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[CPU] Removed custom ShapeInference impl for RandomUniform (#20599)

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This commit is contained in:
Nikolay Shchegolev 2023-10-25 11:15:03 +04:00 committed by GitHub
parent 706d657637
commit dc4240bc61
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GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 34 additions and 127 deletions
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31
src/plugins/intel_cpu/src/nodes/grid_sample.cpp
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@ -2,12 +2,9 @@
// SPDX-License-Identifier: Apache-2.0
//
#include <string>
#include <vector>
#include "grid_sample.hpp"
#include "ie_parallel.hpp"
#include <ngraph/opsets/opset1.hpp>
#include "openvino/op/grid_sample.hpp"
using namespace InferenceEngine;
using namespace ov::intel_cpu;
@ -16,8 +13,6 @@ using namespace ov::intel_cpu::node;
using namespace dnnl::impl::cpu;
#endif // OPENVINO_ARCH_X86_64
#define THROW_ERROR IE_THROW() << getTypeStr() << " node with name '" << getName() << "' "
bool GridSample::isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept {
try {
@ -46,21 +41,21 @@ GridSample::GridSample(const std::shared_ptr<ov::Node>& op, const GraphContext::
: Node(op, context, NgraphShapeInferFactory(op, PortMask(1))) {
std::string errorMessage;
if (!isSupportedOperation(op, errorMessage)) {
IE_THROW(NotImplemented) << errorMessage;
THROW_CPU_NODE_ERR(errorMessage);
}
if (op->get_input_size() != 2 || op->get_output_size() != 1)
THROW_ERROR << "has incorrect number of input/output ports.";
THROW_CPU_NODE_ERR("has incorrect number of input/output ports.");
const auto& dataShape = getInputShapeAtPort(IN_DATA);
if (dataShape.getRank() != 4)
THROW_ERROR << "has incorrect rank of the Data input.";
THROW_CPU_NODE_ERR("has incorrect rank of the Data input.");
const auto& gridShape = getInputShapeAtPort(IN_GRID);
if (gridShape.getRank() != 4)
THROW_ERROR << "has incorrect rank of the Grid input.";
THROW_CPU_NODE_ERR("has incorrect rank of the Grid input.");
if (gridShape.isStatic() && gridShape.getDims()[3] != 2)
THROW_ERROR << "has incorrect shape of the Grid input. The 4th dimension should be equal to 2.";
THROW_CPU_NODE_ERR("has incorrect shape of the Grid input. The 4th dimension should be equal to 2.");
const auto& attributes = ov::as_type_ptr<ov::op::v9::GridSample>(op)->get_attributes();
alignCorners = attributes.align_corners;
@ -75,7 +70,7 @@ GridSample::GridSample(const std::shared_ptr<ov::Node>& op, const GraphContext::
interpolationMode = GridSampleInterpolationMode::NEAREST;
break;
default:
THROW_ERROR << "supports only BILINEAR, BICUBIC, NEAREST interpolation modes.";
THROW_CPU_NODE_ERR("supports only BILINEAR, BICUBIC, NEAREST interpolation modes.");
}
switch (attributes.padding_mode) {
case op::v9::GridSample::PaddingMode::ZEROS:
@ -88,7 +83,7 @@ GridSample::GridSample(const std::shared_ptr<ov::Node>& op, const GraphContext::
paddingMode = GridSamplePaddingMode::REFLECTION;
break;
default:
THROW_ERROR << "supports only BORDER, REFLECTION, ZEROS paddings modes.";
THROW_CPU_NODE_ERR("supports only BORDER, REFLECTION, ZEROS paddings modes.");
}
}
@ -149,7 +144,7 @@ void GridSample::createPrimitive() {
jitKernel.reset(new kernel::GridSampleKernel<x64::sse41>(jcp));
}
if (!jitKernel) {
THROW_ERROR << " could not create JIT kernel.";
THROW_CPU_NODE_ERR("could not create JIT kernel.");
}
jitKernel->create_ker();
@ -187,15 +182,15 @@ void GridSample::createPrimitive() {
void GridSample::prepareParams() {
auto dataMemPtr = getParentEdgeAt(IN_DATA)->getMemoryPtr();
if (!dataMemPtr || !dataMemPtr->isAllocated())
THROW_ERROR << " has not allocated input data memory.";
THROW_CPU_NODE_ERR("has not allocated input data memory.");
auto gridMemPtr = getParentEdgeAt(IN_GRID)->getMemoryPtr();
if (!gridMemPtr || !gridMemPtr->isAllocated())
THROW_ERROR << " has not allocated input grid memory.";
THROW_CPU_NODE_ERR("has not allocated input grid memory.");
auto dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
if (!dstMemPtr || !dstMemPtr->isAllocated())
THROW_ERROR << " has not allocated output memory.";
THROW_CPU_NODE_ERR("has not allocated output memory.");
if (getSelectedPrimitiveDescriptor() == nullptr)
THROW_ERROR << " has unidentified preferable primitive descriptor.";
THROW_CPU_NODE_ERR("has unidentified preferable primitive descriptor.");
const uint64_t dataElPerVec = jitKernel->getDataElPerVec();
const auto& srcDataShape = dataMemPtr->getStaticDims();

4
src/plugins/intel_cpu/src/nodes/grid_sample.hpp
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@ -7,10 +7,6 @@
#include <node.h>
#include "kernels/x64/grid_sample.hpp"
#include <memory>
#include <string>
#include <vector>
namespace ov {
namespace intel_cpu {
namespace node {

6
src/plugins/intel_cpu/src/nodes/kernels/x64/gather_uni_kernel.cpp
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@ -48,7 +48,7 @@ template <x64::cpu_isa_t isa>
void jitUniGatherKernel<isa>::create_ker() {
auto code = x64::jit_generator::create_kernel();
if (code != dnnl::impl::status::success)
IE_THROW() << "Could not create Gather kernel. Error code: " << std::to_string(code);
OPENVINO_THROW("Could not create Gather kernel. Error code: ", std::to_string(code));
ker_ = (decltype(ker_))jit_ker();
}
@ -154,7 +154,7 @@ void jitUniGatherKernel<isa>::generate() {
process(true, true);
} else { // Long case.
IE_THROW() << "Gather kernel does not support static shape with after axis size greater than elements in vector.";
OPENVINO_THROW("Gather kernel does not support static shape with after axis size greater than elements in vector.");
}
}
} else { // Dynamic shapes.
@ -526,7 +526,7 @@ template <x64::cpu_isa_t isa>
void jitUniGatherKernel<isa>::calcSrcShiftLongBlock(Vmm* vAuxPool, bool shiftFirst) {
// Most likely there will no significant performance gain vs memcpy in reference implementation on big blocks after axis,
// therefore no time was invested to this case yet.
IE_THROW() << "Unsupported case.";
OPENVINO_THROW("Unsupported case.");
}
// Requires vAuxPool length 3.

1
src/plugins/intel_cpu/src/nodes/kernels/x64/gather_uni_kernel.hpp
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@ -22,6 +22,7 @@
#pragma once
#include "jit_kernel_base.hpp"
#include "cpu/x64/jit_generator.hpp"
#include <dnnl_types.h>

2
src/plugins/intel_cpu/src/nodes/kernels/x64/grid_sample.cpp
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@ -30,7 +30,7 @@ template <x64::cpu_isa_t isa>
void GridSampleKernel<isa>::create_ker() {
auto code = x64::jit_generator::create_kernel();
if (code != dnnl::impl::status::success)
IE_THROW() << "Could not create GridSample kernel. Error code: " << std::to_string(code);
OPENVINO_THROW("Could not create GridSample kernel. Error code: ", std::to_string(code));
ker_ = (decltype(ker_))jit_ker();
}

26
src/plugins/intel_cpu/src/nodes/kernels/x64/jit_kernel_base.cpp
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@ -89,13 +89,13 @@ void JitKernelBase::uni_vpaddd(const Xbyak::Ymm& v_dst,
paddd(xmmDst, ptr[op.getAddress().getRegExp() + vlen]);
vperm2f128(v_dst, v_dst, v_dst, 0x1);
} else {
IE_THROW() << "Not supported operand type.";
OPENVINO_THROW("Not supported operand type.");
}
} else if (isValidIsa(x64::sse41)) {
assert(v_dst.getIdx() != v_src.getIdx());
paddd(v_dst, op);
} else {
IE_THROW() << "Not defined behavior for instruction 'vpaddd' in current instructions set.";
OPENVINO_THROW("Not defined behavior for instruction 'vpaddd' in current instructions set.");
}
}
@ -136,13 +136,13 @@ void JitKernelBase::uni_vpsubd(const Xbyak::Ymm& v_dst,
psubd(xmmDst, ptr[op.getAddress().getRegExp() + vlen]);
vperm2f128(v_dst, v_dst, v_dst, 0x1);
} else {
IE_THROW() << "Not supported operand type.";
OPENVINO_THROW("Not supported operand type.");
}
} else if (isValidIsa(x64::sse41)) {
assert(v_dst.getIdx() != v_src.getIdx());
psubd(v_dst, op);
} else {
IE_THROW() << "Not defined behavior for instruction 'vpsubd' in current instructions set.";
OPENVINO_THROW("Not defined behavior for instruction 'vpsubd' in current instructions set.");
}
}
@ -244,7 +244,7 @@ void JitKernelBase::gatherdd(const Xbyak::Xmm& v_dst,
const bool useMask,
const bool zeroFill) {
if (kReadMask.getIdx() == 0) {
IE_THROW() << "The vpgatherdd instruction cannot use the register k0 as mask.";
OPENVINO_THROW("The vpgatherdd instruction cannot use the register k0 as mask.");
}
if (!useMask)
kxnord(kReadMask, kReadMask, kReadMask);
@ -261,7 +261,7 @@ void JitKernelBase::gatherdd(const Xbyak::Xmm& v_dst,
const bool useMask,
const bool zeroFill) {
if (v_dst.getIdx() == vSrcShift.getIdx() || v_dst.getIdx() == vReadMask.getIdx() || vSrcShift.getIdx() == vReadMask.getIdx()) {
IE_THROW() << "Any pair of the index, mask, or destination registers cannot be the same.";
OPENVINO_THROW("Any pair of the index, mask, or destination registers cannot be the same.");
}
if (zeroFill)
pxor(v_dst, v_dst); // Don't use vpxor. It zeros the rest of the YMM register.
@ -299,7 +299,7 @@ void JitKernelBase::gatherdd(const Xbyak::Ymm& v_dst,
const bool useMask,
const bool zeroFill) {
if (v_dst.getIdx() == vSrcShift.getIdx() || v_dst.getIdx() == vReadMask.getIdx() || vSrcShift.getIdx() == vReadMask.getIdx()) {
IE_THROW() << "Any pair of the index, mask, or destination registers cannot be the same.";
OPENVINO_THROW("Any pair of the index, mask, or destination registers cannot be the same.");
}
if (isValidIsa(x64::avx2)) {
if (!useMask)
@ -430,7 +430,7 @@ void JitKernelBase::fillRestWorkMask(const Xbyak::Xmm& xmmDstMask,
const Xbyak::Reg64& rWorkRest,
const uint64_t typeSize) {
if (!one_of(typeSize, 1u, 2u, 4u, 8u)) {
IE_THROW() << "Could not fill data with type size " << typeSize;
OPENVINO_THROW("Could not fill data with type size ", typeSize);
}
Xbyak::Label lEnd;
auto r32Ones = getReg32();
@ -459,7 +459,7 @@ void JitKernelBase::fillRestWorkMask(const Xbyak::Ymm& ymmDstMask,
const Xbyak::Reg64& rWorkRest,
const uint64_t typeSize) {
if (!one_of(typeSize, 1u, 2u, 4u, 8u)) {
IE_THROW() << "Could not fill data with type size " << typeSize;
OPENVINO_THROW("Could not fill data with type size ", typeSize);
}
Xbyak::Label lEnd;
auto elPerVec = x64::cpu_isa_traits<x64::sse41>::vlen / typeSize;
@ -499,7 +499,7 @@ void JitKernelBase::load(const Xbyak::Xmm& v_dst,
const size_t typeSize,
const bool zeroFilling) {
if (!one_of(typeSize, 1u, 2u, 4u, 8u)) {
IE_THROW() << "Could not load data with type size " << typeSize;
OPENVINO_THROW("Could not load data with type size ", typeSize);
}
const uint8_t elPerVec = x64::cpu_isa_traits<x64::sse41>::vlen / typeSize;
Xbyak::Label lEnd;
@ -529,7 +529,7 @@ void JitKernelBase::load(const Xbyak::Ymm& v_dst,
const size_t typeSize,
const bool zeroFilling) {
if (!one_of(typeSize, 1u, 2u, 4u, 8u)) {
IE_THROW() << "Could not load data with type size " << typeSize;
OPENVINO_THROW("Could not load data with type size ", typeSize);
}
const size_t elPerXmm = x64::cpu_isa_traits<x64::sse41>::vlen / typeSize;
Xbyak::Label lEnd;
@ -568,7 +568,7 @@ void JitKernelBase::store(const Xbyak::Address& dstAddr,
const Xbyak::Reg64& rToStoreNum,
const size_t typeSize) {
if (!one_of(typeSize, 1u, 2u, 4u, 8u)) {
IE_THROW() << "Could not store data with type size " << typeSize;
OPENVINO_THROW("Could not store data with type size ", typeSize);
}
Xbyak::Label lEnd;
const size_t elPerVec = x64::cpu_isa_traits<x64::sse41>::vlen / typeSize;
@ -596,7 +596,7 @@ void JitKernelBase::store(const Xbyak::Address& dstAddr,
const Xbyak::Reg64& rToStoreNum,
const size_t typeSize) {
if (!one_of(typeSize, 1u, 2u, 4u, 8u)) {
IE_THROW() << "Could not store data with type size " << typeSize;
OPENVINO_THROW("Could not store data with type size ", typeSize);
}
Xbyak::Label lEnd;
Xbyak::Xmm xmmSrc(v_src.getIdx());

7
src/plugins/intel_cpu/src/nodes/random_uniform.cpp
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@ -6,9 +6,8 @@
#include "ie_parallel.hpp"
#include "ie_ngraph_utils.hpp"
#include <openvino/op/constant.hpp>
#include <openvino/op/random_uniform.hpp>
#include "shape_inference/custom/random_uniform.hpp"
#include "openvino/op/constant.hpp"
#include "openvino/op/random_uniform.hpp"
namespace ov {
namespace intel_cpu {
@ -27,7 +26,7 @@ bool RandomUniform::isSupportedOperation(const std::shared_ptr<const ov::Node>&
}
RandomUniform::RandomUniform(const std::shared_ptr<ov::Node>& op, const GraphContext::CPtr& context)
: Node(op, context, RandomUniformShapeInferFactory(op)) {
: Node(op, context, NgraphShapeInferFactory(op, PortMask(0, 1, 2))) {
std::string errorMessage;
if (!isSupportedOperation(op, errorMessage)) {
THROW_CPU_NODE_ERR(errorMessage);

47
src/plugins/intel_cpu/src/shape_inference/custom/random_uniform.cpp
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@ -1,47 +0,0 @@
// Copyright (C) 2018-2023 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include "random_uniform.hpp"
#include <openvino/op/random_uniform.hpp>
namespace ov {
namespace intel_cpu {
namespace node {
// TODO: remove after fixing the issue 123011
IShapeInfer::Result RandomUniformShapeInfer::infer(
const std::vector<std::reference_wrapper<const VectorDims>>& input_shapes,
const std::unordered_map<size_t, MemoryPtr>& data_dependency) {
VectorDims dims;
const auto& mem = data_dependency.at(0);
const auto rank = mem->getShape().getElementsCount();
auto shape_prc = mem->getDesc().getPrecision();
switch (shape_prc) {
case InferenceEngine::Precision::I32: {
auto data = reinterpret_cast<const int32_t*>(mem->getData());
dims.assign(data, data + rank);
} break;
case InferenceEngine::Precision::I64: {
auto data = reinterpret_cast<const int64_t*>(mem->getData());
dims.assign(data, data + rank);
} break;
default:
OPENVINO_THROW("Unexpected Shape input precision: ", shape_prc);
}
return {{dims}, ShapeInferStatus::success};
}
RandomUniformShapeInferFactory::RandomUniformShapeInferFactory(const std::shared_ptr<ov::Node>& op) : m_op(op) {
OPENVINO_ASSERT(ov::is_type<const op::v8::RandomUniform>(m_op),
"Unexpected op type in RandomUniform shape inference factory: ", m_op->get_type_name());
}
ShapeInferPtr RandomUniformShapeInferFactory::makeShapeInfer() const {
return std::make_shared<RandomUniformShapeInfer>();
}
} // namespace node
} // namespace intel_cpu
} // namespace ov

37
src/plugins/intel_cpu/src/shape_inference/custom/random_uniform.hpp
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@ -1,37 +0,0 @@
// Copyright (C) 2018-2023 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include "shape_inference/shape_inference_cpu.hpp"
#include <node.h>
#pragma once
namespace ov {
namespace intel_cpu {
namespace node {
class RandomUniformShapeInfer : public ShapeInferEmptyPads {
public:
explicit RandomUniformShapeInfer() {}
IShapeInfer::Result infer(
const std::vector<std::reference_wrapper<const VectorDims>>& input_shapes,
const std::unordered_map<size_t, MemoryPtr>& data_dependency) override;
port_mask_t get_port_mask() const override {
return PortMask(0);
}
};
class RandomUniformShapeInferFactory : public ShapeInferFactory {
public:
explicit RandomUniformShapeInferFactory(const std::shared_ptr<ov::Node>& op);
ShapeInferPtr makeShapeInfer() const override;
private:
std::shared_ptr<ov::Node> m_op;
};
} // namespace node
} // namespace intel_cpu
} // namespace ov