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[CPU] Added dynamism support for Pad (#8396)

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Alexandra Sidorova 2021-11-15 13:37:34 +03:00 committed by GitHub
parent 29a3f56003
commit c82744e435
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4 changed files with 565 additions and 373 deletions
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414
inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.cpp
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@ -18,36 +18,37 @@ using namespace mkldnn;
using namespace MKLDNNPlugin;
using namespace InferenceEngine;
#define THROW_ERROR IE_THROW() << "Pad layer with name '" << getName() << "' "
bool MKLDNNPadNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
try {
if (isDynamicNgraphNode(op)) {
errorMessage = "Doesn't support op with dynamic shapes";
return false;
}
const auto pad = std::dynamic_pointer_cast<const ngraph::opset1::Pad>(op);
auto pad = ov::as_type_ptr<const ngraph::opset1::Pad>(op);
if (!pad) {
errorMessage = "Only opset1 Pad operation is supported";
return false;
}
if (std::dynamic_pointer_cast<const ngraph::opset1::Constant>(pad->get_input_node_shared_ptr(PADS_BEGIN_ID)) == nullptr ||
std::dynamic_pointer_cast<const ngraph::opset1::Constant>(pad->get_input_node_shared_ptr(PADS_END_ID)) == nullptr ||
(pad->get_pad_mode() == ngraph::op::PadMode::CONSTANT && pad->get_input_size() == 4 &&
std::dynamic_pointer_cast<const ngraph::opset1::Constant>(pad->get_input_node_shared_ptr(PAD_VALUE_ID)) == nullptr)) {
errorMessage = "Only Constant operation on 'pads_begin', 'pads_end', 'pad_value' inpus is supported";
return false;
}
const auto pad_mode = pad->get_pad_mode();
if (pad_mode != ngraph::op::PadMode::CONSTANT && pad_mode != ngraph::op::PadMode::EDGE && pad_mode != ngraph::op::PadMode::REFLECT &&
pad_mode != ngraph::op::PadMode::SYMMETRIC) {
if (!MKLDNNPlugin::one_of(pad_mode, ngraph::op::PadMode::CONSTANT, ngraph::op::PadMode::EDGE, ngraph::op::PadMode::REFLECT,
ngraph::op::PadMode::SYMMETRIC)) {
errorMessage = "Has unsupported pad_mode: " + ngraph::as_string(pad_mode);
return false;
}
if (op->get_input_node_shared_ptr(PADS_BEGIN_ID)->get_type_info() != ov::op::v0::Constant::get_type_info_static() ||
op->get_input_node_shared_ptr(PADS_END_ID)->get_type_info() != ov::op::v0::Constant::get_type_info_static() ||
(pad->get_input_size() == 4 && pad->get_pad_mode() == ngraph::op::PadMode::CONSTANT &&
op->get_input_node_shared_ptr(PAD_VALUE_ID)->get_type_info() != ov::op::v0::Constant::get_type_info_static())) {
// TODO: Support pads_begin, pads_end, pad_value inputs for dynamic shapes.
errorMessage = "Only Constant 'pads_begin', 'pads_end' and 'pad_value' inputs are supported.";
return false;
}
const auto pb = pad->get_pads_begin();
const auto pe = pad->get_pads_end();
if (std::count_if(pb.begin(), pb.end(), [](ptrdiff_t x) { return x < 0; }) != 0 ||
std::count_if(pe.begin(), pe.end(), [](ptrdiff_t x) { return x < 0; }) != 0) {
errorMessage = "Doesn't support 'pads_begin' or 'pads_end' negative value";
if (std::any_of(pb.begin(), pb.end(), [](ptrdiff_t x) { return x < 0; }) ||
std::any_of(pe.begin(), pe.end(), [](ptrdiff_t x) { return x < 0; })) {
errorMessage = "Doesn't support 'pads_begin' or 'pads_end' with negative values";
return false;
}
} catch (...) {
@ -59,62 +60,61 @@ bool MKLDNNPadNode::isSupportedOperation(const std::shared_ptr<const ngraph::Nod
MKLDNNPadNode::MKLDNNPadNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache)
: MKLDNNNode(op, eng, cache) {
std::string errorMessage;
if (isSupportedOperation(op, errorMessage)) {
errorPrefix = "Pad node with name '" + op->get_friendly_name() + "'";
const auto pad = std::dynamic_pointer_cast<const ngraph::opset1::Pad>(op);
const auto pb = pad->get_pads_begin();
const auto pe = pad->get_pads_end();
for (size_t i = 0; i < pb.size(); i++)
padsBegin.push_back(static_cast<unsigned int>(pb[i]));
for (size_t i = 0; i < pe.size(); i++)
padsEnd.push_back(static_cast<unsigned int>(pe[i]));
const auto pad_mode = pad->get_pad_mode();
isPadValueSpecified = pad->get_input_size() == 4;
if (pad_mode == ngraph::op::PadMode::CONSTANT) {
padMode = CONSTANT;
if (isPadValueSpecified) {
if (!ngraph::is_scalar(pad->get_input_shape(PAD_VALUE_ID)))
IE_THROW() << errorPrefix << " has non scalar 'pad_value' input";
padValue = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(pad->get_input_node_shared_ptr(PAD_VALUE_ID))->cast_vector<float>()[0];
}
} else if (pad_mode == ngraph::op::PadMode::EDGE) {
padMode = EDGE;
} else if (pad_mode == ngraph::op::PadMode::REFLECT) {
padMode = REFLECT;
} else if (pad_mode == ngraph::op::PadMode::SYMMETRIC) {
padMode = SYMMETRIC;
}
} else {
if (!isSupportedOperation(op, errorMessage)) {
IE_THROW(NotImplemented) << errorMessage;
}
}
void MKLDNNPadNode::getSupportedDescriptors() {
if (getParentEdges().size() != 3 && getParentEdges().size() != 4)
IE_THROW() << errorPrefix << " has incorrect number of input edges";
if (getChildEdges().empty())
IE_THROW() << errorPrefix << "Incorrect number of output edges";
if (inputShapes.size() != 3 && inputShapes.size() != 4)
THROW_ERROR << " has incorrect number of input edges";
if (outputShapes.size() != 1)
THROW_ERROR << "Incorrect number of output edges";
const auto srcDims = getInputShapeAtPort(DATA_ID).getStaticDims();
const auto dstDims = getOutputShapeAtPort(DATA_ID).getStaticDims();
if (srcDims.size() != dstDims.size() || padsBegin.size() != srcDims.size() || padsEnd.size() != srcDims.size())
IE_THROW() << errorPrefix << " has incorrect number of input/output dimensions!";
const size_t srcDimsRank = inputShapes[DATA_ID].getRank();
const size_t dstDimsRank = outputShapes[DATA_ID].getRank();
if (srcDimsRank != dstDimsRank)
THROW_ERROR << "has incorrect number of input/output dimensions!";
if (padMode == REFLECT) {
for (size_t i = 0; i < srcDims.size(); i++) {
if ((srcDims[i] - 1) < padsBegin[i] || (srcDims[i] - 1) < padsEnd[i])
IE_THROW() << errorPrefix << " has incorrect padsBegin or padsEnd for 'reflect' pad mode";
}
} else if (padMode == SYMMETRIC) {
for (size_t i = 0; i < srcDims.size(); i++) {
if (srcDims[i] < padsBegin[i] || srcDims[i] < padsEnd[i])
IE_THROW() << errorPrefix << " has incorrect padsBegin or padsEnd for 'symmetric' pad mode";
auto pad = ov::as_type_ptr<const ngraph::opset1::Pad>(op);
if (!pad) {
THROW_ERROR << "couldn't be casted to op of opset1";
}
if (op->get_input_node_shared_ptr(PADS_BEGIN_ID)->get_type_info() == ov::op::v0::Constant::get_type_info_static() &&
op->get_input_node_shared_ptr(PADS_END_ID)->get_type_info() == ov::op::v0::Constant::get_type_info_static()) {
const auto pb = pad->get_pads_begin();
const auto pe = pad->get_pads_end();
for (size_t i = 0; i < pb.size(); i++)
attrs.padsBegin.push_back(static_cast<unsigned int>(pb[i]));
for (size_t i = 0; i < pe.size(); i++)
attrs.padsEnd.push_back(static_cast<unsigned int>(pe[i]));
if (attrs.padsBegin.size() != srcDimsRank || attrs.padsEnd.size() != srcDimsRank)
THROW_ERROR << "has incorrect number of input/output dimensions!";
}
const auto pad_mode = pad->get_pad_mode();
isPadValueSpecified = pad->get_input_size() == 4;
if (pad_mode == ngraph::op::PadMode::CONSTANT) {
attrs.padMode = CONSTANT;
if (isPadValueSpecified && op->get_input_node_shared_ptr(PAD_VALUE_ID)->get_type_info() == ov::op::v0::Constant::get_type_info_static()) {
if (!ngraph::is_scalar(pad->get_input_shape(PAD_VALUE_ID)))
THROW_ERROR << "has non scalar 'pad_value' input";
attrs.padValue = ov::as_type_ptr<const ngraph::opset1::Constant>(pad->get_input_node_shared_ptr(PAD_VALUE_ID))->cast_vector<float>()[0];
}
} else if (pad_mode == ngraph::op::PadMode::EDGE) {
attrs.padMode = EDGE;
} else if (pad_mode == ngraph::op::PadMode::REFLECT) {
attrs.padMode = REFLECT;
} else if (pad_mode == ngraph::op::PadMode::SYMMETRIC) {
attrs.padMode = SYMMETRIC;
} else {
THROW_ERROR << "has unsupported pad_mode: " + ngraph::as_string(pad_mode);
}
}
void MKLDNNPadNode::getSupportedDescriptors() {}
void MKLDNNPadNode::initSupportedPrimitiveDescriptors() {
if (!supportedPrimitiveDescriptors.empty())
return;
@ -126,8 +126,8 @@ void MKLDNNPadNode::initSupportedPrimitiveDescriptors() {
if (std::find(supportedPrecisions.begin(), supportedPrecisions.end(), precision) == supportedPrecisions.end())
precision = precision.is_float() ? InferenceEngine::Precision::FP32 : InferenceEngine::Precision::I32;
auto srcDims = getInputShapeAtPort(DATA_ID).getStaticDims();
int numOfDims = srcDims.size();
const auto& inputDataShape = getInputShapeAtPort(DATA_ID);
const size_t numOfDims = inputDataShape.getRank();
NodeConfig config;
config.dynBatchSupport = false;
@ -151,15 +151,17 @@ void MKLDNNPadNode::initSupportedPrimitiveDescriptors() {
pushSupportedPrimitiveDescriptor(LayoutType::ncsp);
auto canUseBlocked = [=](const size_t blockSize) {
return (padMode == CONSTANT && padsBegin[1] % blockSize == 0 && padsEnd[1] % blockSize == 0) ||
(padMode != CONSTANT && padsBegin[1] == 0 && padsEnd[1] == 0);
auto canUseBlocked = [&](const size_t blockSize) {
const auto& srcDims = inputDataShape.getDims();
return srcDims[1] != Shape::UNDEFINED_DIM && srcDims[1] % blockSize == 0 &&
((attrs.padMode == CONSTANT && attrs.padsBegin[1] % blockSize == 0 && attrs.padsEnd[1] % blockSize == 0) ||
(attrs.padMode != CONSTANT && attrs.padsBegin[1] == 0 && attrs.padsEnd[1] == 0));
};
if (numOfDims == 4 || numOfDims == 5) {
if (srcDims[1] % 8 == 0 && canUseBlocked(8))
if (canUseBlocked(8))
pushSupportedPrimitiveDescriptor(LayoutType::nCsp8c);
if (srcDims[1] % 16 == 0 && canUseBlocked(16))
if (canUseBlocked(16))
pushSupportedPrimitiveDescriptor(LayoutType::nCsp16c);
}
}
@ -168,17 +170,76 @@ void MKLDNNPadNode::createPrimitive() {
auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
auto& srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
IE_THROW() << "Destination memory for Pad " << getName() << " didn't allocate.";
THROW_ERROR << "has not allocated source memory.";
if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
IE_THROW() << "Input memory for Pad " << getName() << " didn't allocate.";
THROW_ERROR << "has not allocated destination memory.";
if (getSelectedPrimitiveDescriptor() == nullptr)
IE_THROW() << "Preferable primitive descriptor for Pad " << getName() << " is not set.";
THROW_ERROR << "has unidentified preferable primitive descriptor";
params.sizeData = this->getSelectedPrimitiveDescriptor()->getConfig().inConfs[0].desc->getPrecision().size();
// pads are constant, so we can calculate new collapsing pads for first target dimensions and use it for the next dimensions
// to avoid permanent identical pad calculations
const size_t blockSize = srcMemPtr->getDesc().hasLayoutType(LayoutType::nCsp16c) ? 16 :
(srcMemPtr->getDesc().hasLayoutType(LayoutType::nCsp8c) ? 8 : 1);
if (blockSize > 1) {
attrs.padsBegin[1] /= blockSize;
attrs.padsEnd[1] /= blockSize;
attrs.padsBegin.push_back(0);
attrs.padsEnd.push_back(0);
} else {
auto order = getParentEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getOrder();
std::vector<unsigned int> newPadsBegin(attrs.padsBegin.size(), 0), newPadsEnd(attrs.padsEnd.size(), 0);
for (size_t i = 0; i < attrs.padsBegin.size(); ++i) {
newPadsBegin[i] = attrs.padsBegin[order[i]];
newPadsEnd[i] = attrs.padsEnd[order[i]];
}
attrs.padsBegin = newPadsBegin;
attrs.padsEnd = newPadsEnd;
}
const auto inBlkDesc = getParentEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>();
params.srcDims = inBlkDesc->getBlockDims();
params.dstDims = getChildEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getBlockDims();
// collapse dimensions
attrs.beginPadIdx = 0;
attrs.endPadIdx = attrs.padsBegin.size() - 1;
for (int i = 0; i < attrs.padsBegin.size(); ++i) {
if (attrs.padsBegin[i] != 0 || attrs.padsEnd[i] != 0) {
attrs.beginPadIdx = i - 1;
break;
}
}
for (int i = attrs.padsBegin.size() - 1; i >= 0; --i) {
if (attrs.padsBegin[i] != 0 || attrs.padsEnd[i] != 0) {
attrs.endPadIdx = i;
break;
}
}
if (attrs.beginPadIdx > 0) {
attrs.padsBegin.erase(attrs.padsBegin.begin() + 1, attrs.padsBegin.begin() + attrs.beginPadIdx + 1);
attrs.padsEnd.erase(attrs.padsEnd.begin() + 1, attrs.padsEnd.begin() + attrs.beginPadIdx + 1);
}
attrs.prc = srcMemPtr->getDesc().getPrecision();
if (inputShapesDefined()) {
prepareParams();
updateLastInputDims();
}
}
void MKLDNNPadNode::prepareParams() {
execPtr = std::make_shared<PadExecutor>(attrs,
getParentEdgeAt(0)->getMemoryPtr()->GetDescWithType<BlockedMemoryDesc>()->getBlockDims(),
getChildEdgeAt(0)->getMemoryPtr()->GetDescWithType<BlockedMemoryDesc>()->getBlockDims());
}
MKLDNNPadNode::PadExecutor::PadExecutor(const PadAttrs& attrs,
const VectorDims& srcDims,
const VectorDims& dstDims) {
params.attrs = attrs;
params.srcDims = srcDims;
params.dstDims = dstDims;
params.dataSize = attrs.prc.size();
size_t nDims = params.srcDims.size();
params.srcStrides.resize(nDims, 1);
@ -188,45 +249,11 @@ void MKLDNNPadNode::createPrimitive() {
params.dstStrides[i] = params.dstStrides[i + 1] * params.dstDims[i + 1];
}
if (getParentEdgeAt(0)->getMemory().getDesc().hasLayoutType(LayoutType::nCsp16c) ||
getParentEdgeAt(0)->getMemory().getDesc().hasLayoutType(LayoutType::nCsp8c)) {
padsBegin[1] /= params.srcDims[params.srcDims.size() - 1];
padsEnd[1] /= params.srcDims[params.srcDims.size() - 1];
padsBegin.push_back(0);
padsEnd.push_back(0);
} else {
auto order = inBlkDesc->getOrder();
std::vector<unsigned int> newPadsBegin(padsBegin.size(), 0), newPadsEnd(padsEnd.size(), 0);
for (size_t i = 0; i < padsBegin.size(); ++i) {
newPadsBegin[i] = padsBegin[order[i]];
newPadsEnd[i] = padsEnd[order[i]];
}
padsBegin = newPadsBegin;
padsEnd = newPadsEnd;
}
int beginIdx = 0;
int endIdx = padsBegin.size() - 1;
for (int i = 0; i < padsBegin.size(); ++i) {
if (padsBegin[i] != 0 || padsEnd[i] != 0) {
beginIdx = i - 1;
break;
}
}
for (int i = padsBegin.size() - 1; i >= 0; --i) {
if (padsBegin[i] != 0 || padsEnd[i] != 0) {
endIdx = i;
break;
}
}
params.lastDstDim = params.dstStrides[std::max(endIdx - 1, 0)];
params.nDimsForWork = endIdx - std::max(beginIdx, 0);
params.lastDstDim = params.dstStrides[std::max(params.attrs.endPadIdx - 1, 0)];
params.nDimsForWork = params.attrs.endPadIdx - std::max(params.attrs.beginPadIdx, 0);
params.nThreads = params.nDimsForWork > 0 ? 0 : 1;
params.workAmount = params.nDimsForWork > 0 ? params.dstDims[0] : 1lu;
for (int i = 1; i <= beginIdx; ++i) {
for (int i = 1; i <= params.attrs.beginPadIdx; ++i) {
params.workAmount *= params.dstDims[i];
params.dstDims[0] *= params.dstDims[i];
params.srcDims[0] *= params.srcDims[i];
@ -234,51 +261,62 @@ void MKLDNNPadNode::createPrimitive() {
params.srcStrides[0] /= params.srcDims[i];
}
if (beginIdx > 0) {
beginIdx++;
params.dstDims.erase(params.dstDims.begin() + 1, params.dstDims.begin() + beginIdx);
params.srcDims.erase(params.srcDims.begin() + 1, params.srcDims.begin() + beginIdx);
params.dstStrides.erase(params.dstStrides.begin() + 1, params.dstStrides.begin() + beginIdx);
params.srcStrides.erase(params.srcStrides.begin() + 1, params.srcStrides.begin() + beginIdx);
padsBegin.erase(padsBegin.begin() + 1, padsBegin.begin() + beginIdx);
padsEnd.erase(padsEnd.begin() + 1, padsEnd.begin() + beginIdx);
if (params.attrs.beginPadIdx > 0) {
params.attrs.beginPadIdx++;
params.dstDims.erase(params.dstDims.begin() + 1, params.dstDims.begin() + params.attrs.beginPadIdx);
params.srcDims.erase(params.srcDims.begin() + 1, params.srcDims.begin() + params.attrs.beginPadIdx);
params.dstStrides.erase(params.dstStrides.begin() + 1, params.dstStrides.begin() + params.attrs.beginPadIdx);
params.srcStrides.erase(params.srcStrides.begin() + 1, params.srcStrides.begin() + params.attrs.beginPadIdx);
}
params.workAmount = params.workAmount * params.dstStrides[0] / params.lastDstDim;
params.shift = params.dstStrides[params.nDimsForWork];
if (padMode != CONSTANT || (padMode == CONSTANT && padValue == 0)) {
params.lastDstDim *= params.sizeData;
params.shift *= params.sizeData;
if (params.attrs.padMode != CONSTANT || (params.attrs.padMode == CONSTANT && params.attrs.padValue == 0)) {
params.lastDstDim *= params.dataSize;
params.shift *= params.dataSize;
}
params.srcODims.clear();
for (size_t i = 0; i < params.srcDims.size(); ++i)
params.srcODims.push_back(padsBegin[i] + params.srcDims[i]);
params.srcODims.push_back(params.attrs.padsBegin[i] + params.srcDims[i]);
if (padMode == REFLECT || padMode == SYMMETRIC) {
int shift = padMode == SYMMETRIC ? 1 : 0;
params.srcDimsForReflectOrSymmetric.clear();
if (params.attrs.padMode == REFLECT || params.attrs.padMode == SYMMETRIC) {
int shift = params.attrs.padMode == SYMMETRIC ? 1 : 0;
for (size_t i = 0; i < params.srcDims.size(); ++i)
params.srcDimsForReflectOrSymmetric.push_back(params.srcDims[i] + params.srcODims[i] - 2 + shift);
}
}
void MKLDNNPadNode::execute(mkldnn::stream strm) {
switch (padMode) {
void MKLDNNPadNode::PadExecutor::exec(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
switch (params.attrs.padMode) {
case CONSTANT:
padConstant();
padConstant(srcMemPtr, dstMemPtr);
break;
case EDGE:
padEdge();
padEdge(srcMemPtr, dstMemPtr);
break;
case REFLECT:
padReflectOrSymmetric();
padReflectOrSymmetric(srcMemPtr, dstMemPtr);
break;
case SYMMETRIC:
padReflectOrSymmetric(true);
padReflectOrSymmetric(srcMemPtr, dstMemPtr, true);
break;
}
}
static inline size_t parallel_init(size_t start, size_t nDims, const SizeVector& dims, SizeVector& indexes) {
void MKLDNNPadNode::execute(mkldnn::stream strm) {
if (!execPtr)
THROW_ERROR << "has not compiled executor.";
execPtr->exec(getParentEdgeAt(0)->getMemoryPtr(), getChildEdgeAt(0)->getMemoryPtr());
}
void MKLDNNPadNode::executeDynamicImpl(mkldnn::stream strm) {
execute(strm);
}
static inline size_t parallel_init(size_t start, size_t nDims, const VectorDims& dims, VectorDims& indexes) {
for (int j = nDims - 1; j >= 0; j--) {
indexes[j] = start % dims[j];
start = start / dims[j];
@ -286,7 +324,7 @@ static inline size_t parallel_init(size_t start, size_t nDims, const SizeVector&
return start;
}
static inline void parallel_step(size_t nDims, const SizeVector& dims, SizeVector& indexes) {
static inline void parallel_step(size_t nDims, const VectorDims& dims, VectorDims& indexes) {
for (int j = nDims - 1; j >= 0; --j) {
++indexes[j];
if (indexes[j] < dims[j])
@ -296,17 +334,14 @@ static inline void parallel_step(size_t nDims, const SizeVector& dims, SizeVecto
}
}
void MKLDNNPadNode::padConstant() {
if (padValue == 0) {
padConstantZero();
void MKLDNNPadNode::PadExecutor::padConstant(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
if (params.attrs.padValue == 0) {
padConstantZero(srcMemPtr, dstMemPtr);
return;
}
auto selectedPrimitiveDescriptor = getSelectedPrimitiveDescriptor();
if (!selectedPrimitiveDescriptor)
IE_THROW() << "CPU Pad node with name '" << getName() << "' doesn't have primitive descriptors.";
InferenceEngine::Precision precision = selectedPrimitiveDescriptor->getConfig().inConfs[0].desc->getPrecision();
OV_SWITCH(MKLDNNPlugin, PadConstantEmitter, this, precision,
PadContext ctx { this, srcMemPtr, dstMemPtr };
OV_SWITCH(MKLDNNPlugin, PadConstantEmitter, ctx, params.attrs.prc,
OV_CASE(InferenceEngine::Precision::FP32, float),
OV_CASE(InferenceEngine::Precision::I32, int32_t),
OV_CASE(InferenceEngine::Precision::BF16, bfloat16_t),
@ -315,18 +350,18 @@ void MKLDNNPadNode::padConstant() {
}
template<typename T>
void MKLDNNPadNode::padConstantCommon() {
const T* srcData = reinterpret_cast<const T*>(this->getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
T* dstData = reinterpret_cast<T*>(this->getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
const T value = static_cast<T>(padValue);
void MKLDNNPadNode::PadExecutor::padConstantCommon(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
const T* srcData = reinterpret_cast<const T*>(srcMemPtr->GetPtr());
T* dstData = reinterpret_cast<T*>(dstMemPtr->GetPtr());
const T value = static_cast<T>(params.attrs.padValue);
const size_t beginShift = padsBegin[params.nDimsForWork] * params.shift;
const size_t beginShift = params.attrs.padsBegin[params.nDimsForWork] * params.shift;
const size_t copySize = params.srcDims[params.nDimsForWork] * params.shift;
const size_t endShift = padsEnd[params.nDimsForWork] * params.shift;
const size_t endShift = params.attrs.padsEnd[params.nDimsForWork] * params.shift;
parallel_nt(params.nThreads, [&](const int ithr, const int nthr) {
size_t start = 0, end = 0;
SizeVector indexes(params.nDimsForWork, 0);
VectorDims indexes(params.nDimsForWork, 0);
splitter(params.workAmount, nthr, ithr, start, end);
parallel_init(start, params.nDimsForWork, params.dstDims, indexes);
@ -336,7 +371,7 @@ void MKLDNNPadNode::padConstantCommon() {
for (size_t iwork = start; iwork < end; ++iwork, dstIdx += params.lastDstDim) {
size_t j = 0;
for (; j < params.nDimsForWork; ++j) {
if (indexes[j] < padsBegin[j] || indexes[j] >= params.srcODims[j])
if (indexes[j] < params.attrs.padsBegin[j] || indexes[j] >= params.srcODims[j])
break;
}
@ -348,10 +383,10 @@ void MKLDNNPadNode::padConstantCommon() {
size_t srcIdx = 0;
for (size_t idx = 0; idx < params.nDimsForWork; ++idx)
srcIdx += (indexes[idx] - padsBegin[idx]) * params.srcStrides[idx];
srcIdx += (indexes[idx] - params.attrs.padsBegin[idx]) * params.srcStrides[idx];
std::fill_n(&dstData[dstIdx], beginShift, value);
cpu_memcpy(&dstData[dstIdx + beginShift], &srcData[srcIdx], copySize * params.sizeData);
cpu_memcpy(&dstData[dstIdx + beginShift], &srcData[srcIdx], copySize * params.dataSize);
std::fill_n(&dstData[dstIdx + beginShift + copySize], endShift, value);
parallel_step(params.nDimsForWork, params.dstDims, indexes);
@ -359,27 +394,28 @@ void MKLDNNPadNode::padConstantCommon() {
});
}
void MKLDNNPadNode::padConstantZero() {
const uint8_t* srcData = reinterpret_cast<const uint8_t*>(this->getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
uint8_t* dstData = reinterpret_cast<uint8_t*>(this->getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
void MKLDNNPadNode::PadExecutor::padConstantZero(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
const uint8_t* srcData = reinterpret_cast<const uint8_t*>(srcMemPtr->GetPtr());
uint8_t* dstData = reinterpret_cast<uint8_t*>(dstMemPtr->GetPtr());
const size_t beginShift = padsBegin[params.nDimsForWork] * params.shift;
const size_t beginShift = params.attrs.padsBegin[params.nDimsForWork] * params.shift;
const size_t copySize = params.srcDims[params.nDimsForWork] * params.shift;
const size_t endShift = padsEnd[params.nDimsForWork] * params.shift;
const size_t endShift = params.attrs.padsEnd[params.nDimsForWork] * params.shift;
parallel_nt(params.nThreads, [&](const int ithr, const int nthr) {
size_t start = 0, end = 0;
SizeVector indexes(params.nDimsForWork, 0);
VectorDims indexes(params.nDimsForWork, 0);
splitter(params.workAmount, nthr, ithr, start, end);
parallel_init(start, params.nDimsForWork, params.dstDims, indexes);
size_t dstIdx = 0;
getDstIdx(indexes, dstIdx);
dstIdx *= params.dataSize;
for (size_t iwork = start; iwork < end; ++iwork, dstIdx += params.lastDstDim) {
size_t j = 0;
for (; j < params.nDimsForWork; ++j) {
if (indexes[j] < padsBegin[j] || indexes[j] >= params.srcODims[j])
if (indexes[j] < params.attrs.padsBegin[j] || indexes[j] >= params.srcODims[j])
break;
}
@ -391,8 +427,8 @@ void MKLDNNPadNode::padConstantZero() {
size_t srcIdx = 0;
for (size_t idx = 0; idx < params.nDimsForWork; ++idx)
srcIdx += (indexes[idx] - padsBegin[idx]) * params.srcStrides[idx];
srcIdx *= params.sizeData;
srcIdx += (indexes[idx] - params.attrs.padsBegin[idx]) * params.srcStrides[idx];
srcIdx *= params.dataSize;
memset(&dstData[dstIdx], 0, beginShift);
cpu_memcpy(&dstData[dstIdx + beginShift], &srcData[srcIdx], copySize);
@ -403,37 +439,38 @@ void MKLDNNPadNode::padConstantZero() {
});
}
void MKLDNNPadNode::padEdge() {
const uint8_t* srcData = reinterpret_cast<const uint8_t*>(this->getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
uint8_t* dstData = reinterpret_cast<uint8_t*>(this->getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
void MKLDNNPadNode::PadExecutor::padEdge(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) {
const uint8_t* srcData = reinterpret_cast<const uint8_t*>(srcMemPtr->GetPtr());
uint8_t* dstData = reinterpret_cast<uint8_t*>(dstMemPtr->GetPtr());
const size_t beginShift = padsBegin[params.nDimsForWork] * params.shift;
const size_t beginShift = params.attrs.padsBegin[params.nDimsForWork] * params.shift;
const size_t copySize = params.srcDims[params.nDimsForWork] * params.shift;
parallel_nt(params.nThreads, [&](const int ithr, const int nthr) {
size_t start = 0, end = 0;
SizeVector indexes(params.nDimsForWork, 0);
VectorDims indexes(params.nDimsForWork, 0);
splitter(params.workAmount, nthr, ithr, start, end);
parallel_init(start, params.nDimsForWork, params.dstDims, indexes);
size_t dstIdx = 0;
getDstIdx(indexes, dstIdx);
dstIdx *= params.dataSize;
for (size_t iwork = start; iwork < end; ++iwork, dstIdx += params.lastDstDim) {
size_t srcIdx = 0;
for (size_t idx = 0; idx < params.nDimsForWork; ++idx) {
size_t shift = (indexes[idx] < padsBegin[idx]) ? 0 :
((indexes[idx] >= params.srcODims[idx]) ? (params.srcDims[idx] - 1) : (indexes[idx] - padsBegin[idx]));
size_t shift = (indexes[idx] < params.attrs.padsBegin[idx]) ? 0 :
((indexes[idx] >= params.srcODims[idx]) ? (params.srcDims[idx] - 1) : (indexes[idx] - params.attrs.padsBegin[idx]));
srcIdx += shift * params.srcStrides[idx];
}
srcIdx *= params.sizeData;
srcIdx *= params.dataSize;
for (size_t i = 0; i < padsBegin[params.nDimsForWork]; ++i)
for (size_t i = 0; i < params.attrs.padsBegin[params.nDimsForWork]; ++i)
cpu_memcpy(&dstData[dstIdx + i * params.shift], &srcData[srcIdx], params.shift);
cpu_memcpy(&dstData[dstIdx + beginShift], &srcData[srcIdx], copySize);
for (size_t i = 0; i < padsEnd[params.nDimsForWork]; ++i)
for (size_t i = 0; i < params.attrs.padsEnd[params.nDimsForWork]; ++i)
cpu_memcpy(&dstData[dstIdx + beginShift + copySize + i * params.shift],
&srcData[srcIdx + (params.srcDims[params.nDimsForWork] - 1) * params.shift], params.shift);
@ -442,38 +479,40 @@ void MKLDNNPadNode::padEdge() {
});
}
void MKLDNNPadNode::padReflectOrSymmetric(const bool isSymmetric) {
const uint8_t* srcData = reinterpret_cast<const uint8_t*>(this->getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
uint8_t* dstData = reinterpret_cast<uint8_t*>(this->getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
void MKLDNNPadNode::PadExecutor::padReflectOrSymmetric(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr, const bool isSymmetric) {
const uint8_t* srcData = reinterpret_cast<const uint8_t*>(srcMemPtr->GetPtr());
uint8_t* dstData = reinterpret_cast<uint8_t*>(dstMemPtr->GetPtr());
size_t shift = isSymmetric ? 1 : 0;
parallel_nt(params.nThreads, [&](const int ithr, const int nthr) {
size_t start = 0, end = 0;
SizeVector indexes(params.nDimsForWork, 0);
VectorDims indexes(params.nDimsForWork, 0);
splitter(params.workAmount, nthr, ithr, start, end);
parallel_init(start, params.nDimsForWork, params.dstDims, indexes);
size_t dstIdx = 0;
getDstIdx(indexes, dstIdx);
dstIdx *= params.dataSize;
for (size_t iwork = start; iwork < end; ++iwork, dstIdx += params.lastDstDim) {
size_t srcIdx = 0;
for (size_t i = 0; i < params.nDimsForWork; ++i) {
size_t idx = (indexes[i] < padsBegin[i]) ? (padsBegin[i] - indexes[i] - shift) :
((indexes[i] >= params.srcODims[i]) ? (params.srcDimsForReflectOrSymmetric[i] - indexes[i]) : (indexes[i] - padsBegin[i]));
size_t idx = (indexes[i] < params.attrs.padsBegin[i]) ? (params.attrs.padsBegin[i] - indexes[i] - shift) :
((indexes[i] >= params.srcODims[i]) ? (params.srcDimsForReflectOrSymmetric[i] - indexes[i]) :
(indexes[i] - params.attrs.padsBegin[i]));
srcIdx += idx * params.srcStrides[i];
}
srcIdx *= params.sizeData;
srcIdx *= params.dataSize;
for (size_t i = 0; i < padsBegin[params.nDimsForWork]; ++i)
for (size_t i = 0; i < params.attrs.padsBegin[params.nDimsForWork]; ++i)
cpu_memcpy(&dstData[dstIdx + i * params.shift],
&srcData[srcIdx + (padsBegin[params.nDimsForWork] - shift - i) * params.shift], params.shift);
&srcData[srcIdx + (params.attrs.padsBegin[params.nDimsForWork] - shift - i) * params.shift], params.shift);
cpu_memcpy(&dstData[dstIdx + padsBegin[params.nDimsForWork] * params.shift], &srcData[srcIdx],
cpu_memcpy(&dstData[dstIdx + params.attrs.padsBegin[params.nDimsForWork] * params.shift], &srcData[srcIdx],
params.srcDims[params.nDimsForWork] * params.shift);
size_t srcShift = (params.srcDimsForReflectOrSymmetric[params.nDimsForWork] - params.srcODims[params.nDimsForWork]) * params.shift;
for (size_t i = 0; i < padsEnd[params.nDimsForWork]; ++i)
for (size_t i = 0; i < params.attrs.padsEnd[params.nDimsForWork]; ++i)
cpu_memcpy(&dstData[dstIdx + (params.srcODims[params.nDimsForWork] + i) * params.shift],
&srcData[srcIdx + srcShift - i * params.shift], params.shift);
@ -482,10 +521,9 @@ void MKLDNNPadNode::padReflectOrSymmetric(const bool isSymmetric) {
});
}
inline void MKLDNNPadNode::getDstIdx(const InferenceEngine::SizeVector& indexes, size_t& dstIdx) const {
inline void MKLDNNPadNode::PadExecutor::getDstIdx(const VectorDims& indexes, size_t& dstIdx) const {
for (size_t i = 0; i < params.nDimsForWork; ++i)
dstIdx += indexes[i] * params.dstStrides[i];
dstIdx *= (padMode == CONSTANT && padValue != 0) ? 1 : params.sizeData;
}
bool MKLDNNPadNode::created() const {

96
inference-engine/src/mkldnn_plugin/nodes/mkldnn_pad_node.h
View File

@ -14,13 +14,17 @@ class MKLDNNPadNode : public MKLDNNNode {
public:
MKLDNNPadNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
void getSupportedDescriptors() override;
void initSupportedPrimitiveDescriptors() override;
void createPrimitive() override;
void execute(mkldnn::stream strm) override;
bool created() const override;
static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
void prepareParams() override;
protected:
void executeDynamicImpl(mkldnn::stream strm) override;
private:
enum PadMode {
@ -30,48 +34,70 @@ private:
SYMMETRIC = 3
};
void padConstant();
template<typename T> void padConstantCommon();
void padConstantZero();
void padEdge();
void padReflectOrSymmetric(const bool isSymmetric = false);
struct PadAttrs {
PadMode padMode = CONSTANT;
float padValue = 0.f;
std::vector<unsigned int> padsBegin;
std::vector<unsigned int> padsEnd;
int beginPadIdx = 0;
int endPadIdx = 0;
InferenceEngine::Precision prc;
} attrs;
inline void getDstIdx(const InferenceEngine::SizeVector& indexes, size_t& dstIdx) const;
struct PadExecutor {
PadExecutor(const PadAttrs& params, const VectorDims& srcDims, const VectorDims& dstDims);
void exec(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr);
~PadExecutor() = default;
PadMode padMode = CONSTANT;
float padValue = 0.f;
std::vector<unsigned int> padsBegin;
std::vector<unsigned int> padsEnd;
private:
void padConstant(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr);
template<typename T> void padConstantCommon(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr);
void padConstantZero(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr);
void padEdge(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr);
void padReflectOrSymmetric(MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr, const bool isSymmetric = false);
struct {
InferenceEngine::SizeVector srcDims;
InferenceEngine::SizeVector dstDims;
InferenceEngine::SizeVector srcODims;
InferenceEngine::SizeVector srcStrides;
InferenceEngine::SizeVector dstStrides;
InferenceEngine::SizeVector srcDimsForReflectOrSymmetric;
int nThreads = 0;
size_t nDimsForWork = 0lu;
size_t workAmount = 0lu;
size_t lastDstDim = 1lu;
size_t shift = 0lu;
uint8_t sizeData = 1;
} params;
inline void getDstIdx(const VectorDims& indexes, size_t& dstIdx) const;
template<typename T>
struct PadConstantEmitter {
void operator()(MKLDNNPadNode* node) {
node->padConstantCommon<T>();
}
struct PadContext {
PadExecutor* executor;
MKLDNNMemoryPtr srcMemPtr;
MKLDNNMemoryPtr dstMemPtr;
};
template<typename T>
struct PadConstantEmitter {
void operator()(PadContext& ctx) {
ctx.executor->padConstantCommon<T>(ctx.srcMemPtr, ctx.dstMemPtr);
}
};
struct {
PadAttrs attrs;
VectorDims srcDims;
VectorDims dstDims;
VectorDims srcODims;
VectorDims srcStrides;
VectorDims dstStrides;
VectorDims srcDimsForReflectOrSymmetric;
int nThreads = 0;
size_t nDimsForWork = 0lu;
size_t workAmount = 0lu;
size_t lastDstDim = 1lu;
size_t shift = 0lu;
size_t dataSize = 1lu;
PadMode padMode;
} params;
};
std::string errorPrefix;
static const size_t DATA_ID = 0;
static const size_t PADS_BEGIN_ID = 1;
static const size_t PADS_END_ID = 2;
static const size_t PAD_VALUE_ID = 3;
static constexpr size_t DATA_ID = 0lu;
static constexpr size_t PADS_BEGIN_ID = 1lu;
static constexpr size_t PADS_END_ID = 2lu;
static constexpr size_t PAD_VALUE_ID = 3lu;
bool isPadValueSpecified = false;
using executorPtr = std::shared_ptr<PadExecutor>;
executorPtr execPtr = nullptr;
};
} // namespace MKLDNNPlugin

416
inference-engine/tests/functional/plugin/cpu/single_layer_tests/pad.cpp
View File

@ -4,57 +4,75 @@
#include <shared_test_classes/single_layer/pad.hpp>
#include "test_utils/cpu_test_utils.hpp"
#include "shared_test_classes/base/ov_subgraph.hpp"
using namespace InferenceEngine;
using namespace CPUTestUtils;
using namespace ov;
using namespace test;
namespace CPULayerTestsDefinitions {
typedef std::tuple<
LayerTestsDefinitions::padLayerTestParamsSet,
using PadLayerCPUTestParamSet = std::tuple<
InputShape, // Input shape
ElementType, // Input element type
std::vector<int64_t>, // padsBegin
std::vector<int64_t>, // padsEnd
float, // argPadValue
ngraph::helpers::PadMode, // padMode
CPUSpecificParams
> padLayerCPUTestParamsSet;
>;
class PadLayerCPUTest : public testing::WithParamInterface<padLayerCPUTestParamsSet>,
virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
class PadLayerCPUTest : public testing::WithParamInterface<PadLayerCPUTestParamSet>,
virtual public SubgraphBaseTest, public CPUTestsBase {
public:
static std::string getTestCaseName(testing::TestParamInfo<padLayerCPUTestParamsSet> obj) {
LayerTestsDefinitions::padLayerTestParamsSet basicParamsSet;
static std::string getTestCaseName(testing::TestParamInfo<PadLayerCPUTestParamSet> obj) {
InputShape shapes;
ElementType elementType;
std::vector<int64_t> padsBegin, padsEnd;
ngraph::helpers::PadMode padMode;
float argPadValue;
CPUSpecificParams cpuParams;
std::tie(basicParamsSet, cpuParams) = obj.param;
std::tie(shapes, elementType, padsBegin, padsEnd, argPadValue, padMode, cpuParams) = obj.param;
std::ostringstream result;
result << LayerTestsDefinitions::PadLayerTest::getTestCaseName(testing::TestParamInfo<LayerTestsDefinitions::padLayerTestParamsSet>(
basicParamsSet, 0));
std::ostringstream results;
results << "IS=" << CommonTestUtils::partialShape2str({shapes.first}) << "_";
results << "TS=";
for (const auto& item : shapes.second) {
results << CommonTestUtils::vec2str(item) << "_";
}
results << "Prc=" << elementType << "_";
results << "padsBegin=" << CommonTestUtils::vec2str(padsBegin) << "_";
results << "padsEnd=" << CommonTestUtils::vec2str(padsEnd) << "_";
if (padMode == ngraph::helpers::PadMode::CONSTANT) {
results << "Value=" << argPadValue << "_";
}
results << "PadMode=" << padMode << "_";
results << CPUTestsBase::getTestCaseName(cpuParams);
result << CPUTestsBase::getTestCaseName(cpuParams);
return result.str();
return results.str();
}
protected:
void SetUp() override {
LayerTestsDefinitions::padLayerTestParamsSet basicParamsSet;
InputShape shapes;
ElementType elementType;
std::vector<int64_t> padsBegin, padsEnd;
ngraph::helpers::PadMode padMode;
float argPadValue;
CPUSpecificParams cpuParams;
std::tie(basicParamsSet, cpuParams) = this->GetParam();
std::tie(shapes, elementType, padsBegin, padsEnd, argPadValue, padMode, cpuParams) = this->GetParam();
std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
if (selectedType.empty()) {
selectedType = getPrimitiveType();
}
selectedType = selectedType + "_" + InferenceEngine::details::convertPrecision(inType).name();
targetDevice = CommonTestUtils::DEVICE_CPU;
init_input_shapes({shapes});
InferenceEngine::SizeVector inputShape;
std::vector<int64_t> padsBegin, padsEnd;
float argPadValue;
ngraph::helpers::PadMode padMode;
InferenceEngine::Precision netPrecision;
std::tie(padsBegin, padsEnd, argPadValue, padMode, netPrecision, inPrc, outPrc, inLayout, inputShape, targetDevice) =
basicParamsSet;
inPrc = outPrc = netPrecision;
selectedType = std::string("ref_") + netPrecision.name();
auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
auto paramOuts = ngraph::helpers::convert2OutputVector(
ngraph::helpers::castOps2Nodes<ngraph::opset3::Parameter>(params));
auto pad = ngraph::builder::makePad(paramOuts[0], padsBegin, padsEnd, argPadValue, padMode);
auto params = ngraph::builder::makeDynamicParams(elementType, inputDynamicShapes);
auto pad = ngraph::builder::makePad(params[0], padsBegin, padsEnd, argPadValue, padMode);
pad->get_rt_info() = getCPUInfo();
ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(pad)};
function = std::make_shared<ngraph::Function>(results, params, "pad");
@ -64,8 +82,10 @@ protected:
TEST_P(PadLayerCPUTest, CompareWithRefs) {
SKIP_IF_CURRENT_TEST_IS_DISABLED()
Run();
CheckPluginRelatedResults(executableNetwork, "Pad");
run();
// TODO: need to uncomment when this method will be updated
//CheckPluginRelatedResults(executableNetwork, "Pad");
}
namespace {
@ -80,11 +100,13 @@ const auto cpuParams_nCdhw8c = CPUSpecificParams {{nCdhw8c}, {nCdhw8c}, {}, {}};
const auto cpuParams_nhwc = CPUSpecificParams {{nhwc}, {nhwc}, {}, {}};
const auto cpuParams_ndhwc = CPUSpecificParams {{ndhwc}, {ndhwc}, {}, {}};
const auto cpuParams_nchw = CPUSpecificParams {{nchw}, {nchw}, {}, {}};
const auto cpuParams_ncdhw = CPUSpecificParams {{ncdhw}, {ncdhw}, {}, {}};
const std::vector<InferenceEngine::Precision> inputPrecisions = {
InferenceEngine::Precision::FP32,
InferenceEngine::Precision::BF16,
InferenceEngine::Precision::I8
const std::vector<ElementType> inputPrecisions = {
ElementType::f32,
ElementType::bf16,
ElementType::i8
};
const std::vector<float> argPadValue = {0.f, 1.f, 2.5f, -1.f};
@ -95,6 +117,8 @@ const std::vector<ngraph::helpers::PadMode> padMode = {
ngraph::helpers::PadMode::SYMMETRIC
};
/* *======================* Static Shapes Tests 4D *======================* */
const std::vector<std::vector<int64_t>> padsBegin4DConstBlocked = {{0, 0, 0, 0}, {0, 0, 1, 3}, {2, 16, 1, 0}, {0, 0, 2, 0}};
const std::vector<std::vector<int64_t>> padsEnd4DConstBlocked = {{0, 0, 0, 0}, {0, 0, 2, 1}, {2, 0, 0, 1}, {1, 32, 2, 0}};
@ -109,95 +133,142 @@ const std::vector<CPUSpecificParams> CPUParams4DBlocked = {
cpuParams_nChw8c,
};
const auto pad4DConstParamsBlocked = testing::Combine(
testing::ValuesIn(padsBegin4DConstBlocked),
testing::ValuesIn(padsEnd4DConstBlocked),
testing::ValuesIn(argPadValue),
testing::Values(ngraph::helpers::PadMode::CONSTANT),
testing::ValuesIn(inputPrecisions),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Layout::ANY),
testing::Values(std::vector<size_t>{3, 16, 5, 5}),
testing::Values(CommonTestUtils::DEVICE_CPU)
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPad4DConstBlocked,
PadLayerCPUTest,
::testing::Combine(
pad4DConstParamsBlocked,
::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5}})),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin4DConstBlocked),
::testing::ValuesIn(padsEnd4DConstBlocked),
::testing::ValuesIn(argPadValue),
::testing::Values(ngraph::helpers::PadMode::CONSTANT),
::testing::ValuesIn(CPUParams4DBlocked)),
PadLayerCPUTest::getTestCaseName
);
const auto pad4DConstParams = testing::Combine(
testing::ValuesIn(padsBegin4D),
testing::ValuesIn(padsEnd4D),
testing::ValuesIn(argPadValue),
testing::Values(ngraph::helpers::PadMode::CONSTANT),
testing::ValuesIn(inputPrecisions),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Layout::ANY),
testing::Values(std::vector<size_t>{3, 16, 5, 5}),
testing::Values(CommonTestUtils::DEVICE_CPU)
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPad4DConst,
PadLayerCPUTest,
::testing::Combine(
pad4DConstParams,
::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5}})),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin4D),
::testing::ValuesIn(padsEnd4D),
::testing::ValuesIn(argPadValue),
::testing::Values(ngraph::helpers::PadMode::CONSTANT),
::testing::Values(cpuParams_nhwc)),
PadLayerCPUTest::getTestCaseName
);
const auto pad4DParamsBlocked = testing::Combine(
testing::ValuesIn(padsBegin4DBlocked),
testing::ValuesIn(padsEnd4DBlocked),
testing::Values(0),
testing::ValuesIn(padMode),
testing::ValuesIn(inputPrecisions),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Layout::ANY),
testing::Values(std::vector<size_t>{3, 16, 10, 5}),
testing::Values(CommonTestUtils::DEVICE_CPU)
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPad4DBlocked,
PadLayerCPUTest,
::testing::Combine(
pad4DParamsBlocked,
::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 10, 5}})),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin4DBlocked),
::testing::ValuesIn(padsEnd4DBlocked),
::testing::Values(0),
::testing::ValuesIn(padMode),
::testing::ValuesIn(CPUParams4DBlocked)),
PadLayerCPUTest::getTestCaseName
);
const auto pad4DParams = testing::Combine(
testing::ValuesIn(padsBegin4D),
testing::ValuesIn(padsEnd4D),
testing::Values(0),
testing::ValuesIn(padMode),
testing::ValuesIn(inputPrecisions),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Layout::ANY),
testing::Values(std::vector<size_t>{3, 16, 10, 5}),
testing::Values(CommonTestUtils::DEVICE_CPU)
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPad4D,
PadLayerCPUTest,
::testing::Combine(
pad4DParams,
::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 10, 5}})),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin4DBlocked),
::testing::ValuesIn(padsEnd4DBlocked),
::testing::Values(0),
::testing::ValuesIn(padMode),
::testing::Values(cpuParams_nhwc)),
PadLayerCPUTest::getTestCaseName
);
/* *======================* *=====================* *======================* */
/* *======================* Dynamic Shapes Tests 4D *======================* */
const std::vector<InputShape> inputShapesDynamic4D = {
{{-1, -1, -1, -1}, // dynamic
{{5, 36, 5, 5}, {3, 16, 10, 5}, {3, 24, 10, 10}}}, // target
{{-1, 32, -1, -1}, // dynamic
{{5, 32, 5, 5}, {5, 32, 5, 8}, {3, 32, 8, 8}}}, // target
{{{1, 5}, {16, 32}, {1, 16}, {1, 16}}, // dynamic
{{3, 16, 5, 5}, {5, 24, 5, 8}, {3, 32, 8, 8}}}, // target
};
const std::vector<CPUSpecificParams> CPUParams4DDynamic = {
cpuParams_nhwc,
cpuParams_nchw
};
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPadDynamic4DConst,
PadLayerCPUTest,
::testing::Combine(
::testing::ValuesIn(inputShapesDynamic4D),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin4D),
::testing::ValuesIn(padsEnd4D),
::testing::ValuesIn(argPadValue),
::testing::Values(ngraph::helpers::PadMode::CONSTANT),
::testing::ValuesIn(CPUParams4DDynamic)),
PadLayerCPUTest::getTestCaseName
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPadDynamic4DConstBlocked,
PadLayerCPUTest,
::testing::Combine(
::testing::Values(inputShapesDynamic4D[1]),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin4DConstBlocked),
::testing::ValuesIn(padsEnd4DConstBlocked),
::testing::ValuesIn(argPadValue),
::testing::Values(ngraph::helpers::PadMode::CONSTANT),
::testing::ValuesIn(CPUParams4DBlocked)),
PadLayerCPUTest::getTestCaseName
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPadDynamic4D,
PadLayerCPUTest,
::testing::Combine(
::testing::ValuesIn(inputShapesDynamic4D),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin4D),
::testing::ValuesIn(padsEnd4D),
::testing::Values(0),
::testing::ValuesIn(padMode),
::testing::ValuesIn(CPUParams4DDynamic)),
PadLayerCPUTest::getTestCaseName
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPadDynamic4DBlocked,
PadLayerCPUTest,
::testing::Combine(
::testing::Values(inputShapesDynamic4D[1]),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin4DBlocked),
::testing::ValuesIn(padsEnd4DBlocked),
::testing::Values(0),
::testing::ValuesIn(padMode),
::testing::ValuesIn(CPUParams4DBlocked)),
PadLayerCPUTest::getTestCaseName
);
/* *======================* *=====================* *======================* */
/* *======================* Static Shapes Tests 5D *======================* */
const std::vector<std::vector<int64_t>> padsBegin5DConstBlocked = {{0, 0, 0, 0, 0}, {0, 0, 1, 1, 0}, {2, 32, 1, 1, 0}, {0, 0, 1, 3, 1}, {0, 0, 0, 1, 0}};
const std::vector<std::vector<int64_t>> padsEnd5DConstBlocked = {{0, 0, 0, 0, 0}, {1, 16, 1, 1, 0}, {0, 0, 0, 1, 0}, {0, 0, 0, 1, 1}, {0, 0, 1, 0, 1}};
@ -212,94 +283,139 @@ const std::vector<CPUSpecificParams> CPUParams5DBlocked = {
cpuParams_nCdhw8c,
};
const auto pad5DConstParamsBlocked = testing::Combine(
testing::ValuesIn(padsBegin5DConstBlocked),
testing::ValuesIn(padsEnd5DConstBlocked),
testing::ValuesIn(argPadValue),
testing::Values(ngraph::helpers::PadMode::CONSTANT),
testing::ValuesIn(inputPrecisions),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Layout::ANY),
testing::Values(std::vector<size_t>{3, 16, 5, 5, 5}),
testing::Values(CommonTestUtils::DEVICE_CPU)
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPad5DConstBlocked,
PadLayerCPUTest,
::testing::Combine(
pad5DConstParamsBlocked,
::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin5DConstBlocked),
::testing::ValuesIn(padsEnd5DConstBlocked),
::testing::ValuesIn(argPadValue),
::testing::Values(ngraph::helpers::PadMode::CONSTANT),
::testing::ValuesIn(CPUParams5DBlocked)),
PadLayerCPUTest::getTestCaseName
);
const auto pad5DConstParams = testing::Combine(
testing::ValuesIn(padsBegin5D),
testing::ValuesIn(padsEnd5D),
testing::ValuesIn(argPadValue),
testing::Values(ngraph::helpers::PadMode::CONSTANT),
testing::ValuesIn(inputPrecisions),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Layout::ANY),
testing::Values(std::vector<size_t>{3, 16, 10, 5, 5}),
testing::Values(CommonTestUtils::DEVICE_CPU)
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPad5DConst,
PadLayerCPUTest,
::testing::Combine(
pad5DConstParams,
::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin5D),
::testing::ValuesIn(padsEnd5D),
::testing::ValuesIn(argPadValue),
::testing::Values(ngraph::helpers::PadMode::CONSTANT),
::testing::Values(cpuParams_ndhwc)),
PadLayerCPUTest::getTestCaseName
);
const auto pad5DParamsBlocked = testing::Combine(
testing::ValuesIn(padsBegin5DBlocked),
testing::ValuesIn(padsEnd5DBlocked),
testing::Values(0),
testing::ValuesIn(padMode),
testing::ValuesIn(inputPrecisions),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Layout::ANY),
testing::Values(std::vector<size_t>{3, 16, 5, 5, 5}),
testing::Values(CommonTestUtils::DEVICE_CPU)
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPad5DBlocked,
PadLayerCPUTest,
::testing::Combine(
pad5DParamsBlocked,
::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin5DBlocked),
::testing::ValuesIn(padsEnd5DBlocked),
::testing::Values(0),
::testing::ValuesIn(padMode),
::testing::ValuesIn(CPUParams5DBlocked)),
PadLayerCPUTest::getTestCaseName
);
const auto pad5DParams = testing::Combine(
testing::ValuesIn(padsBegin5D),
testing::ValuesIn(padsEnd5D),
testing::Values(0),
testing::ValuesIn(padMode),
testing::ValuesIn(inputPrecisions),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Precision::UNSPECIFIED),
testing::Values(InferenceEngine::Layout::ANY),
testing::Values(std::vector<size_t>{3, 16, 5, 5, 5}),
testing::Values(CommonTestUtils::DEVICE_CPU)
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPad5D,
PadLayerCPUTest,
::testing::Combine(
pad5DParams,
::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin5D),
::testing::ValuesIn(padsEnd5D),
::testing::Values(0),
::testing::ValuesIn(padMode),
::testing::Values(cpuParams_ndhwc)),
PadLayerCPUTest::getTestCaseName
);
/* *======================* *=====================* *======================* */
/* *======================* Dynamic Shapes Tests 5D *======================* */
const std::vector<InputShape> inputShapesDynamic5D = {
{{-1, -1, -1, -1, -1}, // dynamic
{{5, 36, 5, 5, 5}, {3, 16, 8, 5, 7}, {3, 24, 10, 10, 10}}}, // target
{{-1, 32, -1, -1, -1}, // dynamic
{{5, 32, 5, 5, 5}, {3, 32, 8, 5, 7}, {3, 32, 10, 10, 10}}}, // target
{{{1, 5}, {16, 32}, {1, 16}, {1, 16}, {1, 16}}, // dynamic
{{3, 16, 5, 5, 5}, {3, 24, 8, 5, 7}, {4, 32, 10, 10, 10}}}, // target
};
const std::vector<CPUSpecificParams> CPUParams5DDynamic = {
cpuParams_ndhwc,
cpuParams_ncdhw
};
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPadDynamic5DConst,
PadLayerCPUTest,
::testing::Combine(
::testing::ValuesIn(inputShapesDynamic5D),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin5D),
::testing::ValuesIn(padsEnd5D),
::testing::ValuesIn(argPadValue),
::testing::Values(ngraph::helpers::PadMode::CONSTANT),
::testing::ValuesIn(CPUParams5DDynamic)),
PadLayerCPUTest::getTestCaseName
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPadDynamic5DConstBlocked,
PadLayerCPUTest,
::testing::Combine(
::testing::Values(inputShapesDynamic5D[1]),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin5DConstBlocked),
::testing::ValuesIn(padsEnd5DConstBlocked),
::testing::ValuesIn(argPadValue),
::testing::Values(ngraph::helpers::PadMode::CONSTANT),
::testing::ValuesIn(CPUParams5DBlocked)),
PadLayerCPUTest::getTestCaseName
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPadDynamic5D,
PadLayerCPUTest,
::testing::Combine(
::testing::ValuesIn(inputShapesDynamic5D),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin5D),
::testing::ValuesIn(padsEnd5D),
::testing::Values(0),
::testing::ValuesIn(padMode),
::testing::ValuesIn(CPUParams5DDynamic)),
PadLayerCPUTest::getTestCaseName
);
INSTANTIATE_TEST_SUITE_P(
smoke_CPUPadDynamic5DBlocked,
PadLayerCPUTest,
::testing::Combine(
::testing::Values(inputShapesDynamic5D[1]),
::testing::ValuesIn(inputPrecisions),
::testing::ValuesIn(padsBegin5DBlocked),
::testing::ValuesIn(padsEnd5DBlocked),
::testing::Values(0),
::testing::ValuesIn(padMode),
::testing::ValuesIn(CPUParams5DBlocked)),
PadLayerCPUTest::getTestCaseName
);
/* *======================* *=====================* *======================* */
} // namespace
} // namespace CPULayerTestsDefinitions

12
ngraph/core/src/op/pad.cpp
View File

@ -159,6 +159,18 @@ void op::v1::Pad::validate_and_infer_types() {
"of at least 2 at each "
"spatial axis.");
}
NODE_VALIDATION_CHECK(
this,
m_pad_mode != op::PadMode::REFLECT || (pads_begin_coord[i] < arg_shape[i].get_length() &&
pads_end_coord[i] < arg_shape[i].get_length()),
"REFLECT padding mode requires that 'pads_begin[D]' and 'pads_end[D]' "
"must be not greater than 'data_shape[D] - 1'.");
NODE_VALIDATION_CHECK(
this,
m_pad_mode != op::PadMode::SYMMETRIC || (pads_begin_coord[i] <= arg_shape[i].get_length() &&
pads_end_coord[i] <= arg_shape[i].get_length()),
"SYMMETRIC padding mode requires that 'pads_begin[D]' and 'pads_end[D]' "
"must be not greater than 'data_shape[D]'.");
}
}
set_output_type(0, get_input_element_type(0), result_dims);