Teresa Charlin | 989e2f6 | 2022-04-27 16:26:11 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "ClGatherNdWorkload.hpp" |
| 7 | #include "ClWorkloadUtils.hpp" |
| 8 | #include "backendsCommon/WorkloadUtils.hpp" |
| 9 | #include <aclCommon/ArmComputeUtils.hpp> |
| 10 | #include <cl/ClTensorHandle.hpp> |
| 11 | |
| 12 | using namespace armnn::armcomputetensorutils; |
| 13 | |
| 14 | namespace armnn |
| 15 | { |
| 16 | arm_compute::Status ClGatherNdWorkloadValidate(const TensorInfo& paramsInfo, |
| 17 | const TensorInfo& indicesInfo, |
| 18 | const TensorInfo& outputInfo) |
| 19 | { |
| 20 | // Calculate ND, K, W, C. |
| 21 | std::map<std::string, unsigned int> keyIndices = CalculateGatherNdKeyIndices(paramsInfo, indicesInfo); |
| 22 | |
| 23 | /// Validate Mul |
| 24 | // Indices with shape { W, ND } |
| 25 | armnn::TensorInfo indices_W_ND_Info = indicesInfo; |
| 26 | indices_W_ND_Info.SetShape({ keyIndices["W"], keyIndices["ND"] }); |
| 27 | const arm_compute::TensorInfo aclIndicesInfo = BuildArmComputeTensorInfo(indices_W_ND_Info); |
| 28 | |
| 29 | // Flattened coefficients with shape { ND } |
| 30 | armnn::TensorInfo flattenedCoeff_Info = indicesInfo; |
| 31 | flattenedCoeff_Info.SetShape({ keyIndices["ND"] }); |
| 32 | const arm_compute::TensorInfo aclFlattenedCoeffInfo = BuildArmComputeTensorInfo(flattenedCoeff_Info); |
| 33 | |
| 34 | // Output of Mul with shape { W, ND } |
| 35 | const arm_compute::TensorInfo aclOutputMulInfo = BuildArmComputeTensorInfo(indices_W_ND_Info); |
| 36 | |
| 37 | auto statusMul = arm_compute::CLPixelWiseMultiplication::validate(&aclIndicesInfo, |
| 38 | &aclFlattenedCoeffInfo, |
| 39 | &aclOutputMulInfo, |
| 40 | 1.0f, |
| 41 | arm_compute::ConvertPolicy::WRAP, |
| 42 | arm_compute::RoundingPolicy::TO_ZERO, |
| 43 | arm_compute::ActivationLayerInfo()); |
| 44 | |
| 45 | /// Validate ReduceSum |
| 46 | // Flattened indices with shape { W } |
| 47 | armnn::TensorInfo flattenedIndices_Info = indicesInfo; |
| 48 | flattenedIndices_Info.SetShape({ keyIndices["W"] }); |
| 49 | const arm_compute::TensorInfo aclFlattenedIndicesInfo = BuildArmComputeTensorInfo(flattenedIndices_Info); |
| 50 | |
| 51 | const std::vector<unsigned int> armnnReduceAxes(1, 1); |
| 52 | arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(aclOutputMulInfo.num_dimensions(), |
| 53 | indices_W_ND_Info.GetNumDimensions(), |
| 54 | armnnReduceAxes); |
| 55 | |
| 56 | auto statusReduceSum = arm_compute::CLReductionOperation::validate(&aclOutputMulInfo, |
| 57 | &aclFlattenedIndicesInfo, |
| 58 | static_cast<unsigned int>(coords[0]), |
| 59 | arm_compute::ReductionOperation::SUM, |
| 60 | false); |
| 61 | |
| 62 | /// Validate Gather |
| 63 | // Params with shape { K, C } |
| 64 | armnn::TensorInfo params_K_C_Info = paramsInfo; |
| 65 | params_K_C_Info.SetShape({ keyIndices["K"], keyIndices["C"] }); |
| 66 | const arm_compute::TensorInfo aclParamsInfo = BuildArmComputeTensorInfo(params_K_C_Info); |
| 67 | |
| 68 | // Output of gather with shape { W, C } |
| 69 | armnn::TensorInfo outputGather_Info = outputInfo; |
| 70 | outputGather_Info.SetShape({ keyIndices["W"], keyIndices["C"] }); |
| 71 | const arm_compute::TensorInfo aclOutputGatherInfo = BuildArmComputeTensorInfo(outputGather_Info); |
| 72 | |
| 73 | auto aclAxis = ComputeAclAxis(0, params_K_C_Info); |
| 74 | auto statusGather = |
| 75 | arm_compute::CLGather::validate(&aclParamsInfo, &aclFlattenedIndicesInfo, &aclOutputGatherInfo, aclAxis); |
| 76 | |
| 77 | /// Validate Reshape |
| 78 | const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(outputInfo); |
| 79 | |
| 80 | auto statusReshape = arm_compute::CLReshapeLayer::validate(&aclOutputGatherInfo, &aclOutputInfo); |
| 81 | |
| 82 | /// Return OK if all the layers are valid |
| 83 | auto okCode = arm_compute::ErrorCode::OK; |
| 84 | if (statusMul.error_code() == okCode && |
| 85 | statusReduceSum.error_code() == okCode && |
| 86 | statusGather.error_code() == okCode && |
| 87 | statusReshape.error_code() == okCode) |
| 88 | { |
| 89 | return arm_compute::Status(arm_compute::ErrorCode::OK, |
| 90 | "All GatherND layers validate status OK."); |
| 91 | } |
| 92 | else |
| 93 | { |
| 94 | return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR, |
| 95 | "GatherND layer validate status failed."); |
| 96 | } |
| 97 | } |
| 98 | |
| 99 | ClGatherNdWorkload::ClGatherNdWorkload(const GatherNdQueueDescriptor& descriptor, |
| 100 | const WorkloadInfo& info, |
| 101 | const arm_compute::CLCompileContext& clCompileContext) |
| 102 | : ClBaseWorkload<GatherNdQueueDescriptor>(descriptor, info) |
| 103 | { |
| 104 | m_Data.ValidateInputsOutputs("ClGatherNdWorkload", 2, 1); |
| 105 | |
| 106 | TensorInfo paramsInfo = info.m_InputTensorInfos[0]; |
| 107 | TensorInfo indicesInfo = info.m_InputTensorInfos[1]; |
| 108 | TensorInfo outputInfo = info.m_OutputTensorInfos[0]; |
| 109 | |
| 110 | arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| 111 | arm_compute::ICLTensor& indices = static_cast<IClTensorHandle*>(m_Data.m_Inputs[1])->GetTensor(); |
| 112 | arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| 113 | |
| 114 | // Calculate ND, K, W, C. |
| 115 | std::map<std::string, unsigned int> keyIndices = CalculateGatherNdKeyIndices(paramsInfo, indicesInfo); |
| 116 | |
| 117 | /// Calculate flattened indices: m_FlattenedIndices = indices * m_FlattenedCoeff. |
| 118 | /// This could be done using MatMul instead of multiplication followed by reduce sum operation, |
| 119 | /// but GeMM does not support s32 at the moment. |
| 120 | |
| 121 | // Prepare the tensor to store the output of the reduce_sum operation |
| 122 | armnn::TensorInfo flattenedIndices_Info = indicesInfo; |
| 123 | flattenedIndices_Info.SetShape({ keyIndices["W"] }); |
| 124 | BuildArmComputeTensor(m_FlattenedIndices, flattenedIndices_Info); |
| 125 | armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_FlattenedIndices); |
| 126 | |
| 127 | // Reshape indices into { W, ND } |
| 128 | indices.info()->set_tensor_shape(BuildArmComputeTensorShape({ keyIndices["W"], keyIndices["ND"] })); |
| 129 | |
| 130 | // Calculate the m_FlattenedCoeff |
| 131 | TensorShape paramsShape = paramsInfo.GetShape(); |
| 132 | std::vector<int32_t> flattenedCoeff(keyIndices["ND"], 1); |
| 133 | for (unsigned int i = 1; i < keyIndices["ND"]; ++i) |
| 134 | { |
| 135 | flattenedCoeff[i - 1] = static_cast<int32_t>(paramsShape[i]); |
| 136 | } |
| 137 | for (unsigned int i = keyIndices["ND"] - 1; i > 0; --i) |
| 138 | { |
| 139 | flattenedCoeff[i - 1] *= flattenedCoeff[i]; |
| 140 | } |
| 141 | armnn::TensorInfo flattenedCoeff_Info = indicesInfo; |
| 142 | flattenedCoeff_Info.SetShape({ keyIndices["ND"] }); |
| 143 | BuildArmComputeTensor(m_FlattenedCoeff, flattenedCoeff_Info); |
| 144 | armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_FlattenedCoeff); |
| 145 | ARMNN_ASSERT_MSG(indicesInfo.GetDataType() == DataType::Signed32, |
| 146 | "flattenedCoeff must be same data type as m_FlattenedCoeff"); |
| 147 | CopyArmComputeClTensorData<int32_t>(m_FlattenedCoeff, flattenedCoeff.data()); |
| 148 | |
| 149 | // Prepare the tensor to store the output of the multiplication |
| 150 | armnn::TensorInfo outputMul_Info = indicesInfo; |
| 151 | outputMul_Info.SetShape({ keyIndices["W"], keyIndices["ND"] }); |
| 152 | BuildArmComputeTensor(m_OutputMul, outputMul_Info); |
| 153 | armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_OutputMul); |
| 154 | |
| 155 | // Multiply |
| 156 | m_MulLayer.configure(clCompileContext, |
| 157 | &indices, |
| 158 | &m_FlattenedCoeff, |
| 159 | &m_OutputMul, |
| 160 | 1.0f, |
| 161 | arm_compute::ConvertPolicy::WRAP, |
| 162 | arm_compute::RoundingPolicy::TO_ZERO, |
| 163 | arm_compute::ActivationLayerInfo()); |
| 164 | |
| 165 | // Reduce Sum |
| 166 | const std::vector<unsigned int> armnnReduceAxes(1, 1); |
| 167 | arm_compute::Coordinates coords = BuildArmComputeReductionCoordinates(m_OutputMul.info()->num_dimensions(), |
| 168 | outputMul_Info.GetNumDimensions(), |
| 169 | armnnReduceAxes); |
| 170 | m_ReduceSumLayer.configure(clCompileContext, |
| 171 | &m_OutputMul, |
| 172 | &m_FlattenedIndices, |
| 173 | static_cast<unsigned int>(coords[0]), |
| 174 | arm_compute::ReductionOperation::SUM, |
| 175 | false); |
| 176 | |
| 177 | /// Call Gather with adequate shapes |
| 178 | // Reshape params into { K, C } |
| 179 | paramsInfo.SetShape({ keyIndices["K"], keyIndices["C"] }); |
| 180 | input.info()->set_tensor_shape(BuildArmComputeTensorShape(paramsInfo.GetShape())); |
| 181 | |
| 182 | // Reshape output to have the shape given by gather { W, C } |
| 183 | // (the original outputInfo has the shape given by gatherNd) |
| 184 | armnn::TensorInfo outputGather_Info = outputInfo; |
| 185 | outputGather_Info.SetShape({ keyIndices["W"], keyIndices["C"] }); |
| 186 | BuildArmComputeTensor(m_OutputGather, outputGather_Info); |
| 187 | armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_OutputGather); |
| 188 | { |
| 189 | ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClGatherNdWorkload_configure"); |
| 190 | auto aclAxis = ComputeAclAxis(0, paramsInfo); |
| 191 | m_GatherLayer.configure(clCompileContext, &input, &m_FlattenedIndices, &m_OutputGather, aclAxis); |
| 192 | } |
| 193 | |
| 194 | // Reshape output to the original output shape |
| 195 | m_ReshapeLayer.configure(clCompileContext, &m_OutputGather, &output); |
| 196 | }; |
| 197 | |
| 198 | void ClGatherNdWorkload::Execute() const |
| 199 | { |
| 200 | ARMNN_SCOPED_PROFILING_EVENT_CL_GUID("ClGatherNdWorkload_Execute", this->GetGuid()); |
| 201 | RunClFunction(m_MulLayer, CHECK_LOCATION()); |
| 202 | RunClFunction(m_ReduceSumLayer, CHECK_LOCATION()); |
| 203 | RunClFunction(m_GatherLayer, CHECK_LOCATION()); |
| 204 | RunClFunction(m_ReshapeLayer, CHECK_LOCATION()); |
| 205 | } |
| 206 | } // namespace armnn |