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[GPU] Fix warnings #16196

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merged 12 commits into from
Mar 16, 2023
Merged

[GPU] Fix warnings #16196

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a5365b9
fix 1
andrei-cv Mar 7, 2023
84d62dd
fix 2-10
andrei-cv Mar 7, 2023
dc33d2b
fixed code style
andrei-cv Mar 9, 2023
2e1fafb
fixed win plugin
andrei-cv Mar 9, 2023
f7458d7
fixed linux plugin
andrei-cv Mar 9, 2023
3ef91fc
fixed a part of tests
andrei-cv Mar 10, 2023
b0fc46f
fixed test fot linux
andrei-cv Mar 10, 2023
de6fad7
fixed pooling_gpu_test fot linux
andrei-cv Mar 10, 2023
4461bfa
fixed pooling_gpu_test fot linux
andrei-cv Mar 10, 2023
2b75a2c
fix after review and enable wd4267 in makefile
andrei-cv Mar 11, 2023
607ffb6
fix after review
andrei-cv Mar 13, 2023
698fa68
errors of unit test are fixed
andrei-cv Mar 14, 2023
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8 changes: 4 additions & 4 deletions src/inference/dev_api/performance_heuristics.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@ static MemBandwidthPressure MemBandwidthPressureTolerance(
const float memThresholdAssumeLimited = MemBandwidthPressure::LIMITED) {
int total_convs = 0, mem_limited_convs = 0, compute_convs = 0, total_gemms = 0, mem_limited_gemms = 0,
total_deconvs = 0, compute_deconvs = 0, mem_limited_deconvs = 0;
auto memLimitedFactor = [&](int size_data_moved, int datatype_size = 4) -> float {
auto memLimitedFactor = [&](size_t size_data_moved, int datatype_size = 4) -> float {
return (cache_size / (size_data_moved * datatype_size));
};
auto isLowPrecision = [&](ngraph::element::Type type) -> bool {
Expand Down Expand Up @@ -57,7 +57,7 @@ static MemBandwidthPressure MemBandwidthPressureTolerance(
const bool isBF16orFP16 = isHalfPrecision(type1);
const int data_type_size = isINT8 ? 1 : isBF16orFP16 ? 2 : 4;

int dataSizeInput = 0, dataSizeOutput = 0;
size_t dataSizeInput = 0, dataSizeOutput = 0;
if (!std::strcmp("MatMul", node_name)) {
const auto input0 = node->input(0);
const auto input1 = node->input(1);
Expand Down Expand Up @@ -103,7 +103,7 @@ static MemBandwidthPressure MemBandwidthPressureTolerance(
std::accumulate(shapeInput.begin(), shapeInput.end(), size_t(1), std::multiplies<size_t>());
dataSizeOutput =
std::accumulate(shapeOutput.begin(), shapeOutput.end(), size_t(1), std::multiplies<size_t>());
const auto factor = memLimitedFactor(dataSizeInput + dataSizeOutput, data_type_size);
const auto factor = memLimitedFactor(static_cast<int>(dataSizeInput + dataSizeOutput), data_type_size);
mem_limited_convs += factor < memThresholdAssumeLimited;
worst_case = std::min(factor, worst_case);
}
Expand All @@ -124,7 +124,7 @@ static MemBandwidthPressure MemBandwidthPressureTolerance(
std::accumulate(shapeInput.begin(), shapeInput.end(), size_t(1), std::multiplies<size_t>());
dataSizeOutput =
std::accumulate(shapeOutput.begin(), shapeOutput.end(), size_t(1), std::multiplies<size_t>());
const auto factor = memLimitedFactor(dataSizeInput + dataSizeOutput, data_type_size);
const auto factor = memLimitedFactor(static_cast<int>(dataSizeInput + dataSizeOutput), data_type_size);
mem_limited_deconvs += factor < memThresholdAssumeLimited;
worst_case = std::min(factor, worst_case);
}
Expand Down
2 changes: 1 addition & 1 deletion src/plugins/intel_gpu/include/intel_gpu/plugin/graph.hpp
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Expand Up @@ -59,7 +59,7 @@ class Graph {
cldnn::engine& get_engine() const { return m_context->get_engine(); }
const ExecutionConfig& get_config() const { return m_config; }

int GetMaxDynamicBatchSize() const { return m_config.get_property(ov::intel_gpu::max_dynamic_batch); }
size_t GetMaxDynamicBatchSize() const { return m_config.get_property(ov::intel_gpu::max_dynamic_batch);}
const std::map<std::string, cldnn::layout>& GetInputLayouts() const { return m_program->GetInputLayouts(); }
const InferenceEngine::InputsDataMap GetNetworkInputs() const { return m_program->GetNetworkInputs(); }
const InferenceEngine::OutputsDataMap GetNetworkOutputs() const { return m_program->GetNetworkOutputs(); }
Expand Down
4 changes: 3 additions & 1 deletion src/plugins/intel_gpu/include/intel_gpu/primitives/arg_max_min.hpp
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Expand Up @@ -103,7 +103,9 @@ struct arg_max_min : public primitive_base<arg_max_min> {
values_first == rhs_casted.values_first;
}

uint32_t get_output_nums() const { return (input_size() == 3 ? 2 : output_size()); }
size_t get_output_nums() const {
return (input_size() == 3 ? 2 : output_size());
}
bool has_second_output() const { return get_output_nums() == 2; }
bool use_multiple_outputs() const { return input_size() != 3; }

Expand Down
2 changes: 1 addition & 1 deletion src/plugins/intel_gpu/src/graph/broadcast.cpp
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Expand Up @@ -88,7 +88,7 @@ std::vector<layout> broadcast_inst::calc_output_layouts(broadcast_node const& /*
if (input1.is_static()) {
output_rank = input1.get_dim(0); // target shape rank is set as second input.
}
output_shapes[0] = ShapeType::dynamic(std::max(output_rank, static_cast<int>(1)));
output_shapes[0] = ShapeType::dynamic(std::max(output_rank, 1));
}

format output_format = format::adjust_to_rank(input0_layout.format, output_shapes[0].size());
Expand Down
24 changes: 12 additions & 12 deletions src/plugins/intel_gpu/src/graph/convolution.cpp
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Expand Up @@ -42,13 +42,13 @@ layout convolution_inst::calc_output_layout(convolution_node const& node, kernel
output_type = data_types::f32;
}

uint32_t stride_z = stride.size() >= 3 ? stride[stride.size() - 3] : 1;
uint32_t stride_y = stride.size() >= 2 ? stride[stride.size() - 2] : 1;
uint32_t stride_x = stride.size() >= 1 ? stride[stride.size() - 1] : 1;
auto stride_z = stride.size() >= 3 ? stride[stride.size() - 3] : 1;
auto stride_y = stride.size() >= 2 ? stride[stride.size() - 2] : 1;
auto stride_x = stride.size() >= 1 ? stride[stride.size() - 1] : 1;

uint32_t dilation_z = dilation.size() >= 3 ? dilation[dilation.size() - 3] : 1;
uint32_t dilation_y = dilation.size() >= 2 ? dilation[dilation.size() - 2] : 1;
uint32_t dilation_x = dilation.size() >= 1 ? dilation[dilation.size() - 1] : 1;
auto dilation_z = dilation.size() >= 3 ? dilation[dilation.size() - 3] : 1;
auto dilation_y = dilation.size() >= 2 ? dilation[dilation.size() - 2] : 1;
auto dilation_x = dilation.size() >= 1 ? dilation[dilation.size() - 1] : 1;

// TODO: Consider moving general parameter verification to arguments constructor.
CLDNN_ERROR_LESS_OR_EQUAL_THAN(desc->id,
Expand Down Expand Up @@ -249,13 +249,13 @@ std::vector<layout> convolution_inst::calc_output_layouts(convolution_node const
output_type = data_types::f32;
}

uint32_t stride_z = stride.size() >= 3 ? stride[stride.size() - 3] : 1;
uint32_t stride_y = stride.size() >= 2 ? stride[stride.size() - 2] : 1;
uint32_t stride_x = stride.size() >= 1 ? stride[stride.size() - 1] : 1;
auto stride_z = stride.size() >= 3 ? stride[stride.size() - 3] : 1;
auto stride_y = stride.size() >= 2 ? stride[stride.size() - 2] : 1;
auto stride_x = stride.size() >= 1 ? stride[stride.size() - 1] : 1;

uint32_t dilation_z = dilation.size() >= 3 ? dilation[dilation.size() - 3] : 1;
uint32_t dilation_y = dilation.size() >= 2 ? dilation[dilation.size() - 2] : 1;
uint32_t dilation_x = dilation.size() >= 1 ? dilation[dilation.size() - 1] : 1;
auto dilation_z = dilation.size() >= 3 ? dilation[dilation.size() - 3] : 1;
auto dilation_y = dilation.size() >= 2 ? dilation[dilation.size() - 2] : 1;
auto dilation_x = dilation.size() >= 1 ? dilation[dilation.size() - 1] : 1;

// TODO: Consider moving general parameter verification to arguments constructor.
CLDNN_ERROR_LESS_OR_EQUAL_THAN(desc->id,
Expand Down
9 changes: 6 additions & 3 deletions src/plugins/intel_gpu/src/graph/deconvolution.cpp
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Expand Up @@ -80,14 +80,17 @@ layout deconvolution_inst::calc_output_layout(deconvolution_node const& node, ke
3,
"As for now, deconvolutions with more than 3 dimensions are not supported");

int32_t x = off_factor * pad[pad.size() - 1] + (input_layout.spatial(0) - 1) * strd[strd.size() - 1] + weights_layout.spatial(0);
int32_t x = static_cast<int32_t>(
off_factor * pad[pad.size() - 1] + (input_layout.spatial(0) - 1) * strd[strd.size() - 1] + weights_layout.spatial(0));
int32_t y = 1;
if (spatial_dims > 1) {
y = off_factor * pad[pad.size() - 2] + (input_layout.spatial(1) - 1) * strd[strd.size() - 2] + weights_layout.spatial(1);
y = static_cast<int32_t>(
off_factor * pad[pad.size() - 2] + (input_layout.spatial(1) - 1) * strd[strd.size() - 2] + weights_layout.spatial(1));
}
int32_t z = 1;
if (spatial_dims > 2) {
z = off_factor * pad[pad.size() - 3] + (input_layout.spatial(2) - 1) * strd[strd.size() - 3] + weights_layout.spatial(2);
z = static_cast<int32_t>(
off_factor * pad[pad.size() - 3] + (input_layout.spatial(2) - 1) * strd[strd.size() - 3] + weights_layout.spatial(2));
}

tensor output_size(input_layout.batch(),
Expand Down
2 changes: 1 addition & 1 deletion src/plugins/intel_gpu/src/graph/fully_connected.cpp
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Expand Up @@ -107,7 +107,7 @@ layout fully_connected_inst::calc_output_layout(fully_connected_node const& node

auto reshape_to_2d = [](const ov::PartialShape& shape, int64_t feature) {
auto staticShape = shape.to_shape();
size_t total = std::accumulate(staticShape.begin(), staticShape.end(), 1, std::multiplies<size_t>());
size_t total = std::accumulate(staticShape.begin(), staticShape.end(), static_cast<size_t>(1), std::multiplies<size_t>());
std::vector<int64_t> reshapeSize = { static_cast<int64_t>(total) / feature, feature };
return reshapeSize;
};
Expand Down
5 changes: 4 additions & 1 deletion src/plugins/intel_gpu/src/graph/gather.cpp
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Expand Up @@ -17,7 +17,10 @@ layout gather_inst::calc_output_layout(gather_node const& node, kernel_impl_para
auto desc = impl_param.typed_desc<gather>();

auto input_layout = impl_param.get_input_layout();
std::vector<tensor::value_type> dims_converted(desc->output_shape.begin(), desc->output_shape.end());
std::vector<tensor::value_type> dims_converted;
for (auto dim : desc->output_shape) {
dims_converted.push_back(static_cast<tensor::value_type>(dim));
}
// extend shape to 4d
for (size_t i = dims_converted.size(); i < 4; i++)
dims_converted.push_back(1);
Expand Down
4 changes: 2 additions & 2 deletions src/plugins/intel_gpu/src/graph/graph_optimizer/pre_replace_deconv.cpp
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Expand Up @@ -225,7 +225,7 @@ void pre_replace_deconv::run(program& p) {
p.rename(deconv_node, rename_id);

// reshape weights
int pixel_shuffle_size = scale_factor * scale_factor;
auto pixel_shuffle_size = static_cast<tensor::value_type>(scale_factor * scale_factor);
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int kernel_size = 5;
tensor target_weights_size = { pixel_shuffle_size, filter_layout.feature(), kernel_size, kernel_size };
auto target_weights_layout = layout{ weights_layout.data_type, weights_layout.format, target_weights_size };
Expand All @@ -252,7 +252,7 @@ void pre_replace_deconv::run(program& p) {
static_cast<int>(filter_layout.feature()),
static_cast<int>(filter_layout.spatial(0)),
static_cast<int>(filter_layout.spatial(1)),
scale_factor,
static_cast<int>(scale_factor),
subpixel_weights);

if (weights_data_type == data_types::f16) {
Expand Down
26 changes: 16 additions & 10 deletions src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_padding.cpp
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Expand Up @@ -102,7 +102,7 @@ void prepare_padding::run(program& p) {
// WA for this format. sliding window needs to be fixed --perf degradation for IncepctionV1 type models
tensor size(1);
for (size_t i = 0; i < prim->size.size(); i++) {
size.spatial[i] = prim->size[prim->size.size() - i - 1];
size.spatial[i] = static_cast<tensor::value_type>(prim->size[prim->size.size() - i - 1]);
}

if (node->get_output_layout().format == format::b_fs_yx_fsv16)
Expand Down Expand Up @@ -183,13 +183,13 @@ void prepare_padding::run(program& p) {
auto pad = conv->pad;
auto stride = conv->stride;
auto dilation = conv->dilation;
uint32_t stride_z = stride.size() >= 3 ? stride[stride.size() - 3] : 1;
uint32_t stride_y = stride.size() >= 2 ? stride[stride.size() - 2] : 1;
uint32_t stride_x = stride.size() >= 1 ? stride[stride.size() - 1] : 1;
uint32_t stride_z = stride.size() >= 3 ? static_cast<uint32_t>(stride[stride.size() - 3]) : 1;
uint32_t stride_y = stride.size() >= 2 ? static_cast<uint32_t>(stride[stride.size() - 2]) : 1;
uint32_t stride_x = stride.size() >= 1 ? static_cast<uint32_t>(stride[stride.size() - 1]) : 1;

uint32_t dilation_z = dilation.size() >= 3 ? dilation[dilation.size() - 3] : 1;
uint32_t dilation_y = dilation.size() >= 2 ? dilation[dilation.size() - 2] : 1;
uint32_t dilation_x = dilation.size() >= 1 ? dilation[dilation.size() - 1] : 1;
uint32_t dilation_z = dilation.size() >= 3 ? static_cast<uint32_t>(dilation[dilation.size() - 3]) : 1;
uint32_t dilation_y = dilation.size() >= 2 ? static_cast<uint32_t>(dilation[dilation.size() - 2]) : 1;
uint32_t dilation_x = dilation.size() >= 1 ? static_cast<uint32_t>(dilation[dilation.size() - 1]) : 1;

tensor::value_type pad_z = pad.size() >= 3 ? pad[pad.size() - 3] : 0;
tensor::value_type pad_y = pad.size() >= 2 ? pad[pad.size() - 2] : 0;
Expand Down Expand Up @@ -277,9 +277,15 @@ void prepare_padding::run(program& p) {
auto padding_begin_x = std::max<tensor::value_type>(pad_x, 0);
auto padding_begin_y = std::max<tensor::value_type>(pad_y, 0);
auto padding_begin_z = std::max<tensor::value_type>(pad_z, 0);
auto padding_end_x = std::max<tensor::value_type>(input_limit_x - prev_prim_output_layout.spatial(0), 0);
auto padding_end_y = std::max<tensor::value_type>(input_limit_y - prev_prim_output_layout.spatial(1), 0);
auto padding_end_z = std::max<tensor::value_type>(input_limit_z - prev_prim_output_layout.spatial(2), 0);
auto padding_end_x = std::max<tensor::value_type>(
static_cast<tensor::value_type>(input_limit_x) - prev_prim_output_layout.spatial(0),
0);
auto padding_end_y = std::max<tensor::value_type>(
static_cast<tensor::value_type>(input_limit_y) - prev_prim_output_layout.spatial(1),
0);
auto padding_end_z = std::max<tensor::value_type>(
static_cast<tensor::value_type>(input_limit_z) - prev_prim_output_layout.spatial(2),
0);

cldnn::padding needed_padding({0, 0, padding_begin_x, padding_begin_y, padding_begin_z}, {0, 0, padding_end_x, padding_end_y, padding_end_z}, 0);
needed_padding = padding::max(prev_prim_output_layout.data_padding, needed_padding);
Expand Down
2 changes: 1 addition & 1 deletion src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_primitive_fusing.cpp
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Expand Up @@ -275,7 +275,7 @@ void prepare_primitive_fusing::fuse_bias(program &p) {
for (size_t i = 0; i < const_shape.size(); ++i) {
if (const_shape[i] != 1) {
count_elements_not_one++;
idx_element_not_one = i;
idx_element_not_one = static_cast<int32_t>(i);
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}
if (count_elements_not_one > 1)
break;
Expand Down
2 changes: 1 addition & 1 deletion src/plugins/intel_gpu/src/graph/impls/ocl/arg_max_min.cpp
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Expand Up @@ -66,7 +66,7 @@ struct arg_max_min_impl : typed_primitive_impl_ocl<arg_max_min> {
const auto& mode = primitive->mode;
const auto& sort_type = primitive->sort;
const auto& values_first = primitive->values_first;
const auto& outputs_num = (primitive->input_size() == 3 ? 2 : primitive->output_size());
const auto& outputs_num = primitive->input_size() == 3 ? 2 : primitive->output_size();

auto argm_params = get_default_params<kernel_selector::arg_max_min_params>(impl_param);
auto argm_optional_params =
Expand Down
12 changes: 6 additions & 6 deletions src/plugins/intel_gpu/src/graph/impls/ocl/binary_convolution.cpp
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Expand Up @@ -59,14 +59,14 @@ struct binary_convolution_impl : typed_primitive_impl_ocl<binary_convolution> {
uint32_t pad_x = std::max<std::ptrdiff_t>(pad.size() >= 1 ? pad[pad.size() - 1] : 0, 0);
params.padding = {pad_x, pad_y, pad_z};

uint32_t stride_z = stride.size() >= 3 ? stride[stride.size() - 3] : 1;
uint32_t stride_y = stride.size() >= 2 ? stride[stride.size() - 2] : 1;
uint32_t stride_x = stride.size() >= 1 ? stride[stride.size() - 1] : 1;
uint32_t stride_z = stride.size() >= 3 ? static_cast<uint32_t>(stride[stride.size() - 3]) : 1;
uint32_t stride_y = stride.size() >= 2 ? static_cast<uint32_t>(stride[stride.size() - 2]) : 1;
uint32_t stride_x = stride.size() >= 1 ? static_cast<uint32_t>(stride[stride.size() - 1]) : 1;
params.stride = {stride_x, stride_y, stride_z};

uint32_t dilation_z = dilation.size() >= 3 ? dilation[dilation.size() - 3] : 1;
uint32_t dilation_y = dilation.size() >= 2 ? dilation[dilation.size() - 2] : 1;
uint32_t dilation_x = dilation.size() >= 1 ? dilation[dilation.size() - 1] : 1;
uint32_t dilation_z = dilation.size() >= 3 ? static_cast<uint32_t>(dilation[dilation.size() - 3]) : 1;
uint32_t dilation_y = dilation.size() >= 2 ? static_cast<uint32_t>(dilation[dilation.size() - 2]) : 1;
uint32_t dilation_x = dilation.size() >= 1 ? static_cast<uint32_t>(dilation[dilation.size() - 1]) : 1;
params.dilation = {dilation_x, dilation_y, dilation_z};

return {params, optional_params};
Expand Down
4 changes: 2 additions & 2 deletions src/plugins/intel_gpu/src/graph/impls/ocl/concatenation.cpp
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Expand Up @@ -13,8 +13,8 @@ namespace ocl {

namespace {
kernel_selector::concat_axis convert_axis(int64_t axis, size_t rank) {
unsigned cldnn_axis = axis >= 0 ? axis : axis + static_cast<int64_t>(rank);
if (cldnn_axis >= rank)
auto cldnn_axis = axis >= 0 ? axis : axis + static_cast<int64_t>(rank);
if (cldnn_axis >= static_cast<int64_t>(rank))
IE_THROW() << "Concatenation axis exceeds number of dimensions";

// Difference in dimension ordering between IE and GPU plugin,
Expand Down
12 changes: 6 additions & 6 deletions src/plugins/intel_gpu/src/graph/impls/ocl/convolution.cpp
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Expand Up @@ -78,14 +78,14 @@ struct convolution_impl : typed_primitive_impl_ocl<convolution> {
uint32_t pad_x = std::max<std::ptrdiff_t>(pad.size() >= 1 ? pad[pad.size() - 1] : 0, 0);
conv_params.padding = {pad_x, pad_y, pad_z};

uint32_t stride_z = stride.size() >= 3 ? stride[stride.size() - 3] : 1;
uint32_t stride_y = stride.size() >= 2 ? stride[stride.size() - 2] : 1;
uint32_t stride_x = stride.size() >= 1 ? stride[stride.size() - 1] : 1;
uint32_t stride_z = stride.size() >= 3 ? static_cast<uint32_t>(stride[stride.size() - 3]) : 1;
uint32_t stride_y = stride.size() >= 2 ? static_cast<uint32_t>(stride[stride.size() - 2]) : 1;
uint32_t stride_x = stride.size() >= 1 ? static_cast<uint32_t>(stride[stride.size() - 1]) : 1;
conv_params.stride = {stride_x, stride_y, stride_z};

uint32_t dilation_z = dilation.size() >= 3 ? dilation[dilation.size() - 3] : 1;
uint32_t dilation_y = dilation.size() >= 2 ? dilation[dilation.size() - 2] : 1;
uint32_t dilation_x = dilation.size() >= 1 ? dilation[dilation.size() - 1] : 1;
uint32_t dilation_z = dilation.size() >= 3 ? static_cast<uint32_t>(dilation[dilation.size() - 3]) : 1;
uint32_t dilation_y = dilation.size() >= 2 ? static_cast<uint32_t>(dilation[dilation.size() - 2]) : 1;
uint32_t dilation_x = dilation.size() >= 1 ? static_cast<uint32_t>(dilation[dilation.size() - 1]) : 1;
conv_params.dilation = {dilation_x, dilation_y, dilation_z};

if ((impl_param.input_layouts[0].data_type == data_types::u8 ||
Expand Down
2 changes: 1 addition & 1 deletion src/plugins/intel_gpu/src/graph/impls/ocl/crop.cpp
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Expand Up @@ -45,7 +45,7 @@ struct crop_impl : typed_primitive_impl_ocl<crop> {
auto runtime_offset = convert_data_tensor(impl_param.get_input_layout(), impl_param.input_offsets[0]).GetFirstElementOffset();
kernel_selector::ScalarDescriptor s;
s.t = kernel_selector::ScalarDescriptor::Types::UINT32;
s.v.u32 = runtime_offset;
s.v.u32 = static_cast<uint32_t>(runtime_offset);
OPENVINO_ASSERT(_kernel_data.kernels[0].params.scalars.size() == 1,
"[GPU] Scalar field for runtime offset is not added for crop shape agnostic impl");
_kernel_data.kernels[0].params.scalars[0] = s;
Expand Down
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