| // |
| // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "BatchMatMulImpl.hpp" |
| |
| #include <armnn/backends/WorkloadData.hpp> |
| #include <armnn/Logging.hpp> |
| |
| namespace armnn |
| { |
| |
| void BatchMatMul::BatchMatMulImpl() |
| { |
| inputXData = inputXDecoder.DecodeTensor(inputXInfo.GetShape()); |
| inputYData = inputYDecoder.DecodeTensor(inputYInfo.GetShape()); |
| // At this point, we don't touch the input decoders - just the resultant vectors |
| |
| // Pre-transpose and pre-adjoint if their vectors aren't empty |
| // and also DataLayouts which may change with permutations/adjoints |
| |
| // Todo: Have you updated input validation and inferred output shapes to accommodate for these pre-permutes? |
| |
| auto idx = std::vector<unsigned int>(outputInfo.GetNumDimensions(), 0); |
| RecurseBMM(idx, 0); |
| } |
| |
| void BatchMatMul::RecurseBMM(std::vector<unsigned int>& curIdx, unsigned int curDim) |
| { |
| // We're working off of the indexes of the output tensor (the max possible shape) |
| |
| if(!(curDim < outputInfo.GetNumDimensions())) |
| { |
| // We're at the leaf level of this call tree, so we operate here (each leaf is a data point) |
| |
| auto axesToMul = BatchMatMulDescriptor::GetAxesToMul(params, |
| inputXInfo.GetShape(), |
| inputYInfo.GetShape()); |
| AdjustAxesToMulForUnequalRanks(axesToMul); |
| |
| unsigned int inputXColDim = axesToMul.first.second; |
| unsigned int inputYRowDim = axesToMul.second.first; |
| |
| unsigned int inputYRowSize = inputYInfo.GetShape()[inputYRowDim]; |
| |
| float sum = 0.0f; |
| |
| // You could also use inputXColSize |
| for (unsigned int inputYRowIdx = 0; inputYRowIdx < inputYRowSize; inputYRowIdx++) { |
| auto xIdx = curIdx; |
| xIdx[inputXColDim] = inputYRowIdx; |
| |
| auto yIdx = curIdx; |
| yIdx[inputYRowDim] = inputYRowIdx; |
| |
| sum += (GetValueAt(DataSlot::InputX, xIdx) |
| * GetValueAt(DataSlot::InputY, yIdx)); |
| } |
| |
| SetValueAt(sum, DataSlot::Output, curIdx); |
| |
| return; |
| } |
| |
| for (unsigned int i = 0; i < outputInfo.GetShape()[curDim]; i++) |
| { |
| curIdx[curDim] = i; |
| RecurseBMM(curIdx, curDim+1); |
| } |
| } |
| |
| void BatchMatMul::AdjustAxesToMulForUnequalRanks( |
| std::pair<std::pair<unsigned int, unsigned int>, std::pair<unsigned int, unsigned int>>& axesToMul) |
| { |
| int rankDiff = static_cast<int>(inputXInfo.GetNumDimensions()) - |
| static_cast<int>(inputYInfo.GetNumDimensions()); |
| if(rankDiff == 0) |
| { |
| return; |
| } |
| else if(rankDiff < 0) |
| { |
| // Y is the larger one |
| axesToMul.first.first += static_cast<std::make_unsigned<unsigned int>::type>(std::abs(rankDiff)); |
| axesToMul.first.second += static_cast<std::make_unsigned<unsigned int>::type>(std::abs(rankDiff)); |
| } |
| else if(rankDiff > 0) |
| { |
| // X is the larger one |
| axesToMul.second.first += static_cast<std::make_unsigned<unsigned int>::type>(std::abs(rankDiff)); |
| axesToMul.second.second += static_cast<std::make_unsigned<unsigned int>::type>(std::abs(rankDiff)); |
| } |
| } |
| |
| float BatchMatMul::GetValueAt(DataSlot type, std::vector<unsigned int> idx) |
| { |
| // This gets the data from the input vector that we have, Not the decoder |
| // But for the output, it is operating on the encoder itself |
| |
| AdjustToSafeIdx(type, idx); |
| unsigned int flatIdx = CalcFlatIdx(type, idx); |
| float value = 0.0f; |
| |
| switch(type) |
| { |
| case DataSlot::InputX: |
| value = inputXData[flatIdx]; |
| break; |
| case DataSlot::InputY: |
| value = inputYData[flatIdx]; |
| break; |
| case DataSlot::Output: |
| outputEncoder[flatIdx]; |
| value = outputEncoder.Get(); |
| break; |
| default: |
| break; |
| } |
| |
| return value; |
| } |
| |
| void BatchMatMul::SetValueAt(float value, DataSlot type, std::vector<unsigned int> idx) |
| { |
| AdjustToSafeIdx(type, idx); |
| |
| unsigned int flatIdx = CalcFlatIdx(type, idx); |
| |
| switch(type) |
| { |
| case DataSlot::InputX: |
| inputXData[flatIdx] = value; |
| break; |
| case DataSlot::InputY: |
| inputYData[flatIdx] = value; |
| break; |
| case DataSlot::Output: |
| outputEncoder[flatIdx]; |
| outputEncoder.Set(value); |
| break; |
| default: |
| break; |
| } |
| } |
| |
| void BatchMatMul::AdjustToSafeIdx(DataSlot type, std::vector<unsigned int>& idx) |
| { |
| for(unsigned int dim = 0; dim < idx.size(); dim++) |
| { |
| switch(type) |
| { |
| case DataSlot::InputX: |
| { |
| auto xRank = inputXInfo.GetNumDimensions(); |
| auto xDiff = outputInfo.GetNumDimensions() - xRank; |
| if (dim < xDiff || |
| idx[dim] > inputXInfo.GetShape()[dim-xDiff]-1) |
| { |
| idx[dim] = 0; // Broadcasting |
| } |
| break; |
| } |
| case DataSlot::InputY: |
| { |
| auto yRank = inputYInfo.GetNumDimensions(); |
| auto yDiff = outputInfo.GetNumDimensions() - yRank; |
| if (dim < yDiff || |
| idx[dim] > inputYInfo.GetShape()[dim-yDiff]-1) |
| { |
| idx[dim] = 0; |
| } |
| break; |
| } |
| case DataSlot::Output: |
| { |
| // Our indices are based off the output |
| break; |
| } |
| default: |
| break; |
| } |
| } |
| } |
| |
| unsigned int BatchMatMul::CalcFlatIdx(DataSlot type, const std::vector<unsigned int>& idx) |
| { |
| unsigned int result = idx[idx.size()-1]; |
| |
| unsigned int dimMultiplier = 1; |
| |
| unsigned int offset; |
| |
| // -2 because final dim is already accounted for in the multiplier (last dim is just a multiplier of 1x) |
| for(unsigned int i = static_cast<unsigned int>(idx.size()-2); static_cast<int>(i) >= 0; i--) |
| { |
| switch(type) |
| { |
| case DataSlot::InputX: |
| offset = outputInfo.GetNumDimensions() - inputXInfo.GetNumDimensions(); |
| dimMultiplier *= inputXInfo.GetShape()[i + 1 - offset]; |
| break; |
| case DataSlot::InputY: |
| offset = outputInfo.GetNumDimensions() - inputYInfo.GetNumDimensions(); |
| dimMultiplier *= inputYInfo.GetShape()[i + 1 - offset]; |
| break; |
| case DataSlot::Output: |
| dimMultiplier *= outputInfo.GetShape()[i+1]; |
| break; |
| default: |
| break; |
| } |
| result += (idx[i] * dimMultiplier); |
| } |
| return result; |
| } |
| |
| template <typename T> |
| std::string BatchMatMul::StringifyVec(const std::vector<T>& vec) |
| { |
| std::string res = "{ "; |
| for(auto x : vec) |
| { |
| res += std::to_string(x); |
| res += " "; |
| } |
| res += "}"; |
| return res; |
| } |
| |
| } // namespace armnn |