| // |
| // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "RefGatherNdWorkload.hpp" |
| |
| #include "Gather.hpp" |
| #include "Profiling.hpp" |
| #include "RefWorkloadUtils.hpp" |
| #include "backendsCommon/WorkloadUtils.hpp" |
| |
| namespace armnn |
| { |
| |
| void RefGatherNdWorkload::Execute() const |
| { |
| Execute(m_Data.m_Inputs, m_Data.m_Outputs); |
| } |
| |
| void RefGatherNdWorkload::ExecuteAsync(ExecutionData& executionData) |
| { |
| WorkingMemDescriptor* workingMemDescriptor = static_cast<WorkingMemDescriptor*>(executionData.m_Data); |
| Execute(workingMemDescriptor->m_Inputs, workingMemDescriptor->m_Outputs); |
| } |
| |
| void RefGatherNdWorkload::Execute(std::vector<ITensorHandle*> inputs, std::vector<ITensorHandle*> outputs) const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefGatherNdWorkload_Execute"); |
| |
| const TensorInfo& inputInfo0 = GetTensorInfo(inputs[0]); |
| const TensorInfo& inputInfo1 = GetTensorInfo(inputs[1]); |
| const TensorInfo& outputInfo = GetTensorInfo(outputs[0]); |
| |
| std::unique_ptr<Decoder<float>> params_decoderPtr = MakeDecoder<float>(inputInfo0, inputs[0]->Map()); |
| |
| const int32_t* indicesDataPtr = reinterpret_cast<int32_t*>(inputs[1]->Map()); |
| std::vector<int32_t> indices(indicesDataPtr, indicesDataPtr + inputInfo1.GetNumElements()); |
| |
| std::unique_ptr<Encoder<float>> output_encoderPtr = MakeEncoder<float>(outputInfo, outputs[0]->Map()); |
| |
| std::map<std::string, unsigned int> keyIndices = CalculateGatherNdKeyIndices(inputInfo0, inputInfo1); |
| |
| /// Calculate flattened indices: flattenedIndices = indices * flattenedCoefficients |
| // Calculate the flattened coefficients to use in the multiplication |
| // to calculate the flattened indices needed by gather |
| TensorShape paramsShape = inputInfo0.GetShape(); |
| std::vector<unsigned int> flattenedCoeff(keyIndices["ND"], 1); |
| for (unsigned int i = 1; i < keyIndices["ND"]; ++i) |
| { |
| flattenedCoeff[i-1] = paramsShape[i]; |
| } |
| for (unsigned int i = keyIndices["ND"]-1; i > 0; --i) |
| { |
| flattenedCoeff[i-1] *= flattenedCoeff[i]; |
| } |
| |
| // Prepare the vector to store the output of the matrix multiplication, |
| // which will represent the flattened indices needed by gather |
| armnn::TensorInfo flattenedIndices_Info = inputInfo1; |
| flattenedIndices_Info.SetShape({ keyIndices["W"] }); |
| std::vector<int32_t> flattenedIndices(flattenedIndices_Info.GetNumElements(), 0); |
| |
| // Multiplication to calculate the flattened indices, which are the indices needed by gather. |
| for (unsigned int i = 0; i < keyIndices["W"]; ++i) |
| { |
| for (unsigned int j = 0; j < keyIndices["ND"]; ++j) |
| { |
| flattenedIndices[i] += indices[i * keyIndices["ND"] + j] * static_cast<int32_t>(flattenedCoeff[j]); |
| } |
| } |
| |
| /// Call Gather with adequate shapes |
| // Reshape params into {K, C} |
| armnn::TensorInfo params_K_C_Info = inputInfo0; |
| params_K_C_Info.SetShape({ keyIndices["K"], keyIndices["C"] }); |
| |
| // Reshape indices into {N, W} |
| armnn::TensorInfo indices_N_W_Info = inputInfo1; |
| indices_N_W_Info.SetShape({ keyIndices["N"], keyIndices["W"] }); |
| |
| // Reshape output to have the shape given by gather {N, W, C} |
| // (the original outputInfo has the shape given by gatherNd) |
| armnn::TensorInfo outputGather_Info = outputInfo; |
| outputGather_Info.SetShape({ keyIndices["N"], keyIndices["W"], keyIndices["C"] }); |
| |
| // output_gather = gather(params_K_C, indices_N_W) |
| Gather(params_K_C_Info, indices_N_W_Info, outputGather_Info, |
| *params_decoderPtr, flattenedIndices.data(), *output_encoderPtr, 0); |
| } |
| |
| } //namespace armnn |