);
auto r = convPackedParams->apply(qx, out_qscale, out_qzero);
r = r.squeeze_(quant_utils::kConv1dSqueezeDim + 2);
@ -462,7 +427,6 @@ void nnc_aten_quantized_conv1d_out(
reinterpret_cast<ConvPackedParamsBase<2>*>(buf_data[2]);
const double out_qscale = ((double*)extra_args)[3];
const int64_t out_qzero = extra_args[4];
// NOLINTNEXTLINE
auto qx = tensors[1].unsqueeze(quant_utils::kConv1dSqueezeDim + 2);
auto r = convPackedParams->apply(qx, out_qscale, out_qzero);
r = r.squeeze_(quant_utils::kConv1dSqueezeDim + 2);
@ -495,7 +459,6 @@ void nnc_aten_quantized_conv2d(
reinterpret_cast<ConvPackedParamsBase<2>*>(buf_data[2]);
const double out_qscale = ((double*)extra_args)[3];
const int64_t out_qzero = extra_args[4];
// NOLINTNEXTLINE
auto r = convPackedParams->apply(tensors[1], out_qscale, out_qzero);
memcpy(buf_data[0], r.const_data_ptr(), r.element_size() * r.numel());
}
@ -526,7 +489,6 @@ void nnc_aten_quantized_conv2d_out(
reinterpret_cast<ConvPackedParamsBase<2>*>(buf_data[2]);
const double out_qscale = ((double*)extra_args)[3];
const int64_t out_qzero = extra_args[4];
// NOLINTNEXTLINE
auto r = convPackedParams->apply(tensors[1], out_qscale, out_qzero);
buf_data[0] = r.data_ptr();
c10::raw::intrusive_ptr::incref(r.getIntrusivePtr().get());
@ -557,7 +519,6 @@ void nnc_aten_quantized_conv2d_relu(
reinterpret_cast<ConvPackedParamsBase<2>*>(buf_data[2]);
const double out_qscale = ((double*)extra_args)[3];
const int64_t out_qzero = extra_args[4];
// NOLINTNEXTLINE
auto r = convPackedParams->apply_relu(tensors[1], out_qscale, out_qzero);
memcpy(buf_data[0], r.const_data_ptr(), r.element_size() * r.numel());
}
@ -588,7 +549,6 @@ void nnc_aten_quantized_conv2d_relu_out(
reinterpret_cast<ConvPackedParamsBase<2>*>(buf_data[2]);
const double out_qscale = ((double*)extra_args)[3];
const int64_t out_qzero = extra_args[4];
// NOLINTNEXTLINE
auto r = convPackedParams->apply_relu(tensors[1], out_qscale, out_qzero);
buf_data[0] = r.data_ptr();
c10::raw::intrusive_ptr::incref(r.getIntrusivePtr().get());
@ -619,7 +579,6 @@ void nnc_aten_quantized_linear(
reinterpret_cast<LinearPackedParamsBase*>(buf_data[2]);
const double out_qscale = ((double*)extra_args)[3];
const int64_t out_qzero = extra_args[4];
// NOLINTNEXTLINE
auto r = linearPackedParams->apply(tensors[1], out_qscale, out_qzero);
memcpy(buf_data[0], r.const_data_ptr(), r.element_size() * r.numel());
}
@ -650,7 +609,6 @@ void nnc_aten_quantized_linear_out(
reinterpret_cast<LinearPackedParamsBase*>(buf_data[2]);
const double out_qscale = ((double*)extra_args)[3];
const int64_t out_qzero = extra_args[4];
// NOLINTNEXTLINE
auto r = linearPackedParams->apply(tensors[1], out_qscale, out_qzero);
buf_data[0] = r.data_ptr();
c10::raw::intrusive_ptr::incref(r.getIntrusivePtr().get());
@ -681,7 +639,6 @@ void nnc_aten_quantized_linear_relu(
reinterpret_cast<LinearPackedParamsBase*>(buf_data[2]);
const double out_qscale = ((double*)extra_args)[3];
const int64_t out_qzero = extra_args[4];
// NOLINTNEXTLINE
auto r = linearPackedParams->apply_relu(tensors[1], out_qscale, out_qzero);
memcpy(buf_data[0], r.const_data_ptr(), r.element_size() * r.numel());
}
@ -716,7 +673,6 @@ void nnc_aten_quantized_add(
const double out_qscale = ((double*)extra_args)[6];
const int64_t out_qzero = extra_args[7];
// NOLINTNEXTLINE
auto r = quantized_add(tensors[1], tensors[2], out_qscale, out_qzero);
memcpy(buf_data[0], r.const_data_ptr(), r.element_size() * r.numel());
}
@ -747,7 +703,6 @@ void nnc_aten_quantized_mul(
{2u, {b_qscale, b_qzero, toQIntType(b_qdtype)}}});
const double out_qscale = ((double*)extra_args)[6];
const int64_t out_qzero = extra_args[7];
// NOLINTNEXTLINE
auto r = quantized_mul(tensors[1], tensors[2], out_qscale, out_qzero);
memcpy(buf_data[0], r.const_data_ptr(), r.element_size() * r.numel());
}
@ -780,7 +735,6 @@ void nnc_aten_quantized_mul_out(
1u);
const double out_qscale = ((double*)extra_args)[6];
const int64_t out_qzero = extra_args[7];
// NOLINTNEXTLINE
auto r = quantized_mul(tensors[1], tensors[2], out_qscale, out_qzero);
buf_data[0] = r.data_ptr();
c10::raw::intrusive_ptr::incref(r.getIntrusivePtr().get());
@ -808,7 +762,6 @@ void nnc_aten_quantized_mul_scalar(
buf_dtypes,
{{1u, {x_qscale, x_qzero, toQIntType(x_qdtype)}}});
const double scalar = ((double*)extra_args)[3];
// NOLINTNEXTLINE
auto r = quantized_mul_scalar(tensors[1], scalar);
memcpy(buf_data[0], r.const_data_ptr(), r.element_size() * r.numel());
}
@ -836,7 +789,6 @@ void nnc_aten_quantized_mul_scalar_out(
{{1u, {x_qscale, x_qzero, toQIntType(x_qdtype)}}},
bufs_out_num);
const double scalar = ((double*)extra_args)[3];
// NOLINTNEXTLINE
auto r = quantized_mul_scalar(tensors[1], scalar);
buf_data[0] = r.data_ptr();
c10::raw::intrusive_ptr::incref(r.getIntrusivePtr().get());
@ -863,7 +815,6 @@ void nnc_aten_quantized_relu(
buf_strides,
buf_dtypes,
{{1u, {x_qscale, x_qzero, toQIntType(x_qdtype)}}});
// NOLINTNEXTLINE
auto r = at::relu(tensors[1]);
memcpy(buf_data[0], r.const_data_ptr(), r.element_size() * r.numel());
}
@ -889,7 +840,6 @@ void nnc_aten_quantized_sigmoid(
buf_dtypes,
{{1u, {x_qscale, x_qzero, toQIntType(x_qdtype)}}});
// NOLINTNEXTLINE
auto r = at::sigmoid(tensors[1]);
memcpy(buf_data[0], r.const_data_ptr(), r.element_size() * r.numel());
}
@ -917,7 +867,6 @@ void nnc_aten_quantized_sigmoid_out(
{{1u, {x_qscale, x_qzero, toQIntType(x_qdtype)}}},
bufs_out_num);
// NOLINTNEXTLINE
auto r = at::sigmoid(tensors[1]);
buf_data[0] = r.data_ptr();
c10::raw::intrusive_ptr::incref(r.getIntrusivePtr().get());
@ -1123,7 +1072,6 @@ void nnc_aten_dequantize(
buf_dtypes,
{{1u,
{qscale, qzero, toQIntType(static_cast<c10::ScalarType>(qdtype))}}});
// NOLINTNEXTLINE
auto r = at::dequantize(tensors[1]);
memcpy(buf_data[0], r.const_data_ptr(), r.element_size() * r.numel());
}
@ -1150,7 +1098,6 @@ void nnc_aten_dequantize_out(
buf_dtypes,
{{1u, {qscale, qzero, toQIntType(static_cast<c10::ScalarType>(qdtype))}}},
bufs_out_num);
// NOLINTNEXTLINE
auto r = at::dequantize(tensors[1]);
buf_data[0] = r.data_ptr();
c10::raw::intrusive_ptr::incref(r.getIntrusivePtr().get());

1
torch/csrc/jit/tensorexpr/ir_mutator.cpp
View file

@ -108,7 +108,6 @@ ExprPtr IRMutator::mutate(const CompareSelectPtr& v) {
return v;
}
// NOLINTNEXTLINE
#define IMM_MUTATE_DEFINE(_1, Name) \
ExprPtr IRMutator::mutate(const Name##ImmPtr& v) { \
return v; \

1
torch/csrc/jit/tensorexpr/ir_printer.cpp
View file

@ -226,7 +226,6 @@ static void formatImm(std::ostream& os, T v) {
formatIntSuffix(os, v);
}
// NOLINTNEXTLINE
#define IMM_PRINT_VISIT(Type, Name) \
void IRPrinter::visit(const Name##ImmPtr& v) { \
formatImm(os(), v->value()); \

1
torch/csrc/jit/tensorexpr/lowerings.cpp
View file

@ -22,7 +22,6 @@ RegisterNNCLoweringsFunction::RegisterNNCLoweringsFunction(
}
namespace {
// NOLINTNEXTLINE
int nnc_lowerings_lazy_registration() {
RegisterNNCLoweringsFunction aten_dropout(
{"aten::dropout(Tensor input, float p, bool train) -> (Tensor)"},

3
torch/csrc/jit/tensorexpr/operators/quantization.cpp
View file

@ -543,7 +543,6 @@ Tensor computeQuantizedMulScalar(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
const std::vector<ExprHandle>& outputStrides,
// NOLINTNEXTLINE
const std::optional<ScalarType>& outputType,
at::Device device) {
const BufHandle& qa = std::get<BufHandle>(inputs[0]);
@ -598,9 +597,7 @@ Tensor computeQuantizedCat(
const std::vector<ArgValue>& inputs,
const std::vector<ExprHandle>& outputShape,
const std::vector<ExprHandle>& outputStrides,
// NOLINTNEXTLINE
const std::optional<ScalarType>& outputType,
// NOLINTNEXTLINE
at::Device device) {
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
auto inputList = std::get<BufList>(inputs[0]);

2
torch/csrc/jit/tensorexpr/types.cpp
View file

@ -31,7 +31,6 @@ TORCH_API Dtype kHandle(ScalarType::Undefined, 1);
Dtype ToDtype(ScalarType type) {
switch (type) {
// NOLINTNEXTLINE
#define TYPE_CASE(_1, n) \
case ScalarType::n: \
return k##n;
@ -93,7 +92,6 @@ int Dtype::byte_size() const {
std::string Dtype::ToCppString() const {
switch (scalar_type_) {
// NOLINTNEXTLINE
#define TYPE_CASE(t, n) \
case ScalarType::n: \
return #t;

1
torch/csrc/lazy/core/lazy_graph_executor.cpp
View file

@ -447,7 +447,6 @@ void LazyGraphExecutor::WaitDeviceOps(c10::ArrayRef<BackendDevice> devices) {
// The LockDevices() API returns a vector of
// ExceptionCleanup object, which is going to be freed
// immediately, turning this operation into a lock barrier.
// NOLINTNEXTLINE
DeviceLockerArena::Get()->LockDevices(wait_devices);
}

3
torch/csrc/profiler/unwind/mem_file.h
View file

@ -46,8 +46,7 @@ struct MemFile {
"failed to open {}: {}",
filename_,
c10::utils::str_error(errno));
// NOLINTNEXTLINE
struct stat s;
struct stat s {};
if (-1 == fstat(fd_, &s)) {
close(fd_); // destructors don't run during exceptions
UNWIND_CHECK(

2
torch/csrc/profiler/unwind/sections.h
View file

@ -16,7 +16,7 @@ static std::string demangle(const std::string& mangled_name) {
abi::__cxa_demangle(mangled_name.c_str(), nullptr, nullptr, &status);
if (status == 0) {
std::string demangled_name(realname);
// NOLINTNEXTLINE
// NOLINTNEXTLINE(cppcoreguidelines-no-malloc)
free(realname);
return demangled_name;
} else {