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tests fixes + windows build (#7)
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* scalar fix

* int8 fix

* windows build fix

* transpose + small fixes
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Maxim Andronov committed Jul 20, 2021
1 parent 3819fe5 commit 7c3865d
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Showing 36 changed files with 178 additions and 160 deletions.
2 changes: 1 addition & 1 deletion inference-engine/src/mkldnn_plugin/cpu_blocked_memory_desc.h
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Expand Up @@ -16,7 +16,7 @@ class BlockedMemoryDesc : public MemoryDesc {
const std::vector<size_t>& strides = {});

MemoryDescPtr clone() const override {
return make_unique<BlockedMemoryDesc>(*this);
return MKLDNNPlugin::make_unique<BlockedMemoryDesc>(*this);
}

bool isDefined() const override;
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25 changes: 20 additions & 5 deletions inference-engine/src/mkldnn_plugin/cpu_memory_desc_utils.cpp
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Expand Up @@ -134,6 +134,21 @@ MKLDNNMemoryDesc MemoryDescUtils::convertToMKLDNNMemoryDesc(const MemoryDesc& de

MKLDNNMemoryDesc MemoryDescUtils::convertToMKLDNNMemoryDesc(const BlockedMemoryDesc& desc) {
dnnl_memory_desc_t mkldnnDesc;

// scalar case
if (desc.getShape().getRank() == 0) {
mkldnn::memory::desc convertedDesc;
convertedDesc.data.format_kind = dnnl_blocked;
convertedDesc.data.data_type = memory::convert_to_c(MKLDNNMemory::convertToDataType(desc.getPrecision()));
convertedDesc.data.ndims = 1;
convertedDesc.data.dims[0] = 1;
convertedDesc.data.padded_dims[0] = 1;
convertedDesc.data.format_desc.blocking.strides[0] = 1;
convertedDesc.data.padded_offsets[0] = 0;
convertedDesc.data.offset0 = desc.getOffsetPadding();
return MKLDNNMemoryDesc(convertedDesc);
}

auto dims = desc.getShape().getStaticDims();

auto ie_blkdDims = desc.getBlockDims();
Expand Down Expand Up @@ -332,11 +347,11 @@ BlockedMemoryDesc MemoryDescUtils::convertToBlockedDescriptor(const MemoryDesc &

MemoryDescPtr MemoryDescUtils::applyUndefinedOffset(const MKLDNNMemoryDesc& desc) {
if (desc.getFormatKind() != dnnl_format_kind_t::dnnl_blocked)
return make_unique<MKLDNNMemoryDesc>(desc);
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(desc);

mkldnn::memory::desc retDesc = desc;
retDesc.data.offset0 = Shape::UNDEFINED_DIM;
return make_unique<MKLDNNMemoryDesc>(retDesc);
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(retDesc);
}

MemoryDescPtr MemoryDescUtils::applyUndefinedOffset(const BlockedMemoryDesc &desc) {
Expand All @@ -347,20 +362,20 @@ MemoryDescPtr MemoryDescUtils::applyUndefinedOffset(const BlockedMemoryDesc &des
offsetPaddingToData.resize(desc.getBlockDims().size(), 0);
size_t offsetPadding = Shape::UNDEFINED_DIM;

return make_unique<BlockedMemoryDesc>(desc.getPrecision(), desc.getShape().getDims(), desc.getBlockDims(),
return MKLDNNPlugin::make_unique<BlockedMemoryDesc>(desc.getPrecision(), desc.getShape().getDims(), desc.getBlockDims(),
desc.getOrder(), offsetPadding, offsetPaddingToData, strides);
}

MemoryDescPtr MemoryDescUtils::resetOffset(const MemoryDesc* desc) {
if (MemoryDescType::Blocked == desc->getType()) {
auto blockedDesc = desc->as<BlockedMemoryDesc>();
return make_unique<BlockedMemoryDesc>(blockedDesc->getPrecision(), blockedDesc->getShape().getDims(),
return MKLDNNPlugin::make_unique<BlockedMemoryDesc>(blockedDesc->getPrecision(), blockedDesc->getShape().getDims(),
blockedDesc->getBlockDims(), blockedDesc->getOrder());
} else if (MemoryDescType::Mkldnn == desc->getType()) {
auto mkldnnDesc = desc->as<MKLDNNMemoryDesc>();
mkldnn::memory::desc retDesc = *mkldnnDesc;
retDesc.data.offset0 = 0;
return make_unique<MKLDNNMemoryDesc>(retDesc);
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(retDesc);
}
return desc->clone();
}
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2 changes: 1 addition & 1 deletion inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
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Expand Up @@ -1689,7 +1689,7 @@ void MKLDNNGraphOptimizer::MergeTransposeAndReorder(MKLDNNGraph &graph) {
}

auto& transposeOrder = transposeNode->getOrder();
auto &layoutOrder = MemoryDescUtils::convertToBlockedDescriptor(
auto layoutOrder = MemoryDescUtils::convertToBlockedDescriptor(
*transposeNode->getSelectedPrimitiveDescriptor()->getConfig().outConfs[0].desc).getOrder();

auto inBlockedDesc = MemoryDescUtils::convertToBlockedDescriptor(*reorderNode->getSelectedPrimitiveDescriptor()->getConfig().inConfs[0].desc);
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2 changes: 1 addition & 1 deletion inference-engine/src/mkldnn_plugin/mkldnn_memory.h
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Expand Up @@ -87,7 +87,7 @@ class MKLDNNMemoryDesc : public MemoryDesc {
}

std::unique_ptr<MemoryDesc> clone() const override {
return make_unique<MKLDNNMemoryDesc>(*this);
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(*this);
}

bool checkGeneralLayout(GeneralLayout layoutType) const override;
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4 changes: 2 additions & 2 deletions inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
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Expand Up @@ -1041,7 +1041,7 @@ bool MKLDNNNode::isConfigDefined(const NodeConfig &config) const {
}

std::unique_ptr<MKLDNNMemoryDesc> MKLDNNNode::getSrcMemDesc(mkldnn::primitive_desc_iterator &primitive_desc_it, size_t idx) {
return make_unique<MKLDNNMemoryDesc>(primitive_desc_it.src_desc(idx));
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(primitive_desc_it.src_desc(idx));
// TODO [DS]: uncomment or remove
// InferenceEngine::TensorDesc desc = MKLDNNMemoryDesc(primitive_desc_it.src_desc(idx));
// if (desc.getLayout() == InferenceEngine::Layout::ANY)
Expand All @@ -1055,7 +1055,7 @@ std::unique_ptr<MKLDNNMemoryDesc> MKLDNNNode::getSrcMemDesc(mkldnn::primitive_de
}

std::unique_ptr<MKLDNNMemoryDesc> MKLDNNNode::getDstMemDesc(mkldnn::primitive_desc_iterator &primitive_desc_it, size_t idx) {
return make_unique<MKLDNNMemoryDesc>(primitive_desc_it.dst_desc(idx));
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(primitive_desc_it.dst_desc(idx));
// TODO [DS]: uncomment or remove
// InferenceEngine::TensorDesc desc = MKLDNNMemoryDesc(primitive_desc_it.dst_desc(idx));
// if (desc.getLayout() == InferenceEngine::Layout::ANY)
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2 changes: 1 addition & 1 deletion inference-engine/src/mkldnn_plugin/nodes/common/blocked_desc_creator.h
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Expand Up @@ -30,7 +30,7 @@ class BlockedDescCreator {
makeFilteredRange(const CreatorsMap& map, Predicate predicate);
virtual BlockedMemoryDesc createDesc(const InferenceEngine::Precision& precision, const InferenceEngine::SizeVector& srcDims) const = 0;
std::unique_ptr<BlockedMemoryDesc> createUniqueDesc(const InferenceEngine::Precision& precision, const InferenceEngine::SizeVector& srcDims) const {
return make_unique<BlockedMemoryDesc>(createDesc(precision, srcDims));
return MKLDNNPlugin::make_unique<BlockedMemoryDesc>(createDesc(precision, srcDims));
}
virtual size_t getMinimalRank() const = 0;
virtual ~BlockedDescCreator() = default;
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2 changes: 1 addition & 1 deletion inference-engine/src/mkldnn_plugin/nodes/mkldnn_bin_conv_node.cpp
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Expand Up @@ -988,7 +988,7 @@ void MKLDNNBinaryConvolutionNode::initSupportedPrimitiveDescriptors() {
weiDims[2], weiDims[3], weiFirstDimBlockSize, 32};
std::vector<size_t> weiOrder = {0, 1, 2, 3, 0, 1};

config.inConfs[1].desc = make_unique<BlockedMemoryDesc>(Precision::BIN, weiDims, weiBlockDims, weiOrder);
config.inConfs[1].desc = MKLDNNPlugin::make_unique<BlockedMemoryDesc>(Precision::BIN, weiDims, weiBlockDims, weiOrder);

//result
auto outputPrecision = withBinarization ? Precision::BIN : Precision::FP32;
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6 changes: 3 additions & 3 deletions inference-engine/src/mkldnn_plugin/nodes/mkldnn_concat_node.cpp
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Expand Up @@ -178,14 +178,14 @@ void MKLDNNConcatNode::initSupportedPrimitiveDescriptors() {
}
}

config.outConfs[0].desc = make_unique<BlockedMemoryDesc>(outputPrecision, dstDims, blkDims, order, offset, offsets, strides);
config.outConfs[0].desc = MKLDNNPlugin::make_unique<BlockedMemoryDesc>(outputPrecision, dstDims, blkDims, order, offset, offsets, strides);

for (size_t i = 0; i < getParentEdges().size(); i++) {
const auto& srcBlkDims = refConfig.inConfs[i].desc->as<BlockedMemoryDesc>()->getBlockDims();
const auto& dims = refConfig.inConfs[i].desc->getShape().getStaticDims();

config.inConfs[i].inPlace = 0;
config.inConfs[i].desc = make_unique<BlockedMemoryDesc>(inputPrecision, dims, srcBlkDims, order, offset, offsets, strides);
config.inConfs[i].desc = MKLDNNPlugin::make_unique<BlockedMemoryDesc>(inputPrecision, dims, srcBlkDims, order, offset, offsets, strides);
}
supportedPrimitiveDescriptors.emplace_back(config, impl_desc_type::unknown);
}
Expand Down Expand Up @@ -432,7 +432,7 @@ void MKLDNNConcatNode::initOptimalPrimitiveDescriptor() {
size_t offset = 0;
for (size_t i = 0; i < config.inConfs.size(); i++) {
auto inpBlockingDesc = MemoryDescUtils::convertToBlockedDescriptor(*config.inConfs[i].desc);
config.inConfs[i].desc = make_unique<BlockedMemoryDesc>(inpBlockingDesc.getPrecision(),
config.inConfs[i].desc = MKLDNNPlugin::make_unique<BlockedMemoryDesc>(inpBlockingDesc.getPrecision(),
inpBlockingDesc.getShape().getStaticDims(),
inpBlockingDesc.getBlockDims(),
inpBlockingDesc.getOrder(),
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42 changes: 21 additions & 21 deletions inference-engine/src/mkldnn_plugin/nodes/mkldnn_conv_node.cpp
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Expand Up @@ -243,9 +243,9 @@ void MKLDNNConvolutionNode::getSupportedDescriptors() {
outputDataType = memory::data_type::f32;
if (eltwisePrecision == Precision::BF16)
eltwisePrecision = Precision::FP32;
in_candidate = make_unique<MKLDNNMemoryDesc>(getParentEdgeAt(0)->getShape().getStaticMklDims(),
in_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(getParentEdgeAt(0)->getShape().getStaticMklDims(),
inputDataType, ndims == 5 ? memory::format_tag::ndhwc : memory::format_tag::nhwc);
out_candidate = make_unique<MKLDNNMemoryDesc>(getChildEdgeAt(0)->getShape().getStaticMklDims(),
out_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(getChildEdgeAt(0)->getShape().getStaticMklDims(),
outputDataType, ndims == 5 ? memory::format_tag::ndhwc : memory::format_tag::nhwc);
createDescriptor({ in_candidate.get() }, { out_candidate.get() });
} else {
Expand Down Expand Up @@ -288,31 +288,31 @@ void MKLDNNConvolutionNode::getSupportedDescriptors() {
auto outputDims = getChildEdgeAt(0)->getShape().getStaticMklDims();

if (IC == 1 && groupOC == 1) {
in_candidate = make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, ncsp);
out_candidate = make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, ncsp);
in_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, ncsp);
out_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, ncsp);
createDescriptor({ in_candidate.get() }, { out_candidate.get() });
} else if (IC < 4) {
in_candidate = make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, ncsp);
out_candidate = make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, nCsp16c);
in_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, ncsp);
out_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, nCsp16c);
createDescriptor({ in_candidate.get() }, { out_candidate.get() });
out_candidate = make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, nCsp8c);
out_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, nCsp8c);
createDescriptor({ in_candidate.get() }, { out_candidate.get() });
} else {
in_candidate = make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, nCsp16c);
out_candidate = make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, nCsp16c);
in_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, nCsp16c);
out_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, nCsp16c);
createDescriptor({ in_candidate.get() }, { out_candidate.get() });
in_candidate = make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, nCsp8c);
out_candidate = make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, nCsp8c);
in_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, nCsp8c);
out_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, nCsp8c);
createDescriptor({ in_candidate.get() }, { out_candidate.get() });
}

in_candidate = make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, ncsp);
out_candidate = make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, ncsp);
in_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, ncsp);
out_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, ncsp);
createDescriptor({ in_candidate.get() }, { out_candidate.get() });

if (inputDataType != memory::data_type::bf16 && isNspcAvailable()) {
in_candidate = make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, nspc);
out_candidate = make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, nspc);
in_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(inputDims, inputDataType, nspc);
out_candidate = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(outputDims, outputDataType, nspc);
createDescriptor({ in_candidate.get() }, { out_candidate.get() });
}
}
Expand Down Expand Up @@ -412,10 +412,10 @@ void MKLDNNConvolutionNode::initSupportedPrimitiveDescriptors() {
PortConfig dataConfig;
dataConfig.inPlace = -1;
dataConfig.constant = false;
dataConfig.desc = make_unique<MKLDNNMemoryDesc>(dwWeightsDims, weightsPrc, memory::format_tag::Goihw8g);
dataConfig.desc = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(dwWeightsDims, weightsPrc, memory::format_tag::Goihw8g);
config.inConfs.push_back(dataConfig);

dataConfig.desc = make_unique<MKLDNNMemoryDesc>(dwBiasesDims, biasPrc, memory::format_tag::x);
dataConfig.desc = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(dwBiasesDims, biasPrc, memory::format_tag::x);
config.inConfs.push_back(dataConfig);
}

Expand Down Expand Up @@ -597,10 +597,10 @@ void MKLDNNConvolutionNode::initDescriptor(const NodeConfig& config) {
PortConfig dataConfig;
dataConfig.inPlace = -1;
dataConfig.constant = false;
dataConfig.desc = make_unique<MKLDNNMemoryDesc>(dwWeightsDims, weightsPrc, memory::format_tag::Goihw8g);
dataConfig.desc = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(dwWeightsDims, weightsPrc, memory::format_tag::Goihw8g);
cfg.inConfs.push_back(dataConfig);

dataConfig.desc = make_unique<MKLDNNMemoryDesc>(dwBiasesDims, biasPrc, memory::format_tag::x);
dataConfig.desc = MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(dwBiasesDims, biasPrc, memory::format_tag::x);
cfg.inConfs.push_back(dataConfig);
}

Expand Down Expand Up @@ -717,9 +717,9 @@ std::unique_ptr<MKLDNNMemoryDesc> MKLDNNConvolutionNode::getSrcMemDesc(mkldnn::p
new_dims.push_back(old_dims[i]);
}

return make_unique<MKLDNNMemoryDesc>(MKLDNNDims(new_dims), desc.getDataType(), desc.getFormat());
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(MKLDNNDims(new_dims), desc.getDataType(), desc.getFormat());
} else {
return make_unique<MKLDNNMemoryDesc>(std::move(desc));
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(std::move(desc));
}
}

Expand Down
4 changes: 2 additions & 2 deletions inference-engine/src/mkldnn_plugin/nodes/mkldnn_convert_node.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -92,8 +92,8 @@ void MKLDNNConvertNode::initSupportedPrimitiveDescriptors() {
auto range = BlockedDescCreator::makeFilteredRange(creators, insShape.getRank());

for (auto itr = range.first; itr != range.second; ++itr) {
config.inConfs[0].desc = make_unique<BlockedMemoryDesc>(itr->second->createDesc(insPrecision, insShape.getDims()));
config.outConfs[0].desc = make_unique<BlockedMemoryDesc>(itr->second->createDesc(outPrecision, outputShape.getDims()));
config.inConfs[0].desc = MKLDNNPlugin::make_unique<BlockedMemoryDesc>(itr->second->createDesc(insPrecision, insShape.getDims()));
config.outConfs[0].desc = MKLDNNPlugin::make_unique<BlockedMemoryDesc>(itr->second->createDesc(outPrecision, outputShape.getDims()));

supportedPrimitiveDescriptors.emplace_back(config, impl_desc_type::unknown);
}
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8 changes: 4 additions & 4 deletions inference-engine/src/mkldnn_plugin/nodes/mkldnn_deconv_node.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -403,24 +403,24 @@ void MKLDNNDeconvolutionNode::createDescriptor(const std::vector<const MemoryDes
std::unique_ptr<MKLDNNMemoryDesc> MKLDNNDeconvolutionNode::getSrcMemDesc(mkldnn::primitive_desc_iterator &primitive_desc_it, size_t idx) {
if (idx == 2) {
auto dataType = MKLDNNExtensionUtils::IEPrecisionToDataType(getOriginalInputPrecisionAtPort(2));
return make_unique<MKLDNNMemoryDesc>(getParentEdgeAt(2)->getShape().getStaticMklDims(), dataType,
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(getParentEdgeAt(2)->getShape().getStaticMklDims(), dataType,
MKLDNNMemory::GetPlainFormatByRank(getParentEdgeAt(2)->getShape().getRank()));
}

MKLDNNMemoryDesc desc = idx > 0 ? MKLDNNMemoryDesc(primitive_desc_it.weights_desc(idx - 1))
: isInt8 ? MKLDNNMemoryDesc(primitive_desc_it.src_desc(idx)) : MKLDNNMemoryDesc(primitive_desc_it.diff_dst_desc(idx));

if (getParentEdgeAt(idx)->getShape().getRank() != desc.getShape().getRank()) {
return make_unique<MKLDNNMemoryDesc>(MKLDNNDims(weightDims), desc.getDataType(), desc.getFormat());
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(MKLDNNDims(weightDims), desc.getDataType(), desc.getFormat());
} else {
return make_unique<MKLDNNMemoryDesc>(std::move(desc));
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(std::move(desc));
}
}

std::unique_ptr<MKLDNNMemoryDesc> MKLDNNDeconvolutionNode::getDstMemDesc(mkldnn::primitive_desc_iterator &primitive_desc_it, size_t idx) {
MKLDNNMemoryDesc desc = isInt8 ? MKLDNNMemoryDesc(primitive_desc_it.dst_desc(idx))
: MKLDNNMemoryDesc(primitive_desc_it.diff_src_desc(idx));
return make_unique<MKLDNNMemoryDesc>(getChildEdgeAt(idx)->getShape().getStaticMklDims(), desc.getDataType(), desc.getFormat());
return MKLDNNPlugin::make_unique<MKLDNNMemoryDesc>(getChildEdgeAt(idx)->getShape().getStaticMklDims(), desc.getDataType(), desc.getFormat());
}

InferenceEngine::Precision MKLDNNDeconvolutionNode::getRuntimePrecision() const {
Expand Down
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