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remove debug

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This commit is contained in:
Evgeny Kotov 2023-06-19 15:22:22 +02:00
parent 2711af22ed
commit 574a3a04f3
8 changed files with 8 additions and 120 deletions
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30
src/plugins/intel_gna/src/debug_new_pass.cpp
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@ -1,30 +0,0 @@
// Copyright (C) 2023 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include "debug_new_pass.hpp"
#ifdef DEBUG_VISUALIZE
# include "ngraph/pass/visualize_tree.hpp" // DEBUG
//#include "openvino/pass/serialize.hpp" // DEBUG
# include <sstream>
#endif
namespace intel_gna_debug {
void DebugVisualize(ov::pass::Manager& manager, const std::string& name) {
#ifdef DEBUG_VISUALIZE
static unsigned counter = 0;
std::stringstream ss;
# ifdef DEBUG_VISUALIZETREE
ss << counter << "_" << name << ".png";
manager.register_pass<ov::pass::VisualizeTree>(ss.str());
# else
ss << counter << "_" << name;
manager.register_pass<ov::pass::Serialize>(ss.str() + ".xml", ss.str() + ".bin");
# endif
++counter;
#endif
}
} // namespace intel_gna_debug

41
src/plugins/intel_gna/src/debug_new_pass.hpp
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@ -1,41 +0,0 @@
// Copyright (C) 2023 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#pragma once
#include <iostream>
#undef DEBUG_USE_NEW_PASS
#define DEBUG_USE_NEW_PASS 1
#undef DEBUG_VISUALIZE
//#define DEBUG_VISUALIZE 1
#undef DEBUG_VISUALIZETREE
#define DEBUG_VISUALIZETREE 1
#define EMUTEX_DEBUG_CHECKPOINT std::cout << "[EMUTEX DEBUG] CHECKPOINT " << __FILE__ << ":" << __LINE__ << std::endl;
#define EMUTEX_DEBUG_CHECKPOINT_MESSAGE(message) \
std::cout << "[EMUTEX DEBUG] CHECKPOINT " << __FILE__ << ":" << __LINE__ << " " << message << std::endl;
#define EMUTEX_DEBUG_VALUE(value) \
std::cout << "[EMUTEX DEBUG] " << __FILE__ << ":" << __LINE__ << " " << #value << " = " << value << std::endl;
#include "openvino/pass/manager.hpp"
namespace intel_gna_debug {
void DebugVisualize(ov::pass::Manager& manager, const std::string& name);
} // namespace intel_gna_debug
template <typename T>
std::ostream& operator<<(std::ostream& os, const std::vector<T>& vec) {
os << "{";
for (size_t i = 0; i < vec.size(); ++i) {
if (i)
os << ", ";
os << i;
}
os << "}";
return os;
}
#define EMUTEX_DEBUG_VISUALIZE(name) intel_gna_debug::DebugVisualize(manager, name);

5
src/plugins/intel_gna/src/frontend/layer_quantizer.cpp
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@ -6,7 +6,6 @@
#include "backend/gna_types.hpp"
#include "common/gna_target.hpp"
#include "debug_new_pass.hpp" // DEBUG
#include "gna_graph_tools.hpp"
#include "weights_converter.hpp"
@ -91,11 +90,7 @@ size_t LayerQuantizer::GetBiasSizeForLayer(InferenceEngine::WeightableLayer& wl)
return wl._biases->size();
} else if (LayerInfo(wl).isConvolution()) {
// Calculating biases len using outdata dims: biases number should be equal to output channels number
#ifndef DEBUG_USE_NEW_PASS
return InferenceEngine::GetDataDimByName(wl.outData.front(), InferenceEngine::DataDimName::C);
#else
return InferenceEngine::GetDataDimSizeNHWC(wl.outData.front(), InferenceEngine::DataDimName::C);
#endif
} else {
// Calculating biases size using outData dimensions
return wl.outData.front()->getDims().back();

1
src/plugins/intel_gna/src/frontend/scale_factor_calc.cpp
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@ -5,7 +5,6 @@
#include "scale_factor_calc.hpp"
#include "common/numerical_utils.hpp"
#include "debug_new_pass.hpp" // DEBUG
#include "gna_slope_scale.hpp"
#include "gna_upstream_iterator.hpp"
#include "layer_quantizer.hpp"

48
src/plugins/intel_gna/src/gna_graph_compiler.cpp
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@ -24,7 +24,6 @@
#include "backend/gna_limitations.hpp"
#include "caseless.hpp"
#include "common/numerical_utils.hpp"
#include "debug_new_pass.hpp"
#include "descriptions/gna_desc.hpp"
#include "frontend/layer_quantizer.hpp"
#include "frontend/scale_factor_calc.hpp"
@ -313,7 +312,6 @@ void GNAGraphCompiler::assertConvolutionLayoutProper(const InferenceEngine::Data
}
}
#ifdef DEBUG_USE_NEW_PASS
namespace {
template <typename T>
@ -327,7 +325,6 @@ PropertyVector<T> PropertyVectorAppend(PropertyVector<T> properties, T value) {
}
} // namespace
#endif
/**
* Create AMIntelDNN Convolutional1DComponent from ConvolutionLayer
@ -354,7 +351,6 @@ void GNAGraphCompiler::ConvolutionPrimitive(InferenceEngine::CNNLayerPtr layer)
const auto outputs = layer->outData.front();
assertConvolutionLayoutProper(inputs);
#ifdef DEBUG_USE_NEW_PASS
const auto in_batch = GetDataDimSizeNHWC(inputs, InferenceEngine::DataDimName::N);
const auto in_channels = GetDataDimSizeNHWC(inputs, InferenceEngine::DataDimName::C);
auto in_height = GetDataDimSizeNHWC(inputs, InferenceEngine::DataDimName::H);
@ -374,18 +370,6 @@ void GNAGraphCompiler::ConvolutionPrimitive(InferenceEngine::CNNLayerPtr layer)
convolution._pads_end = PropertyVectorAppend<unsigned int>(convolution._pads_end, 0);
}
#else
const auto in_batch = InferenceEngine::GetDataDimByName(inputs, InferenceEngine::DataDimName::N);
const auto in_channels = InferenceEngine::GetDataDimByName(inputs, InferenceEngine::DataDimName::C);
auto in_height = InferenceEngine::GetDataDimByName(inputs, InferenceEngine::DataDimName::H);
auto in_width = InferenceEngine::GetDataDimByName(inputs, InferenceEngine::DataDimName::W);
const auto out_batch = InferenceEngine::GetDataDimByName(outputs, InferenceEngine::DataDimName::N);
const auto out_channels = InferenceEngine::GetDataDimByName(outputs, InferenceEngine::DataDimName::C);
auto out_height = InferenceEngine::GetDataDimByName(outputs, InferenceEngine::DataDimName::H);
auto out_width = InferenceEngine::GetDataDimByName(outputs, InferenceEngine::DataDimName::W);
#endif
if (in_height > 1 && in_width == 1 && !ShouldUseOnlyConv2DGnaIface()) {
std::swap(in_height, in_width);
std::swap(out_height, out_width);
@ -627,34 +611,28 @@ void GNAGraphCompiler::finalizeConvolution1DPrimitive(InferenceEngine::CNNLayerP
});
}
#ifndef DEBUG_USE_NEW_PASS
#endif
connectOutput(layer, ptr_outputs, num_data_bytes_out);
// Transpose H with W or C with HW
auto A = transpose_h_w ? in_kernel_h : in_channels;
auto B = transpose_h_w ? in_kernel_w : convolution._kernel[X_AXIS];
std::vector<uint8_t> transposedWeights;
std::vector<uint8_t> copiedWeights;
for (uint32_t k = 0; k < num_filters; k++) {
uint8_t* ptr_filt_current =
convolution._weights->cbuffer().as<uint8_t*>() + k * A * B * convolution.precision.size();
#ifdef DEBUG_USE_NEW_PASS
auto transposedPart = copyMatrix(ptr_filt_current, convolution.precision.size(), A, B);
#else
auto transposedPart = transposeMatrix(ptr_filt_current, convolution.precision.size(), A, B);
#endif
transposedWeights.insert(transposedWeights.end(), transposedPart.begin(), transposedPart.end());
auto copieddPart = copyMatrix(ptr_filt_current, convolution.precision.size(), A, B);
copiedWeights.insert(copiedWeights.end(), copieddPart.begin(), copieddPart.end());
}
if (transposedWeights.size() != convolution._weights->byteSize()) {
THROW_GNA_LAYER_EXCEPTION(&convolution) << "weights was transposed incorrectly. " << transposedWeights.size()
if (copiedWeights.size() != convolution._weights->byteSize()) {
THROW_GNA_LAYER_EXCEPTION(&convolution) << "weights was transposed incorrectly. " << copiedWeights.size()
<< ' ' << convolution._weights->byteSize();
}
if (num_conv_kernel_padding == 0) {
gnamem->getQueue(REGION_RO)->push_local_ptr(layer,
ptr_weights,
transposedWeights.data(),
copiedWeights.data(),
convolution._weights->byteSize());
} else {
auto paddedWeights = num_filter_coefficients * num_filters;
@ -666,7 +644,7 @@ void GNAGraphCompiler::finalizeConvolution1DPrimitive(InferenceEngine::CNNLayerP
layerName,
num_conv_kernel_padding,
cpSize,
transposedWeights,
copiedWeights,
num_filters,
single_conv_kernel_size](void* data, std::size_t size) {
if (paddedWeightsSize > size) {
@ -678,7 +656,7 @@ void GNAGraphCompiler::finalizeConvolution1DPrimitive(InferenceEngine::CNNLayerP
for (int i = 0; i < num_filters; i++) {
ie_memcpy(dstPtr + offset,
size - offset,
transposedWeights.data() + single_conv_kernel_size * i * cpSize,
copiedWeights.data() + single_conv_kernel_size * i * cpSize,
single_conv_kernel_size * cpSize);
offset += single_conv_kernel_size * cpSize;
ie_memcpy(dstPtr + offset, size - offset, &padding_zeros[0], padding_zeros.size());
@ -1010,7 +988,6 @@ void GNAGraphCompiler::PoolingPrimitive(InferenceEngine::CNNLayerPtr layer) {
auto inputs = layer->insData.begin()->lock();
auto outputs = *layer->outData.begin();
#ifdef DEBUG_USE_NEW_PASS
uint32_t w_dim_in = GetDataDimSizeNHWC(inputs, InferenceEngine::DataDimName::W);
uint32_t h_dim_in = GetDataDimSizeNHWC(inputs, InferenceEngine::DataDimName::H);
const uint32_t c_dim_in = GetDataDimSizeNHWC(inputs, InferenceEngine::DataDimName::C);
@ -1024,15 +1001,6 @@ void GNAGraphCompiler::PoolingPrimitive(InferenceEngine::CNNLayerPtr layer) {
pooling._kernel = PropertyVectorAppend<unsigned int>(pooling._kernel, 1);
pooling._stride = PropertyVectorAppend<unsigned int>(pooling._stride, 1);
}
#else
uint32_t w_dim_in = InferenceEngine::GetDataDimByName(inputs, InferenceEngine::DataDimName::W);
uint32_t h_dim_in = InferenceEngine::GetDataDimByName(inputs, InferenceEngine::DataDimName::H);
const uint32_t c_dim_in = InferenceEngine::GetDataDimByName(inputs, InferenceEngine::DataDimName::C);
uint32_t w_dim_out = InferenceEngine::GetDataDimByName(outputs, InferenceEngine::DataDimName::W);
uint32_t h_dim_out = InferenceEngine::GetDataDimByName(outputs, InferenceEngine::DataDimName::H);
const uint32_t c_dim_out = InferenceEngine::GetDataDimByName(outputs, InferenceEngine::DataDimName::C);
#endif
void* ptr_inputs = nullptr;
void* ptr_outputs = nullptr;

1
src/plugins/intel_gna/src/gna_transformations_pipeline.cpp
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@ -4,7 +4,6 @@
#include "gna_transformations_pipeline.hpp"
#include "debug_new_pass.hpp"
#include "gna_itt.hpp"
#include "legacy/net_pass.h"
#include "legacy/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.hpp"

1
src/plugins/intel_gna/src/transformations/gather_sinking_fuse.cpp
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@ -8,7 +8,6 @@
#include <transformations/utils/utils.hpp>
#include <utility>
#include "../debug_new_pass.hpp"
#include "openvino/opsets/opset9.hpp"
#include "openvino/pass/pattern/op/wrap_type.hpp"
#include "transformations/rt_info/gather_sinking_attr.hpp"

1
src/plugins/intel_gna/src/transformations/ts_concat.cpp
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@ -6,7 +6,6 @@
#include <openvino/cc/ngraph/itt.hpp>
#include "../debug_new_pass.hpp"
#include "backend/gna_limitations.hpp"
#include "common/graph_utils.hpp"
#include "openvino/core/rt_info.hpp"