blob: 9f7d562df648e18de5b5a57a6642311095a61dbe [file] [log] [blame]
//
// Copyright © 2017-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "ClLayerSupport.hpp"
#include "ClBackendId.hpp"
#include "ClBackendModelContext.hpp"
#include <armnn/BackendRegistry.hpp>
#include <InternalTypes.hpp>
#include <LayerSupportCommon.hpp>
#include <armnn/utility/IgnoreUnused.hpp>
#include <armnn/utility/PolymorphicDowncast.hpp>
#if defined(ARMCOMPUTECL_ENABLED)
#include <aclCommon/ArmComputeUtils.hpp>
#include <aclCommon/ArmComputeTensorUtils.hpp>
#include <backendsCommon/WorkloadUtils.hpp>
#include "workloads/ClAbsWorkload.hpp"
#include "workloads/ClAdditionWorkload.hpp"
#include "workloads/ClActivationWorkload.hpp"
#include "workloads/ClArgMinMaxWorkload.hpp"
#include "workloads/ClBatchMatMulWorkload.hpp"
#include "workloads/ClBatchNormalizationFloatWorkload.hpp"
#include "workloads/ClBatchToSpaceNdWorkload.hpp"
#include "workloads/ClCastWorkload.hpp"
#include "workloads/ClChannelShuffleWorkload.hpp"
#include "workloads/ClComparisonWorkload.hpp"
#include "workloads/ClConstantWorkload.hpp"
#include "workloads/ClConvertFp16ToFp32Workload.hpp"
#include "workloads/ClConvertFp32ToFp16Workload.hpp"
#include "workloads/ClConvolution2dWorkload.hpp"
#include "workloads/ClConvolution3dWorkload.hpp"
#include "workloads/ClDepthToSpaceWorkload.hpp"
#include "workloads/ClDepthwiseConvolutionWorkload.hpp"
#include "workloads/ClDequantizeWorkload.hpp"
#include "workloads/ClDivisionWorkload.hpp"
#include "workloads/ClElementwiseBinaryWorkload.hpp"
#include "workloads/ClExpWorkload.hpp"
#include "workloads/ClFillWorkload.hpp"
#include "workloads/ClFloorFloatWorkload.hpp"
#include "workloads/ClFullyConnectedWorkload.hpp"
#include "workloads/ClGatherWorkload.hpp"
#include "workloads/ClGatherNdWorkload.hpp"
#include "workloads/ClInstanceNormalizationWorkload.hpp"
#include "workloads/ClL2NormalizationFloatWorkload.hpp"
#include "workloads/ClLogWorkload.hpp"
#include "workloads/ClLogSoftmaxWorkload.hpp"
#include "workloads/ClLogicalAndWorkload.hpp"
#include "workloads/ClLogicalNotWorkload.hpp"
#include "workloads/ClLogicalOrWorkload.hpp"
#include "workloads/ClLstmFloatWorkload.hpp"
#include "workloads/ClMaximumWorkload.hpp"
#include "workloads/ClMeanWorkload.hpp"
#include "workloads/ClConcatWorkload.hpp"
#include "workloads/ClMinimumWorkload.hpp"
#include "workloads/ClMultiplicationWorkload.hpp"
#include "workloads/ClNegWorkload.hpp"
#include "workloads/ClNormalizationFloatWorkload.hpp"
#include "workloads/ClPadWorkload.hpp"
#include "workloads/ClPermuteWorkload.hpp"
#include "workloads/ClPooling2dWorkload.hpp"
#include "workloads/ClPooling3dWorkload.hpp"
#include "workloads/ClPreluWorkload.hpp"
#include "workloads/ClQLstmWorkload.hpp"
#include "workloads/ClQuantizedLstmWorkload.hpp"
#include "workloads/ClQuantizeWorkload.hpp"
#include "workloads/ClReduceWorkload.hpp"
#include "workloads/ClReshapeWorkload.hpp"
#include "workloads/ClResizeWorkload.hpp"
#include "workloads/ClReverseV2Workload.hpp"
#include "workloads/ClRsqrtWorkload.hpp"
#include "workloads/ClSinWorkload.hpp"
#include "workloads/ClSliceWorkload.hpp"
#include "workloads/ClSoftmaxWorkload.hpp"
#include "workloads/ClSpaceToBatchNdWorkload.hpp"
#include "workloads/ClSpaceToDepthWorkload.hpp"
#include "workloads/ClSplitterWorkload.hpp"
#include "workloads/ClSqrtWorkload.hpp"
#include "workloads/ClStackWorkload.hpp"
#include "workloads/ClStridedSliceWorkload.hpp"
#include "workloads/ClSubtractionWorkload.hpp"
#include "workloads/ClTileWorkload.hpp"
#include "workloads/ClTransposeConvolution2dWorkload.hpp"
#include "workloads/ClTransposeWorkload.hpp"
#include "workloads/ClUnidirectionalSequenceLstmFloatWorkload.hpp"
#endif
namespace armnn
{
namespace
{
template<unsigned int FilterSize>
bool IsMatchingSize2d(const TensorInfo& weightInfo)
{
// Width & Height must match.
return (weightInfo.GetShape()[3] == FilterSize) && (weightInfo.GetShape()[2] == FilterSize);
}
template<uint32_t ValidStride>
bool IsMatchingStride(uint32_t actualStride)
{
return ValidStride == actualStride;
}
template<uint32_t FirstStride, uint32_t SecondStride, uint32_t... ValidStrides>
bool IsMatchingStride(uint32_t actualStride)
{
return IsMatchingStride<FirstStride>(actualStride) || IsMatchingStride<SecondStride, ValidStrides...>(actualStride);
}
template<typename ... Args>
bool IsClBackendSupported(Optional<std::string&> reasonIfUnsupported, Args... args)
{
IgnoreUnused(reasonIfUnsupported, (args)...);
#if defined(ARMCOMPUTECL_ENABLED)
return true;
#else
if (reasonIfUnsupported)
{
reasonIfUnsupported.value() = "The armnn library has been built without CL support";
}
return false;
#endif
}
#if defined(ARMCOMPUTECL_ENABLED)
#define FORWARD_CL_LAYER_SUPPORT_FUNC(expr) (expr)
#else
#define FORWARD_CL_LAYER_SUPPORT_FUNC(expr) IsClBackendSupported(reasonIfUnsupported)
#endif
#if defined(ARMCOMPUTECL_ENABLED)
template<class FuncType, class... Args>
inline bool IsWorkloadSupported(FuncType&& func, Optional<std::string&> reasonIfUnsupported, Args&&... args)
{
arm_compute::Status aclStatus = func(std::forward<Args>(args)...);
const bool supported = (aclStatus.error_code() == arm_compute::ErrorCode::OK);
if (!supported && reasonIfUnsupported)
{
reasonIfUnsupported.value() = aclStatus.error_description();
}
return supported;
}
#define FORWARD_WORKLOAD_VALIDATE_FUNC(func, reasonIfUnsupported, ...) \
return IsWorkloadSupported(func, reasonIfUnsupported, __VA_ARGS__);
#else
#define FORWARD_WORKLOAD_VALIDATE_FUNC(func, reasonIfUnsupported, ...) \
return IsClBackendSupported(reasonIfUnsupported, __VA_ARGS__);
#endif
template<typename FloatFunc, typename Uint8Func, typename ... Params>
bool IsSupportedForDataTypeCl(Optional<std::string&> reasonIfUnsupported,
DataType dataType,
FloatFunc floatFuncPtr,
Uint8Func uint8FuncPtr,
Params&&... params)
{
return IsClBackendSupported(reasonIfUnsupported) &&
IsSupportedForDataTypeGeneric(reasonIfUnsupported,
dataType,
floatFuncPtr,
floatFuncPtr,
uint8FuncPtr,
&FalseFunc<>,
&FalseFunc<>,
std::forward<Params>(params)...);
}
} // anonymous namespace
ClLayerSupport::ClLayerSupport(const IBackendInternal::IBackendSpecificModelContextPtr& modelContextPtr)
: m_ModelContextPtr(modelContextPtr)
{
}
ClLayerSupport::ClLayerSupport()
: m_ModelContextPtr(nullptr)
{
}
bool ClLayerSupport::IsLayerSupported(const LayerType& type,
const std::vector<TensorInfo>& infos,
const BaseDescriptor& descriptor,
const Optional<LstmInputParamsInfo>& lstmParamsInfo,
const Optional<QuantizedLstmInputParamsInfo>& quantizedLstmParamsInfo,
Optional<std::string&> reasonIfUnsupported) const
{
switch (type)
{
case LayerType::Activation:
return IsActivationSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const ActivationDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Addition:
ARMNN_NO_DEPRECATE_WARN_BEGIN
return IsAdditionSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
ARMNN_NO_DEPRECATE_WARN_END
case LayerType::ArgMinMax:
return IsArgMinMaxSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const ArgMinMaxDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::BatchMatMul:
return IsBatchMatMulSupported(infos[0],
infos[1],
infos[2],
*(PolymorphicDowncast<const BatchMatMulDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::BatchNormalization:
return IsBatchNormalizationSupported(infos[0],
infos[1],
infos[2],
infos[3],
infos[4],
infos[5],
*(PolymorphicDowncast<const BatchNormalizationDescriptor*>
(&descriptor)),
reasonIfUnsupported);
case LayerType::BatchToSpaceNd:
return IsBatchToSpaceNdSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const BatchToSpaceNdDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Cast:
return IsCastSupported(infos[0], infos[1], reasonIfUnsupported);
case LayerType::ChannelShuffle:
return IsChannelShuffleSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const ChannelShuffleDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Comparison:
return IsComparisonSupported(infos[0],
infos[1],
infos[2],
*(PolymorphicDowncast<const ComparisonDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Concat:
{
std::vector<const TensorInfo*> inputInfos;
for (uint32_t i = 0; i < (infos.size() - 1); i++)
{
inputInfos.push_back(&infos[i]);
}
return IsConcatSupported(inputInfos,
infos[infos.size() - 1],
*(PolymorphicDowncast<const OriginsDescriptor*>(&descriptor)),
reasonIfUnsupported);
}
case LayerType::Constant:
return IsConstantSupported(infos[0], reasonIfUnsupported);
case LayerType::ConvertFp16ToFp32:
return IsConvertFp16ToFp32Supported(infos[0], infos[1], reasonIfUnsupported);
case LayerType::ConvertFp32ToFp16:
return IsConvertFp32ToFp16Supported(infos[0], infos[1], reasonIfUnsupported);
case LayerType::Convolution2d:
{
if (infos.size() != 4)
{
throw InvalidArgumentException("Invalid number of Convolution2d TensorInfos. "
"TensorInfos should be of format: {input, output, weights, biases}.");
}
auto desc = *(PolymorphicDowncast<const Convolution2dDescriptor*>(&descriptor));
if (infos[3] == TensorInfo())
{
return IsConvolution2dSupported(infos[0],
infos[1],
desc,
infos[2],
EmptyOptional(),
reasonIfUnsupported);
}
else
{
return IsConvolution2dSupported(infos[0],
infos[1],
desc,
infos[2],
infos[3],
reasonIfUnsupported);
}
}
case LayerType::Convolution3d:
{
if (infos.size() != 4)
{
throw InvalidArgumentException("Invalid number of Convolution3d TensorInfos. "
"TensorInfos should be of format: {input, output, weights, biases}.");
}
auto desc = *(PolymorphicDowncast<const Convolution3dDescriptor*>(&descriptor));
if (infos[3] == TensorInfo())
{
return IsConvolution3dSupported(infos[0],
infos[1],
desc,
infos[2],
EmptyOptional(),
reasonIfUnsupported);
}
else
{
return IsConvolution3dSupported(infos[0],
infos[1],
desc,
infos[2],
infos[3],
reasonIfUnsupported);
}
}
case LayerType::DepthToSpace:
return IsDepthToSpaceSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const DepthToSpaceDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::DepthwiseConvolution2d:
{
if (infos.size() != 4)
{
throw InvalidArgumentException("Invalid number of DepthwiseConvolution2d TensorInfos. "
"TensorInfos should be of format: {input, output, weights, biases}.");
}
auto desc = *(PolymorphicDowncast<const DepthwiseConvolution2dDescriptor*>(&descriptor));
if (infos[3] == TensorInfo())
{
return IsDepthwiseConvolutionSupported(infos[0],
infos[1],
desc,
infos[2],
EmptyOptional(),
reasonIfUnsupported);
}
else
{
return IsDepthwiseConvolutionSupported(infos[0],
infos[1],
desc,
infos[2],
infos[3],
reasonIfUnsupported);
}
}
case LayerType::Dequantize:
return IsDequantizeSupported(infos[0], infos[1], reasonIfUnsupported);
case LayerType::Division:
ARMNN_NO_DEPRECATE_WARN_BEGIN
return IsDivisionSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
ARMNN_NO_DEPRECATE_WARN_END
case LayerType::ElementwiseBinary:
{
auto desc = *(PolymorphicDowncast<const ElementwiseBinaryDescriptor *>(&descriptor));
switch (desc.m_Operation)
{
case BinaryOperation::Add:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClAdditionValidate,
reasonIfUnsupported,
infos[0],
infos[1],
infos[2],
nullptr);
case BinaryOperation::Div:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClDivisionWorkloadValidate,
reasonIfUnsupported,
infos[0],
infos[1],
infos[2],
nullptr);
case BinaryOperation::Minimum:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClMinimumWorkloadValidate,
reasonIfUnsupported,
infos[0],
infos[1],
infos[2]);
case BinaryOperation::Maximum:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClMaximumWorkloadValidate,
reasonIfUnsupported,
infos[0],
infos[1],
infos[2]);
case BinaryOperation::Mul:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClMultiplicationWorkloadValidate,
reasonIfUnsupported,
infos[0],
infos[1],
infos[2],
nullptr);
case BinaryOperation::Power:
case BinaryOperation::SqDiff:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClElementwiseBinaryValidate,
reasonIfUnsupported,
infos[0],
infos[1],
infos[2],
desc,
nullptr);
case BinaryOperation::Sub:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClSubtractionValidate,
reasonIfUnsupported,
infos[0],
infos[1],
infos[2],
nullptr);
default:
return false;
}
}
case LayerType::ElementwiseUnary:
return IsElementwiseUnarySupported(infos[0],
infos[1],
*(PolymorphicDowncast<const ElementwiseUnaryDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Fill:
return IsFillSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const FillDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Floor:
return IsFloorSupported(infos[0], infos[1], reasonIfUnsupported);
case LayerType::FullyConnected:
return IsFullyConnectedSupported(infos[0],
infos[1],
infos[2],
infos[3],
*(PolymorphicDowncast<const FullyConnectedDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Gather:
return IsGatherSupported(infos[0],
infos[1],
infos[2],
*(PolymorphicDowncast<const GatherDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::GatherNd:
return IsGatherNdSupported(infos[0],
infos[1],
infos[2],
reasonIfUnsupported);
case LayerType::Input:
return IsInputSupported(infos[0], reasonIfUnsupported);
case LayerType::InstanceNormalization:
return IsInstanceNormalizationSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const InstanceNormalizationDescriptor*>
(&descriptor)),
reasonIfUnsupported);
case LayerType::L2Normalization:
return IsL2NormalizationSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const L2NormalizationDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::LogicalBinary:
return IsLogicalBinarySupported(infos[0],
infos[1],
infos[2],
*(PolymorphicDowncast<const LogicalBinaryDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::LogSoftmax:
return IsLogSoftmaxSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const LogSoftmaxDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Lstm:
return IsLstmSupported(infos[0],
infos[1],
infos[2],
infos[3],
infos[4],
infos[5],
infos[6],
*(PolymorphicDowncast<const LstmDescriptor*>(&descriptor)),
lstmParamsInfo.value(),
reasonIfUnsupported);
case LayerType::Map:
return true;
case LayerType::MemCopy:
return LayerSupportBase::IsMemCopySupported(infos[0], infos[1], reasonIfUnsupported);
case LayerType::MemImport:
return LayerSupportBase::IsMemImportSupported(infos[0], infos[1], reasonIfUnsupported);
case LayerType::Merge:
return LayerSupportBase::IsMergeSupported(infos[0],
infos[1],
infos[2],
reasonIfUnsupported);
case LayerType::Maximum:
ARMNN_NO_DEPRECATE_WARN_BEGIN
return IsMaximumSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
ARMNN_NO_DEPRECATE_WARN_END
case LayerType::Mean:
return IsMeanSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const MeanDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Minimum:
ARMNN_NO_DEPRECATE_WARN_BEGIN
return IsMinimumSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
ARMNN_NO_DEPRECATE_WARN_END
case LayerType::Multiplication:
ARMNN_NO_DEPRECATE_WARN_BEGIN
return IsMultiplicationSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
ARMNN_NO_DEPRECATE_WARN_END
case LayerType::Normalization:
return IsNormalizationSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const NormalizationDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Output:
return IsOutputSupported(infos[0], reasonIfUnsupported);
case LayerType::Pad:
return IsPadSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const PadDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Permute:
return IsPermuteSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const PermuteDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Pooling2d:
return IsPooling2dSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const Pooling2dDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Pooling3d:
return IsPooling3dSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const Pooling3dDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Prelu:
return IsPreluSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
case LayerType::QLstm:
return IsQLstmSupported(infos[0],
infos[1],
infos[2],
infos[3],
infos[4],
infos[5],
*(PolymorphicDowncast<const QLstmDescriptor*>(&descriptor)),
lstmParamsInfo.value(),
reasonIfUnsupported);
case LayerType::Quantize:
return IsQuantizeSupported(infos[0], infos[1], reasonIfUnsupported);
case LayerType::QuantizedLstm:
return IsQuantizedLstmSupported(infos[0],
infos[1],
infos[2],
infos[3],
infos[4],
quantizedLstmParamsInfo.value(),
reasonIfUnsupported);
case LayerType::Rank:
return true;
case LayerType::Reduce:
return IsReduceSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const ReduceDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Reshape:
return IsReshapeSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const ReshapeDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Resize:
return IsResizeSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const ResizeDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::ReverseV2:
return IsReverseV2Supported(infos[0],
infos[1],
infos[2],
reasonIfUnsupported);
case LayerType::Shape:
return LayerSupportBase::IsShapeSupported(infos[0],
infos[1],
reasonIfUnsupported);
case LayerType::Slice:
return IsSliceSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const SliceDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Softmax:
return IsSoftmaxSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const SoftmaxDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::SpaceToBatchNd:
return IsSpaceToBatchNdSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const SpaceToBatchNdDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::SpaceToDepth:
return IsSpaceToDepthSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const SpaceToDepthDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Splitter:
{
std::vector<TensorInfo> outputInfos;
for (uint32_t i = 1; i < infos.size(); i++)
{
outputInfos.push_back(infos[i]);
}
return IsSplitterSupported(infos[0],
{outputInfos.begin(), outputInfos.end()},
*(PolymorphicDowncast<const ViewsDescriptor*>(&descriptor)),
reasonIfUnsupported);
}
case LayerType::Stack:
{
std::vector<const TensorInfo*> inputInfos;
for (uint32_t i = 0; i < infos.size() - 1; i++)
{
inputInfos.push_back(&infos[i]);
}
return IsStackSupported(inputInfos,
infos[infos.size() - 1],
*(PolymorphicDowncast<const StackDescriptor*>(&descriptor)),
reasonIfUnsupported);
}
case LayerType::StridedSlice:
return IsStridedSliceSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const StridedSliceDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Subtraction:
ARMNN_NO_DEPRECATE_WARN_BEGIN
return IsSubtractionSupported(infos[0], infos[1], infos[2], reasonIfUnsupported);
ARMNN_NO_DEPRECATE_WARN_END
case LayerType::Tile:
return IsTileSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const TileDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::Transpose:
return IsTransposeSupported(infos[0],
infos[1],
*(PolymorphicDowncast<const TransposeDescriptor*>(&descriptor)),
reasonIfUnsupported);
case LayerType::TransposeConvolution2d:
{
if (infos.size() != 4)
{
throw InvalidArgumentException("Invalid number of TransposeConvolution2d TensorInfos. "
"TensorInfos should be of format: {input, output, weights, biases}.");
}
auto desc = *(PolymorphicDowncast<const TransposeConvolution2dDescriptor*>(&descriptor));
if (infos[3] == TensorInfo())
{
return IsTransposeConvolution2dSupported(infos[0],
infos[1],
desc,
infos[2],
EmptyOptional(),
reasonIfUnsupported);
}
else
{
return IsTransposeConvolution2dSupported(infos[0],
infos[1],
desc,
infos[2],
infos[3],
reasonIfUnsupported);
}
}
case LayerType::UnidirectionalSequenceLstm:
return IsUnidirectionalSequenceLstmSupported(infos[0],
infos[1],
infos[2],
infos[3],
infos[4],
infos[5],
*(PolymorphicDowncast<const
UnidirectionalSequenceLstmDescriptor*>(&descriptor)),
lstmParamsInfo.value(),
reasonIfUnsupported);
case LayerType::Unmap:
return true;
default:
// layers not supported in cl by default:
// debug, detectionpostprocess, fakequantization,
// precompiled, standin, switch, pooling3d, fused
return false;
}
}
bool ClLayerSupport::IsActivationSupported(const TensorInfo& input,
const TensorInfo& output,
const ActivationDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClActivationWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsAdditionSupported(const TensorInfo& input0,
const TensorInfo& input1,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClAdditionValidate,
reasonIfUnsupported,
input0,
input1,
output,
nullptr);
}
bool ClLayerSupport::IsArgMinMaxSupported(const TensorInfo& input,
const TensorInfo& output,
const ArgMinMaxDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClArgMinMaxWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsBatchMatMulSupported(const TensorInfo& inputX,
const TensorInfo& inputY,
const TensorInfo& output,
const BatchMatMulDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClBatchMatMulValidate,
reasonIfUnsupported,
inputX,
inputY,
output,
descriptor,
nullptr);
}
bool ClLayerSupport::IsBatchNormalizationSupported(const TensorInfo& input,
const TensorInfo& output,
const TensorInfo& mean,
const TensorInfo& var,
const TensorInfo& beta,
const TensorInfo& gamma,
const BatchNormalizationDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClBatchNormalizationValidate,
reasonIfUnsupported,
input,
output,
mean,
var,
beta,
gamma,
descriptor,
nullptr);
}
bool ClLayerSupport::IsBatchToSpaceNdSupported(const TensorInfo& input,
const TensorInfo& output,
const BatchToSpaceNdDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClBatchToSpaceNdWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsCastSupported(const TensorInfo& input,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClCastValidate,
reasonIfUnsupported,
input,
output);
}
bool ClLayerSupport::IsChannelShuffleSupported(const TensorInfo& input,
const TensorInfo& output,
const ChannelShuffleDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClChannelShuffleValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsComparisonSupported(const TensorInfo& input0,
const TensorInfo& input1,
const TensorInfo& output,
const ComparisonDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClComparisonWorkloadValidate,
reasonIfUnsupported,
input0,
input1,
output,
descriptor);
}
bool ClLayerSupport::IsConcatSupported(const std::vector<const TensorInfo*> inputs,
const TensorInfo& output,
const OriginsDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
if (descriptor.GetNumDimensions() <= descriptor.GetConcatAxis())
{
SetValueChecked(reasonIfUnsupported, "Cl Concat: Concat axis > Number of dimensions.");
return false;
}
unsigned int concatInnerAxis = (descriptor.GetNumDimensions() - descriptor.GetConcatAxis()) - 1;
if(concatInnerAxis < 3) // Width, height, or channels
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClConcatWorkloadValidate,
reasonIfUnsupported,
inputs,
output,
descriptor);
}
else if (concatInnerAxis == 3)
{
// We rely on the sub-tensor optimization to handle the batch dimension for 4D tensors. If we can't use
// sub-tensors for this then we can't support it. Here is where we check that the sub-tensors will work.
for (auto& input : inputs)
{
if (input && !output.IsTypeSpaceMatch(*input)) // Cannot use sub-tensors if the types are not same space
{
SetValueChecked(reasonIfUnsupported, "Cl Concat: Types and quantization parameters must match.");
return false;
}
}
return true; // Sub-tensors support concat along batch
}
else // > 4 dimensions not supported.
{
SetValueChecked(reasonIfUnsupported, "Cl Concat: Maximum of 4 dimensions supported.");
return false;
}
}
bool ClLayerSupport::IsConstantSupported(const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClConstantWorkloadValidate,
reasonIfUnsupported,
output);
}
bool ClLayerSupport::IsConvertFp16ToFp32Supported(const TensorInfo& input,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClConvertFp16ToFp32WorkloadValidate,
reasonIfUnsupported,
input,
output);
}
bool ClLayerSupport::IsConvertFp32ToFp16Supported(const TensorInfo& input,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClConvertFp32ToFp16WorkloadValidate,
reasonIfUnsupported,
input,
output);
}
bool ClLayerSupport::IsConvolution2dSupported(const TensorInfo& input,
const TensorInfo& output,
const Convolution2dDescriptor& descriptor,
const TensorInfo& weights,
const Optional<TensorInfo>& biases,
Optional<std::string&> reasonIfUnsupported) const
{
bool isFastMathEnabled = false;
#if defined(ARMCOMPUTECL_ENABLED)
if (m_ModelContextPtr)
{
if (m_ModelContextPtr.get() != nullptr)
{
auto modelOptions = dynamic_cast<ClBackendModelContext*>(m_ModelContextPtr.get());
if (modelOptions)
{
isFastMathEnabled = modelOptions->IsFastMathEnabled();
}
}
}
#endif
FORWARD_WORKLOAD_VALIDATE_FUNC(ClConvolution2dWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor,
weights,
biases,
isFastMathEnabled,
nullptr);
}
bool ClLayerSupport::IsConvolution3dSupported(const TensorInfo& input,
const TensorInfo& output,
const Convolution3dDescriptor& descriptor,
const TensorInfo& weights,
const Optional<TensorInfo>& biases,
Optional<std::string&> reasonIfUnsupported) const
{
bool isFastMathEnabled = false;
#if defined(ARMCOMPUTECL_ENABLED)
if (m_ModelContextPtr)
{
if (m_ModelContextPtr.get() != nullptr)
{
auto modelOptions = dynamic_cast<ClBackendModelContext*>(m_ModelContextPtr.get());
if (modelOptions)
{
isFastMathEnabled = modelOptions->IsFastMathEnabled();
}
}
}
#endif
FORWARD_WORKLOAD_VALIDATE_FUNC(ClConvolution3dWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor,
weights,
biases,
isFastMathEnabled,
nullptr);
}
bool ClLayerSupport::IsDequantizeSupported(const TensorInfo& input,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClDequantizeWorkloadValidate,
reasonIfUnsupported,
input,
output);
}
bool ClLayerSupport::IsDepthToSpaceSupported(const TensorInfo& input,
const TensorInfo& output,
const DepthToSpaceDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClDepthToSpaceWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsDepthwiseConvolutionSupported(const TensorInfo& input,
const TensorInfo& output,
const DepthwiseConvolution2dDescriptor& descriptor,
const TensorInfo& weights,
const Optional<TensorInfo>& biases,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClDepthwiseConvolutionWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor,
weights,
biases,
nullptr);
}
bool ClLayerSupport::IsDilatedDepthwiseConvolutionSupported(const TensorInfo& input,
const TensorInfo& output,
const DepthwiseConvolution2dDescriptor& descriptor,
const TensorInfo& weights,
const Optional<TensorInfo>& biases,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClDepthwiseConvolutionWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor,
weights,
biases,
nullptr);
}
bool ClLayerSupport::IsDivisionSupported(const TensorInfo& input0,
const TensorInfo& input1,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClDivisionWorkloadValidate,
reasonIfUnsupported,
input0,
input1,
output,
nullptr);
}
bool ClLayerSupport::IsElementwiseUnarySupported(const TensorInfo& input,
const TensorInfo& output,
const ElementwiseUnaryDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
switch(descriptor.m_Operation)
{
case UnaryOperation::Abs:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClAbsWorkloadValidate,
reasonIfUnsupported,
input,
output);
case UnaryOperation::Exp:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClExpWorkloadValidate,
reasonIfUnsupported,
input,
output);
case UnaryOperation::Log:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClLogWorkloadValidate,
reasonIfUnsupported,
input,
output);
case UnaryOperation::LogicalNot:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClLogicalNotWorkloadValidate,
reasonIfUnsupported,
input,
output);
case UnaryOperation::Neg:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClNegWorkloadValidate,
reasonIfUnsupported,
input,
output);
case UnaryOperation::Rsqrt:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClRsqrtWorkloadValidate,
reasonIfUnsupported,
input,
output);
case UnaryOperation::Sin:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClSinWorkloadValidate,
reasonIfUnsupported,
input,
output);
case UnaryOperation::Sqrt:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClSqrtWorkloadValidate,
reasonIfUnsupported,
input,
output);
default:
return false;
}
}
bool ClLayerSupport::IsFillSupported(const TensorInfo& input,
const TensorInfo& output,
const FillDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
armnn::IgnoreUnused(input);
armnn::IgnoreUnused(output);
armnn::IgnoreUnused(descriptor);
return IsClBackendSupported(reasonIfUnsupported);
}
bool ClLayerSupport::IsFloorSupported(const TensorInfo& input,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClFloorWorkloadValidate,
reasonIfUnsupported,
input,
output);
}
bool ClLayerSupport::IsFullyConnectedSupported(const TensorInfo& input,
const TensorInfo& output,
const TensorInfo& weights,
const TensorInfo& biases,
const FullyConnectedDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClFullyConnectedWorkloadValidate,
reasonIfUnsupported,
input,
output,
weights,
biases,
descriptor,
nullptr);
}
bool ClLayerSupport::IsGatherSupported(const TensorInfo& input0,
const TensorInfo& input1,
const TensorInfo& output,
const GatherDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClGatherWorkloadValidate,
reasonIfUnsupported,
input0,
input1,
output,
descriptor);
}
bool ClLayerSupport::IsGatherNdSupported(const TensorInfo& input0,
const TensorInfo& input1,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClGatherNdWorkloadValidate,
reasonIfUnsupported,
input0,
input1,
output);
}
bool ClLayerSupport::IsInputSupported(const TensorInfo& input,
Optional<std::string&> reasonIfUnsupported) const
{
return IsClBackendSupported(reasonIfUnsupported, input);
}
bool ClLayerSupport::IsInstanceNormalizationSupported(const TensorInfo& input,
const TensorInfo& output,
const InstanceNormalizationDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClInstanceNormalizationWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsL2NormalizationSupported(const TensorInfo& input,
const TensorInfo& output,
const L2NormalizationDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClL2NormalizationWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsLogicalBinarySupported(const TensorInfo& input0,
const TensorInfo& input1,
const TensorInfo& output,
const LogicalBinaryDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
IgnoreUnused(output);
switch(descriptor.m_Operation)
{
case LogicalBinaryOperation::LogicalAnd:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClLogicalAndWorkloadValidate,
reasonIfUnsupported,
input0,
input1,
output);
case LogicalBinaryOperation::LogicalOr:
FORWARD_WORKLOAD_VALIDATE_FUNC(ClLogicalOrWorkloadValidate,
reasonIfUnsupported,
input0,
input1,
output);
default:
return false;
}
}
bool ClLayerSupport::IsLogSoftmaxSupported(const TensorInfo& input,
const TensorInfo& output,
const LogSoftmaxDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClLogSoftmaxWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsLstmSupported(const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
const TensorInfo& scratchBuffer,
const TensorInfo& outputStateOut,
const TensorInfo& cellStateOut,
const TensorInfo& output,
const LstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClLstmFloatWorkloadValidate,
reasonIfUnsupported,
input,
outputStateIn,
cellStateIn,
scratchBuffer,
outputStateOut,
cellStateOut,
output,
descriptor,
paramsInfo);
}
bool ClLayerSupport::IsMaximumSupported(const TensorInfo& input0,
const TensorInfo& input1,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClMaximumWorkloadValidate,
reasonIfUnsupported,
input0,
input1,
output);
}
bool ClLayerSupport::IsMeanSupported(const TensorInfo& input,
const TensorInfo& output,
const MeanDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClMeanValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsMinimumSupported(const TensorInfo& input0,
const TensorInfo& input1,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClMinimumWorkloadValidate,
reasonIfUnsupported,
input0,
input1,
output);
}
bool ClLayerSupport::IsMultiplicationSupported(const TensorInfo& input0,
const TensorInfo& input1,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClMultiplicationWorkloadValidate,
reasonIfUnsupported,
input0,
input1,
output,
nullptr);
}
bool ClLayerSupport::IsNormalizationSupported(const TensorInfo& input,
const TensorInfo& output,
const NormalizationDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClNormalizationWorkloadValidate, reasonIfUnsupported, input, output, descriptor);
}
bool ClLayerSupport::IsOutputSupported(const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
return IsClBackendSupported(reasonIfUnsupported, output);
}
bool ClLayerSupport::IsPadSupported(const TensorInfo& input,
const TensorInfo& output,
const PadDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClPadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsPermuteSupported(const TensorInfo& input,
const TensorInfo& output,
const PermuteDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClPermuteWorkloadValidate, reasonIfUnsupported, input, output, descriptor);
}
bool ClLayerSupport::IsPooling2dSupported(const TensorInfo& input,
const TensorInfo& output,
const Pooling2dDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClPooling2dWorkloadValidate, reasonIfUnsupported, input, output, descriptor);
}
bool ClLayerSupport::IsPooling3dSupported(const TensorInfo& input,
const TensorInfo& output,
const Pooling3dDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClPooling3dWorkloadValidate, reasonIfUnsupported, input, output, descriptor);
}
bool ClLayerSupport::IsPreluSupported(const armnn::TensorInfo &input,
const armnn::TensorInfo &alpha,
const armnn::TensorInfo &output,
armnn::Optional<std::string &> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClPreluWorkloadValidate, reasonIfUnsupported, input, alpha, output);
}
bool ClLayerSupport::IsQLstmSupported(const TensorInfo& input,
const TensorInfo& previousOutputIn,
const TensorInfo& previousCellStateIn,
const TensorInfo& outputStateOut,
const TensorInfo& cellStateOut,
const TensorInfo& output,
const QLstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported) const
{
if (input.GetDataType() == armnn::DataType::QAsymmS8 &&
previousOutputIn.GetDataType() == armnn::DataType::QAsymmS8 &&
previousCellStateIn.GetDataType() == armnn::DataType::QSymmS16 &&
outputStateOut.GetDataType() == armnn::DataType::QAsymmS8 &&
cellStateOut.GetDataType() == armnn::DataType::QSymmS16 &&
output.GetDataType() == armnn::DataType::QAsymmS8)
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClQLstmWorkloadValidate,
reasonIfUnsupported,
input,
previousCellStateIn,
previousOutputIn,
cellStateOut,
outputStateOut,
output,
descriptor,
paramsInfo);
}
else
{
return false;
}
}
bool ClLayerSupport::IsQuantizedLstmSupported(const TensorInfo& input,
const TensorInfo& previousCellStateIn,
const TensorInfo& previousOutputIn,
const TensorInfo& cellStateOut,
const TensorInfo& output,
const QuantizedLstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClQuantizedLstmWorkloadValidate,
reasonIfUnsupported,
input,
previousCellStateIn,
previousOutputIn,
cellStateOut,
output,
paramsInfo);
}
bool ClLayerSupport::IsQuantizeSupported(const TensorInfo& input,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClQuantizeWorkloadValidate,
reasonIfUnsupported,
input,
output);
}
bool ClLayerSupport::IsReduceSupported(const TensorInfo& input,
const TensorInfo& output,
const ReduceDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClReduceWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsReshapeSupported(const TensorInfo& input,
const TensorInfo& output,
const ReshapeDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
IgnoreUnused(descriptor);
FORWARD_WORKLOAD_VALIDATE_FUNC(ClReshapeWorkloadValidate, reasonIfUnsupported, input, output);
}
bool ClLayerSupport::IsResizeSupported(const TensorInfo& input,
const TensorInfo& output,
const ResizeDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClResizeWorkloadValidate, reasonIfUnsupported, input, output, descriptor);
}
bool ClLayerSupport::IsReverseV2Supported(const TensorInfo& input,
const TensorInfo& axis,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClReverseV2WorkloadValidate,
reasonIfUnsupported,
input,
axis,
output);
}
bool ClLayerSupport::IsSliceSupported(const TensorInfo& input,
const TensorInfo& output,
const SliceDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClSliceWorkloadValidate, reasonIfUnsupported, input, output, descriptor);
}
bool ClLayerSupport::IsSoftmaxSupported(const TensorInfo& input,
const TensorInfo& output,
const SoftmaxDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClSoftmaxWorkloadValidate, reasonIfUnsupported, input, output, descriptor);
}
bool ClLayerSupport::IsSpaceToBatchNdSupported(const TensorInfo& input,
const TensorInfo& output,
const SpaceToBatchNdDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClSpaceToBatchNdWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsSpaceToDepthSupported(const TensorInfo& input,
const TensorInfo& output,
const SpaceToDepthDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClSpaceToDepthWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsSplitterSupported(const TensorInfo& input,
const std::vector<std::reference_wrapper<TensorInfo>>& outputs,
const ViewsDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
#if defined(ARMCOMPUTECL_ENABLED)
// Split along the last dimension, cannot use sub-tensors
// as width and height of the sub-tensors do not match
// the width and height of the parent tensor
// in case of input with more than 2D.
std::set<unsigned int> splitAxis = ComputeSplitAxis(descriptor, input.GetShape());
if (descriptor.GetNumDimensions() > 2 && splitAxis.size() == 1 &&
*splitAxis.begin() == descriptor.GetNumDimensions() - 1 )
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClSplitterWorkloadValidate,
reasonIfUnsupported,
input,
outputs,
*splitAxis.begin());
}
#endif
IgnoreUnused(descriptor);
for (auto output : outputs)
{
if (!input.IsTypeSpaceMatch(output)) // Cannot use sub-tensors if the types are not same space
{
SetValueChecked(reasonIfUnsupported, "Cl Splitter: Types and quantization parameters must match.");
return false;
}
}
return true;
}
bool ClLayerSupport::IsStackSupported(const std::vector<const TensorInfo*>& inputs,
const TensorInfo& output,
const StackDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClStackWorkloadValidate,
reasonIfUnsupported,
inputs,
output,
descriptor);
}
bool ClLayerSupport::IsStridedSliceSupported(const TensorInfo& input,
const TensorInfo& output,
const StridedSliceDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClStridedSliceWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsSubtractionSupported(const TensorInfo& input0,
const TensorInfo& input1,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClSubtractionValidate,
reasonIfUnsupported,
input0,
input1,
output,
nullptr);
}
bool ClLayerSupport::IsTileSupported(const TensorInfo& input,
const TensorInfo& output,
const TileDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClTileWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor);
}
bool ClLayerSupport::IsTransposeConvolution2dSupported(const TensorInfo& input,
const TensorInfo& output,
const TransposeConvolution2dDescriptor& descriptor,
const TensorInfo& weights,
const Optional<TensorInfo>& biases,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClTransposeConvolution2dWorkloadValidate,
reasonIfUnsupported,
input,
output,
descriptor,
weights,
biases);
}
bool ClLayerSupport::IsTransposeSupported(const TensorInfo& input,
const TensorInfo& output,
const TransposeDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClTransposeWorkloadValidate, reasonIfUnsupported, input, output, descriptor);
}
bool ClLayerSupport::IsUnidirectionalSequenceLstmSupported(const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
const TensorInfo& outputStateOut,
const TensorInfo& cellStateOut,
const TensorInfo& output,
const UnidirectionalSequenceLstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported) const
{
FORWARD_WORKLOAD_VALIDATE_FUNC(ClUnidirectionalSequenceLstmFloatWorkloadValidate,
reasonIfUnsupported,
input,
outputStateIn,
cellStateIn,
outputStateOut,
cellStateOut,
output,
descriptor,
paramsInfo);
}
} // namespace armnn