OSSForks/pytorch
1
0
Fork 0
mirror of https://github.com/zebrajr/pytorch.git synced 2025-12-06 12:20:52 +01:00
Code Issues Actions 38 Packages Projects Releases Wiki Activity

Compile without -Wno-unused-variable (#65954)

Browse Source 
Summary:
Delete `-Wno-unused-variable` from top level `CMakeLists.txt`
Still suppress those warnings for tests and `torch_python`

Delete number of unused variables from caffe2 code
Use `(void)var;` to suppress unused variable in range loops
Use `C10_UNUSED` for global constructors and use `constexpr` instead of `static` for global constants

Pull Request resolved: https://github.com/pytorch/pytorch/pull/65954

Reviewed By: ngimel

Differential Revision: D31326599

Pulled By: malfet

fbshipit-source-id: 924155f1257a2ba1896c50512f615e45ca1f61f3
This commit is contained in:
Nikita Shulga 2021-10-01 17:36:36 -07:00 committed by Facebook GitHub Bot
parent 10f6294281
commit a6280ab653
56 changed files with 73 additions and 94 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
CMakeLists.txt
View File

@ -744,7 +744,6 @@ if(NOT MSVC)
string(APPEND CMAKE_CXX_FLAGS " -Wno-unknown-pragmas")
string(APPEND CMAKE_CXX_FLAGS " -Wno-sign-compare")
string(APPEND CMAKE_CXX_FLAGS " -Wno-unused-parameter")
string(APPEND CMAKE_CXX_FLAGS " -Wno-unused-variable")
string(APPEND CMAKE_CXX_FLAGS " -Wno-unused-function")
string(APPEND CMAKE_CXX_FLAGS " -Wno-unused-result")
string(APPEND CMAKE_CXX_FLAGS " -Wno-unused-local-typedefs")

2
aten/src/ATen/mkl/Limits.h
View File

@ -6,6 +6,6 @@ namespace at { namespace native {
// Since size of MKL_LONG varies on different platforms (linux 64 bit, windows
// 32 bit), we need to programmatically calculate the max.
static int64_t MKL_LONG_MAX = ((1LL << (sizeof(MKL_LONG) * 8 - 2)) - 1) * 2 + 1;
constexpr int64_t MKL_LONG_MAX = ((1LL << (sizeof(MKL_LONG) * 8 - 2)) - 1) * 2 + 1;
}} // namespace

1
aten/src/ATen/test/cuda_stream_test.cpp
View File

@ -173,6 +173,7 @@ TEST(TestStream, StreamPoolTest) {
if (!at::cuda::is_available()) return;
std::vector<at::cuda::CUDAStream> streams{};
for (const auto i : c10::irange(200)) {
(void)i;
streams.emplace_back(at::cuda::getStreamFromPool());
}

2
aten/src/ATen/test/cuda_tensor_interop_test.cpp
View File

@ -84,6 +84,8 @@ TEST(CUDAPytorchToCaffe2, Op) {
auto* c2_tensor_a = BlobSetTensor(workspace.CreateBlob("a"), caffe2::Tensor(at_tensor_a));
auto* c2_tensor_b = BlobSetTensor(workspace.CreateBlob("b"), caffe2::Tensor(at_tensor_b));
(void)c2_tensor_a;
(void)c2_tensor_b;
// Test Alias
{

2
aten/src/ATen/test/math_kernel_test.cpp
View File

@ -54,8 +54,6 @@ TEST(MathKernelTest, NativeGroupNorm) {
TEST(MathKernelTest, NativeLayerNorm) {
const auto input = rand({20, 10, 10, 10});
const auto input_shape = input.sizes();
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
const auto input_ndim = input.dim();
double eps = 1e-05;
for (bool undef_weight: {true, false}) {

4
benchmarks/cpp/nvfuser/CMakeLists.txt
View File

@ -15,4 +15,8 @@ if(USE_CUDA)
main.cpp)
target_link_libraries(nvfuser_bench PRIVATE torch_library benchmark)
if(NOT MSVC)
target_compile_options(nvfuser_bench PRIVATE -Wno-unused-variable)
endif()
endif()

3
c10/test/CMakeLists.txt
View File

@ -6,6 +6,9 @@ if(BUILD_TEST)
get_filename_component(test_file_name ${test_src} NAME_WE)
set(test_name "c10_${test_file_name}")
add_executable(${test_name} "${test_src}")
if(NOT MSVC)
target_compile_options(${test_name} PRIVATE -Wno-unused-variable)
endif()
target_link_libraries(${test_name} c10 gmock gtest gtest_main)
add_test(NAME ${test_name} COMMAND $<TARGET_FILE:${test_name}>)
if(INSTALL_TEST)

3
caffe2/CMakeLists.txt
View File

@ -1762,6 +1762,9 @@ if(BUILD_TEST)
target_include_directories(${test_name} PRIVATE $<INSTALL_INTERFACE:include>)
target_include_directories(${test_name} PRIVATE $<BUILD_INTERFACE:${CMAKE_BINARY_DIR}/include>)
target_include_directories(${test_name} PRIVATE ${Caffe2_CPU_INCLUDE})
if(NOT MSVC)
target_compile_options(${test_name} PRIVATE -Wno-unused-variable)
endif()
add_test(NAME ${test_name} COMMAND $<TARGET_FILE:${test_name}>)
if(INSTALL_TEST)
install(TARGETS ${test_name} DESTINATION test)

2
caffe2/core/blob_serialization.cc
View File

@ -399,8 +399,8 @@ void TensorSerializer::SerializeWithOptions(
std::vector<std::future<void>> futures;
if (tensor.numel() > chunk_size) {
futures.reserve(FLAGS_caffe2_max_tensor_serializer_threads);
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
for (const auto i : c10::irange(FLAGS_caffe2_max_tensor_serializer_threads)) {
(void)i;
futures.emplace_back(std::async(std::launch::async, task));
}
}

1
caffe2/operators/cast_op.h
View File

@ -19,7 +19,6 @@ class CastOp : public Operator<Context> {
: Operator<Context>(operator_def, ws) {
const ArgumentHelper helper(operator_def);
TensorProto_DataType to = cast::GetCastDataType(helper, "to");
TensorProto_DataType from = cast::GetCastDataType(helper, "from_type");
SetBody(to);
}

4
caffe2/operators/conv_op_cudnn.cc
View File

@ -576,7 +576,9 @@ bool CudnnConvOp::DoRunWithType() {
return true;
}
#if !CUDNN_VERSION_MIN(7, 0, 0)
int group_offset_filter = filter.numel() / group_;
#endif
// Set up the cudnn algorithms & workspace if necessary
bool input_changed = (X.sizes() != cudnn_input_dims_);
@ -951,7 +953,9 @@ bool CudnnConvGradientOp::DoRunWithType() {
"If you set group, the number of output channels should be divisible "
"by group.");
#if !CUDNN_VERSION_MIN(7, 0, 0)
int group_offset_filter = filter.numel() / group_;
#endif
if (kernel_.size() == 1) {
ConvPoolOpBase<CUDAContext>::ComputePads({H});
} else if (kernel_.size() == 2) {

3
caffe2/operators/fused_rowwise_nbitfake_conversion_ops.cc
View File

@ -25,11 +25,10 @@ float compress_uniform_simplified_(
float inverse_scale = 1.0f / scale;
float norm = 0.0f;
// NOLINTNEXTLINE(clang-diagnostic-unused-variable)
constexpr int VLEN = 8;
int i = 0;
#ifdef __AVX__
constexpr int VLEN = 8;
// vectorized loop
__m256 norm_v = _mm256_setzero_ps();
for (; i < N / VLEN * VLEN; i += VLEN) {

1
caffe2/operators/fused_rowwise_nbitfake_conversion_ops.h
View File

@ -72,7 +72,6 @@ class FloatToFusedNBitFakeRowwiseQuantizedOp final
CAFFE_THROW("Unsupported data type");
}
bool use_openmp = GREEDY;
#ifdef _OPENMP
vector<float> tmp_vec(input_columns * (GREEDY ? omp_get_max_threads() : 1));
#else

1
caffe2/operators/gather_fused_8bit_rowwise_op.h
View File

@ -30,7 +30,6 @@ class GatherFused8BitRowwiseOp : public Operator<Context> {
const std::vector<int64_t> shape = {indices.size(0), data.size(1) - 8};
auto* output = Output(0, shape, at::dtype<float>());
int block_size = shape[1];
auto block_bytesize = data.size_from_dim(1) * data.dtype().itemsize();
int N = indices.numel();

6
caffe2/operators/generate_proposals_op_util_nms.h
View File

@ -133,8 +133,7 @@ std::vector<int> soft_nms_cpu_upright(
// Find proposal with max score among remaining proposals
int max_pos;
// NOLINTNEXTLINE(clang-diagnostic-unused-variable)
auto max_score = GetSubArray(*out_scores, pending).maxCoeff(&max_pos);
GetSubArray(*out_scores, pending).maxCoeff(&max_pos);
int i = pending[max_pos];
keep.push_back(i);
@ -635,8 +634,7 @@ std::vector<int> soft_nms_cpu_rotated(
// Find proposal with max score among remaining proposals
int max_pos;
// NOLINTNEXTLINE(clang-diagnostic-unused-variable)
auto max_score = GetSubArray(*out_scores, pending).maxCoeff(&max_pos);
GetSubArray(*out_scores, pending).maxCoeff(&max_pos);
int i = pending[max_pos];
keep.push_back(i);

2
caffe2/operators/h_softmax_op.cc
View File

@ -458,8 +458,6 @@ bool HuffmanTreeHierarchyOp<T, Context>::RunOnDevice() {
std::vector<int> labelIndices;
labelIndices.resize(num_classes_);
// NOLINTNEXTLINE(clang-diagnostic-unused-variable)
int current_node_index = 0;
for (int i = 0; i < num_classes_; ++i) {
Node node(i, labelCounts[i]);
nodes.push(node);

2
caffe2/operators/layer_norm_op.h
View File

@ -132,8 +132,6 @@ class LayerNormGradientOp final : public Operator<Context> {
template <typename T>
bool DoRunWithType() {
const auto& dY = Input(0);
// NOLINTNEXTLINE(clang-diagnostic-unused-variable)
const auto& Y = Input(1);
const auto& mean = Input(2);
const auto& sigma = Input(3);
const auto& X = Input(4);

1
caffe2/operators/lengths_reducer_rowwise_8bit_ops.h
View File

@ -53,7 +53,6 @@ class SparseLengths8BitsRowwiseOp : public Operator<Context> {
"the second dim of scale_bias has to be equal to 2");
CAFFE_ENFORCE_EQ(1, indicesInput.dim(), "INDICES must be a vector");
const IndexType* indices = indicesInput.template data<IndexType>();
int64_t dataToReduceSize = indicesInput.size(0);
const int* lengths = lengthsInput.template data<int>();
vector<int64_t> shape = dataInput.sizes().vec();

2
caffe2/operators/local_response_normalization_op_cudnn.cc
View File

@ -193,8 +193,6 @@ bool CuDNNLRNGradientOp::DoRunWithType() {
bool CuDNNLRNGradientOp::RunOnDevice() {
// dispatch based on contents of tensor(s)
const auto& X = Input(0);
const auto& Y = Input(1);
const auto& dY = Input(2);
auto* dX = Output(0);

2
caffe2/operators/quantized/int8_add_op.h
View File

@ -55,8 +55,10 @@ class Int8AddOp final : public Operator<CPUContext> {
initQNNPACK();
#if !defined(FBCODE_CAFFE2) && defined(USE_INTERNAL_PTHREADPOOL_IMPL)
pthreadpool_t threadpool =
reinterpret_cast<pthreadpool_t>(ws_->GetThreadPool());
#endif
if (this->qnnpackOperator_ == nullptr) {
const qnnp_status createStatus = qnnp_create_add_nc_q8(

1
caffe2/operators/quantized/int8_channel_shuffle_op.h
View File

@ -47,7 +47,6 @@ class Int8ChannelShuffleOp final : public ConvPoolOpBase<CPUContext> {
const auto C = X.t.dim32(3);
const auto G = this->group_;
CAFFE_ENFORCE(C % G == 0, "");
const auto B = X.t.numel() / C;
initQNNPACK();

2
caffe2/operators/quantized/int8_conv_op.h
View File

@ -60,8 +60,10 @@ class Int8ConvOp final : public ConvPoolOpBase<CPUContext> {
runWithSharedBuffer<CPUContext>(ws_, [&](Tensor* buffer) {
initQNNPACK();
#if !defined(FBCODE_CAFFE2) && defined(USE_INTERNAL_PTHREADPOOL_IMPL)
pthreadpool_t threadpool =
reinterpret_cast<pthreadpool_t>(ws_->GetThreadPool());
#endif
if (this->qnnpackObject_ == nullptr) {
CAFFE_ENFORCE(

7
caffe2/operators/quantized/int8_conv_transpose_op.h
View File

@ -39,17 +39,12 @@ class Int8ConvTransposeOp final : public ConvTransposeUnpoolBase<CPUContext> {
const auto& W = Inputs()[1]->template Get<Int8TensorCPU>();
const auto& B = Inputs()[2]->template Get<Int8TensorCPU>();
auto* Y = Outputs()[0]->template GetMutable<Int8TensorCPU>();
const auto X_offset = -X.zero_point;
const auto W_offset = -W.zero_point;
const int32_t Y_offset =
this->template GetSingleArgument<int>("Y_zero_point", 0);
double Y_scale = this->template GetSingleArgument<float>("Y_scale", 1);
Y->scale = Y_scale;
Y->zero_point = Y_offset;
const auto N = X.t.size(0);
const auto IH = X.t.size(1);
const auto IW = X.t.size(2);
const auto IC = X.t.size(3);
CHECK_EQ(IC, W.t.size(0));
@ -64,8 +59,10 @@ class Int8ConvTransposeOp final : public ConvTransposeUnpoolBase<CPUContext> {
runWithSharedBuffer<CPUContext>(ws_, [&](Tensor* buffer) {
initQNNPACK();
#if !defined(FBCODE_CAFFE2) && defined(USE_INTERNAL_PTHREADPOOL_IMPL)
pthreadpool_t threadpool =
reinterpret_cast<pthreadpool_t>(ws_->GetThreadPool());
#endif
if (this->qnnpackObject_ == nullptr) {
const qnnp_status createStatus = qnnp_create_deconvolution2d_nhwc_q8(

2
caffe2/operators/quantized/int8_fc_op.h
View File

@ -47,8 +47,10 @@ class Int8FCOp final : public Operator<CPUContext> {
runWithSharedBuffer<CPUContext>(ws_, [&](Tensor* buffer) {
initQNNPACK();
#if !defined(FBCODE_CAFFE2) && defined(USE_INTERNAL_PTHREADPOOL_IMPL)
pthreadpool_t threadpool =
reinterpret_cast<pthreadpool_t>(ws_->GetThreadPool());
#endif
if (this->qnnpackObject_ == nullptr) {
const qnnp_status createStatus = qnnp_create_fully_connected_nc_q8(

2
caffe2/operators/quantized/int8_quantize_op.h
View File

@ -19,10 +19,10 @@ void Int8Quantize(
const int64_t N,
const float Y_scale,
const int32_t Y_offset) {
const float inv_scale = 1.0f / Y_scale;
uint32_t i = 0;
#ifdef INT8_NEON_SIMD
const float inv_scale = 1.0f / Y_scale;
const float32x4_t vinv_scale = vdupq_n_f32(inv_scale);
// magic float and magic int to take care of rounding
// int magic_round(float f): interpret_int32(f + 12582912.0f) - 0x4B400000

1
caffe2/operators/quantized/int8_softmax_op.h
View File

@ -38,7 +38,6 @@ class Int8SoftmaxOp final : public Operator<CPUContext> {
* in-place, we may overwrite these parameters later, when we set
* quantization parameters for output tensor.
*/
const uint8_t X_zero_point = X.zero_point;
const float X_scale = X.scale;
Y->scale = Y_scale;

2
caffe2/operators/text_file_reader.cc
View File

@ -141,8 +141,6 @@ class TextFileReaderReadOp : public Operator<CPUContext> {
(field > 0 && token.startDelimId == 1),
"Invalid number of columns at row ",
instance->rowsRead + rowsRead + 1);
// NOLINTNEXTLINE(clang-diagnostic-unused-variable)
const auto& meta = instance->fieldMetas[field];
char*& data = datas[field];
convert(
(TensorProto_DataType)instance->fieldTypes[field],

2
caffe2/operators/utility_ops.h
View File

@ -686,8 +686,6 @@ class ScatterAssignOp : public Operator<Context> {
const auto dataType = TypeMetaToDataType(data.dtype());
const auto slicesType = TypeMetaToDataType(slices.dtype());
const auto indicesType = TypeMetaToDataType(indices.dtype());
// NOLINTNEXTLINE(clang-diagnostic-unused-variable)
auto* output = Output(0);
auto runner = GetRunner(dataType, slicesType, indicesType);
(this->*runner)();

2
caffe2/operators/weighted_sample_op.cc
View File

@ -57,8 +57,6 @@ bool WeightedSampleOp<float, CPUContext>::RunOnDevice() {
}
}
} else {
// NOLINTNEXTLINE(clang-diagnostic-unused-variable,clang-analyzer-deadcode.DeadStores)
auto* out_idx = Output(0, {0}, at::dtype<int>());
if (OutputSize() == 2) {
auto* out_value = Output(1, {0}, at::dtype<float>());
out_value->template mutable_data<float>();

7
caffe2/opt/bound_shape_inferencer.cc
View File

@ -436,13 +436,6 @@ void BoundShapeInferencer::InferSparseLengthsSum(const OperatorDef& op) {
op.type() == "SparseLengthsWeightedSum4BitRowwiseSparse" ||
op.type() == "SparseLengthsSum4BitRowwiseSparse");
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
const bool isSparse =
(op.type() == "SparseLengthsSum4BitRowwiseSparse" ||
op.type() == "SparseLengthsWeightedSum4BitRowwiseSparse" ||
op.type() == "SparseLengthsSum8BitRowwiseSparse" ||
op.type() == "SparseLengthsWeightedSum8BitRowwiseSparse");
if (weight) {
CAFFE_ENFORCE_GE(
op.input_size(),

6
caffe2/opt/optimize_ideep.cc
View File

@ -533,8 +533,6 @@ bool fuseActivation(repr::NNModule* nn, caffe2::Workspace* ws) {
continue;
}
auto relu_node = consumers.front();
// NOLINTNEXTLINE(clang-diagnostic-unused-variable,clang-analyzer-deadcode.DeadStores)
auto relu = repr::nn::get<repr::Relu>(relu_node);
auto relu_outputs = repr::nn::getOutputs(relu_node);
if (relu_outputs.size() != 1) {
@ -893,10 +891,6 @@ void preConvertFiltersFormat(repr::NNModule* nn, caffe2::Workspace* ws) {
initValue(strides, {1, 1});
auto pads = convTranspose->getPads();
initValue(pads, {0, 0, 0, 0});
// NOLINTNEXTLINE(clang-diagnostic-unused-variable,clang-analyzer-deadcode.DeadStores)
auto* op = getMutableOpDef(*convTranspose);
// NOLINTNEXTLINE(clang-diagnostic-unused-variable)
auto aalgorithm = ialgo::deconvolution_direct;
auto dataType = filter->get_data_type();
ideep::tensor::dims filter_dims_mkldnn{filter->get_dim(1),
filter->get_dim(0),

2
caffe2/quantization/server/dynamic_histogram.cc
View File

@ -64,12 +64,12 @@ void RemapHistograms(Histogram& src_hist, Histogram& dst_hist) {
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
float dst_bin_begin = dst_hist.Min() + dst_bin_width * dst_bin;
float dst_bin_end = dst_bin_begin + dst_bin_width;
// NOLINTNEXTLINE(clang-diagnostic-unused-variable,clang-analyzer-deadcode.DeadStores)
int dst_bin2 =
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
dst_bin_width == 0 ? 0 : (src_bin_end - dst_hist.Min()) / dst_bin_width;
// 1 src_bin is mapped to at most 2 dst bin
assert(dst_bin2 <= dst_bin + 2);
(void)dst_bin2;
// dst_bin_cnt is the count from src_bin that should go to dst_bin
// The remainder should go to dst_bin2

10
caffe2/quantization/server/fbgemm_pack_op.cc
View File

@ -698,9 +698,8 @@ TypeIdentifier Int8ConvDNNLowpPackedWeightBlobShapeFunctions::GetTypeMetaId() {
TypeMeta Int8FCDNNLowpPackedWeightBlobShapeFunctions::GetExternalTensorType(
const void* c) {
// NOLINTNEXTLINE(clang-diagnostic-unused-variable)
const Int8FCDNNLowPPackedWeightBlob* int8_tensor =
reinterpret_cast<const Int8FCDNNLowPPackedWeightBlob*>(c);
// const Int8FCDNNLowPPackedWeightBlob* int8_tensor =
// reinterpret_cast<const Int8FCDNNLowPPackedWeightBlob*>(c);
// We forced the output type to be uint8_t since we know it always is.
// If it is going to be implemented elsewhere, we might need to change here.
// return (int8_tensor->original_tensor).dtype();
@ -709,9 +708,8 @@ TypeMeta Int8FCDNNLowpPackedWeightBlobShapeFunctions::GetExternalTensorType(
TypeMeta Int8ConvDNNLowpPackedWeightBlobShapeFunctions::GetExternalTensorType(
const void* c) {
// NOLINTNEXTLINE(clang-diagnostic-unused-variable)
const Int8ConvDNNLowPPackedWeightBlob* int8_tensor =
reinterpret_cast<const Int8ConvDNNLowPPackedWeightBlob*>(c);
// const Int8ConvDNNLowPPackedWeightBlob* int8_tensor =
// reinterpret_cast<const Int8ConvDNNLowPPackedWeightBlob*>(c);
// return (int8_tensor->original_tensor).dtype();
return TypeMeta::Make<uint8_t>();
}

4
caffe2/quantization/server/p99.cc
View File

@ -21,11 +21,7 @@ TensorQuantizationParams P99::ChooseQuantizationParams(
float org_min = min;
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
float bin_width = (max - min) / nbins;
// NOLINTNEXTLINE(clang-diagnostic-unused-variable,bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions,clang-analyzer-deadcode.DeadStores)
int zero_bin = round(-min / bin_width);
// NOLINTNEXTLINE(clang-diagnostic-unused-variable)
int best_width = 0;
double total_sum = 0;
for (int i = 0; i < nbins; ++i) {
total_sum += bins_f[i];

9
caffe2/share/contrib/nnpack/conv_op.cc
View File

@ -152,8 +152,7 @@ bool NNPACKConvOp::RunOnDeviceWithOrderNCHW() {
auto& filter = Input(1);
auto* Y = Output(0);
CAFFE_ENFORCE(X.ndim() == 4, "Input dim should be 4");
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
const int N = X.dim32(0), C = X.dim32(1), H = X.dim32(2), W = X.dim32(3);
const int C = X.dim32(1), H = X.dim32(2), W = X.dim32(3);
CAFFE_ENFORCE(filter.ndim() == 4, "");
const int M = filter.dim32(0);
CAFFE_ENFORCE(C % this->group_ == 0, "");
@ -181,12 +180,6 @@ bool NNPACKConvOp::RunOnDeviceWithOrderNCHW() {
biasData = dummyBias_.data();
}
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
const size_t batch_size = X.dim32(0);
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
const size_t input_channels = X.dim32(1);
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
const size_t output_channels = Y->dim32(1);
const nnp_size input_size = {.width = static_cast<size_t>(X.dim32(3)),
.height = static_cast<size_t>(X.dim32(2))};
// filter is MCHW

3
test/cpp/api/CMakeLists.txt
View File

@ -53,6 +53,9 @@ endif()
add_executable(test_api ${TORCH_API_TEST_SOURCES})
target_include_directories(test_api PRIVATE ${ATen_CPU_INCLUDE})
target_link_libraries(test_api PRIVATE torch gtest)
if(NOT MSVC)
target_compile_options(test_api PRIVATE -Wno-unused-variable)
endif()
if(USE_CUDA)
target_link_libraries(test_api PRIVATE

3
test/cpp/jit/CMakeLists.txt
View File

@ -97,6 +97,9 @@ endif(MSVC)
target_link_libraries(test_jit PRIVATE ${JIT_TEST_DEPENDENCIES})
target_include_directories(test_jit PRIVATE ${ATen_CPU_INCLUDE})
if(NOT MSVC)
target_compile_options(test_jit PRIVATE -Wno-unused-variable)
endif()
if(LINUX)
#Update to target_link_options when CMake version can be upgraded

1
test/cpp/jit/test_backend_compiler_lib.cpp
View File

@ -89,7 +89,6 @@ class BackendWithCompiler : public PyTorchBackendInterface {
at::Tensor h = val1.toTensor();
c10::List<at::Tensor> output_list;
double scalar_val = 1.0;
for (const auto& token : handle.toList()) {
IValue val = token;
auto instruction = val.toTuple()->elements()[0].toStringRef();

3
test/cpp/tensorexpr/CMakeLists.txt
View File

@ -38,6 +38,9 @@ add_executable(test_tensorexpr
target_link_libraries(test_tensorexpr PRIVATE torch gtest)
target_include_directories(test_tensorexpr PRIVATE ${ATen_CPU_INCLUDE})
target_compile_definitions(test_tensorexpr PRIVATE USE_GTEST)
if(NOT MSVC)
target_compile_options(test_tensorexpr PRIVATE -Wno-unused-variable)
endif()
add_executable(tutorial_tensorexpr ${TENSOREXPR_TEST_ROOT}/tutorial.cpp)
target_link_libraries(tutorial_tensorexpr PRIVATE torch)

4
torch/CMakeLists.txt
View File

@ -354,6 +354,10 @@ if(USE_PRECOMPILED_HEADERS)
"$<$<COMPILE_LANGUAGE:CXX>:ATen/ATen.h>")
endif()
if(NOT MSVC)
target_compile_options(torch_python PRIVATE -Wno-unused-variable)
endif()
# Required workaround for generated sources
# See https://samthursfield.wordpress.com/2015/11/21/cmake-dependencies-between-targets-and-files-and-custom-commands/#custom-commands-in-different-directories
add_dependencies(torch_python generate-torch-sources)

1
torch/csrc/distributed/c10d/ProcessGroupMPI.cpp
View File

@ -270,6 +270,7 @@ c10::intrusive_ptr<ProcessGroupMPI> ProcessGroupMPI::createProcessGroupMPI(
bool groupComm_updated = false;
MPI_Barrier(MPI_COMM_WORLD);
for (const auto i : c10::irange(kMaxNumRetries)) {
(void)i;
if (MPI_Comm_create(MPI_COMM_WORLD, ranksGroup, &groupComm)) {
groupComm_updated = true;
break;

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

@ -939,6 +939,7 @@ std::vector<std::shared_ptr<NCCLComm>>& ProcessGroupNCCL::getNCCLComm(
// created before encountering any communication calls. This is why we need
// the following for loop.
for (const auto i : c10::irange(ncclActiveGroupCounter_)) {
(void)i;
C10D_NCCL_CHECK(ncclGroupEnd(), c10::nullopt);
}
@ -978,6 +979,7 @@ std::vector<std::shared_ptr<NCCLComm>>& ProcessGroupNCCL::getNCCLComm(
// See [Group Start/End Note]
for (const auto i : c10::irange(ncclActiveGroupCounter_)) {
(void)i;
C10D_NCCL_CHECK(ncclGroupStart(), c10::nullopt);
}

3
torch/csrc/distributed/c10d/reducer.cpp
View File

@ -1383,8 +1383,6 @@ void Reducer::finalize_bucket_dense(Bucket& bucket) {
auto& replica = bucket.replicas[replica_index];
for (const auto intra_bucket_index : c10::irange(replica.variables.size())) {
auto& variable = replica.variables[intra_bucket_index];
const auto offset = replica.offsets[intra_bucket_index];
const auto length = replica.lengths[intra_bucket_index];
bool global_unused = false;
// See Note [Skip allreducing local_used_map_dev]
@ -1634,6 +1632,7 @@ void Reducer::sync_bucket_indices(
std::vector<size_t> bucket;
bucket.reserve(bucket_size);
for (const auto j : c10::irange(bucket_size)) {
(void)j;
bucket.push_back(indices_accessor[indices_accessor_Index++]);
}
bucket_indices.emplace_back(std::move(bucket));

2
torch/csrc/jit/codegen/cuda/lower_thread_predicate.cpp
View File

@ -280,7 +280,7 @@ void ThreadPredicateMap::insert(
kir::Bool* ThreadPredicateMap::getPredicate(const TensorView* tv) const {
// No thread predicate is needed when tv is an output of a
// parallel broadcast expression.
if (auto bop = dynamic_cast<BroadcastOp*>(tv->definition())) {
if (dynamic_cast<BroadcastOp*>(tv->definition())) {
if (getParallelBroadcastDomains(tv).any()) {
return kir::IrBuilder(GpuLower::current()->kernel()).trueVal();
}

2
torch/csrc/jit/frontend/ir_emitter.cpp
View File

@ -5163,7 +5163,7 @@ std::unique_ptr<Function> CompilationUnit::define(
if (shouldMangle) {
// If `shouldMangle` is set, we should generate a unique name for this
// function if there is already an existing one.
if (auto fn = find_function(name)) {
if (find_function(name)) {
name = mangle(name);
}
}

4
torch/csrc/jit/frontend/sugared_value.cpp
View File

@ -153,7 +153,7 @@ std::shared_ptr<SugaredValue> SimpleValue::attr(
}
} else if (auto classType = value_->type()->cast<ClassType>()) {
// This is a class, emit the proper attribute lookup
if (auto method = classType->findMethod(field)) {
if (classType->findMethod(field)) {
return std::make_shared<MethodValue>(getValue(), field);
}
if (classType->hasAttribute(field)) {
@ -169,7 +169,7 @@ std::shared_ptr<SugaredValue> SimpleValue::attr(
}
} else if (auto iface = value_->type()->cast<InterfaceType>()) {
// accessing methods of interfaces
if (auto schema = iface->getMethod(field)) {
if (iface->getMethod(field)) {
return std::make_shared<MethodValue>(getValue(), field);
}
} else if (auto enum_type = value_->type()->cast<EnumType>()) {

2
torch/csrc/jit/mobile/promoted_prim_ops.cpp
View File

@ -166,7 +166,7 @@ void numToTensorBool(Stack& stack) {
push(stack, at::scalar_to_tensor(b));
}
static const std::array<mobile::prim_op_fn_register, 14> op_reg = {
static const C10_UNUSED std::array<mobile::prim_op_fn_register, 14> op_reg = {
mobile::prim_op_fn_register("prim::TupleIndex", tupleIndex),
mobile::prim_op_fn_register("aten::Bool.Tensor", boolTensor),
mobile::prim_op_fn_register("aten::format", aten_format),

3
torch/csrc/jit/passes/pass_manager.cpp
View File

@ -3,6 +3,9 @@
namespace torch {
namespace jit {
// Start UUID at 1
static GraphPassNameType graphPassID = 1;
std::vector<GraphPassEntry>& getCustomPostPasses() {
static std::vector<GraphPassEntry> passes;
return passes;

2
torch/csrc/jit/passes/pass_manager.h
View File

@ -26,8 +26,6 @@ using GraphPass = std::function<void(std::shared_ptr<Graph>&)>;
// Since Passes are std::functions, we associate a UUID to each pass, this way
// if we want to deregister a pass, we have something to reference it by.
using GraphPassNameType = unsigned int;
// Start UUID at 1
static GraphPassNameType graphPassID = 1;
// Graph pass entries have a name associated with them
using GraphPassEntry = std::pair<GraphPass, GraphPassNameType>;

2
torch/csrc/jit/runtime/graph_executor.cpp
View File

@ -158,8 +158,8 @@ struct CaptureList {
case CAPTURE_LIST: {
c10::List<at::Tensor> lst;
auto size = *size_it++;
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
for (const auto i : c10::irange(size)) {
(void)i;
lst.emplace_back(var_capture_it->unpack(saved_for));
var_capture_it++;
}

2
torch/csrc/jit/runtime/register_c10_ops.cpp
View File

@ -54,7 +54,7 @@ Registerer& registerer() {
}
// global instance to run its constructor on startup
Registerer& dummy = registerer();
C10_UNUSED Registerer& dummy = registerer();
} // namespace

2
torch/csrc/jit/runtime/script_profile.cpp
View File

@ -103,7 +103,7 @@ auto initBindings() {
return nullptr;
}
const auto torchBindInitializer = initBindings();
const auto C10_UNUSED torchBindInitializer = initBindings();
} // namespace

2
torch/csrc/jit/tensorexpr/external_functions.cpp
View File

@ -25,8 +25,8 @@ std::vector<at::Tensor> constructTensors(
for (const auto i : c10::irange(bufs_num)) {
buf_data_vec.push_back(buf_data[i]);
buf_dims_vec.emplace_back();
// NOLINTNEXTLINE(clang-diagnostic-unused-variable,clang-analyzer-deadcode.DeadStores)
for (const auto dim : c10::irange(buf_ranks[i])) {
(void)dim;
buf_dims_vec[i].push_back(buf_dims[buf_dims_idx++]);
}
buf_dtypes_vec.push_back(static_cast<c10::ScalarType>(buf_dtypes[i]));

6
torch/csrc/jit/tensorexpr/llvm_codegen.cpp
View File

@ -1151,9 +1151,6 @@ void LLVMCodeGenImpl::visit(LoadPtr v) {
throw std::runtime_error("invalid dtype in Load");
}
// Detect whether the vector mask is all true
bool unmasked_load = true;
// Handle the case where the load is contiguous and unmasked efficiently
auto idx_ramp = to<Ramp>(v->flat_index());
if (idx_ramp) {
@ -1805,9 +1802,6 @@ void LLVMCodeGenImpl::visit(IntrinsicsPtr v) {
}
void LLVMCodeGenImpl::visit(ExternalCallPtr v) {
constexpr int max_buffers = 10;
constexpr int max_dimensions = 40;
auto& func_registry = getNNCFunctionRegistry();
if (!func_registry.count(v->func_name())) {
throw unimplemented_lowering(v);

4
torch/csrc/onnx/onnx.h
View File

@ -19,7 +19,7 @@ enum class TrainingMode {
// We pin IR version instead of using onnx::IR_VERSION so that the
// test_operators.py will be more stable. Only bump it when
// necessary.
static const size_t IR_VERSION = 7;
static const char* PRODUCER_VERSION = "1.11";
constexpr size_t IR_VERSION = 7;
constexpr const char* PRODUCER_VERSION = "1.11";
} // namespace onnx
} // namespace torch

10
torch/csrc/utils/byte_order.cpp
View File

@ -236,8 +236,8 @@ void THP_encodeInt16Buffer(uint8_t* dst, const int16_t* src, THPByteOrder order,
{
memcpy(dst, src, sizeof(int16_t) * len);
if (order != THP_nativeByteOrder()) {
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
for(const auto i : c10::irange(len)) {
(void)i;
swapBytes16(dst);
dst += sizeof(int16_t);
}
@ -248,8 +248,8 @@ void THP_encodeInt32Buffer(uint8_t* dst, const int32_t* src, THPByteOrder order,
{
memcpy(dst, src, sizeof(int32_t) * len);
if (order != THP_nativeByteOrder()) {
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
for(const auto i : c10::irange(len)) {
(void)i;
swapBytes32(dst);
dst += sizeof(int32_t);
}
@ -260,8 +260,8 @@ void THP_encodeInt64Buffer(uint8_t* dst, const int64_t* src, THPByteOrder order,
{
memcpy(dst, src, sizeof(int64_t) * len);
if (order != THP_nativeByteOrder()) {
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
for(const auto i : c10::irange(len)) {
(void)i;
swapBytes64(dst);
dst += sizeof(int64_t);
}
@ -272,8 +272,8 @@ void THP_encodeFloatBuffer(uint8_t* dst, const float* src, THPByteOrder order, s
{
memcpy(dst, src, sizeof(float) * len);
if (order != THP_nativeByteOrder()) {
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
for(const auto i : c10::irange(len)) {
(void)i;
swapBytes32(dst);
dst += sizeof(float);
}
@ -284,8 +284,8 @@ void THP_encodeDoubleBuffer(uint8_t* dst, const double* src, THPByteOrder order,
{
memcpy(dst, src, sizeof(double) * len);
if (order != THP_nativeByteOrder()) {
// NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores,clang-diagnostic-unused-variable)
for(const auto i : c10::irange(len)) {
(void)i;
swapBytes64(dst);
dst += sizeof(double);
}