| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "WorkloadUtils.hpp" |
| |
| namespace armnn |
| { |
| |
| armnn::ConstTensor PermuteTensor(const ConstCpuTensorHandle* tensor, |
| const PermutationVector& permutationVector, void* permuteBuffer) |
| { |
| BOOST_ASSERT_MSG(tensor, "Invalid input tensor"); |
| BOOST_ASSERT_MSG(permuteBuffer, "Invalid permute buffer"); |
| |
| TensorInfo tensorInfo = tensor->GetTensorInfo(); |
| |
| if (permutationVector.GetSize() > 0) |
| { |
| tensorInfo = armnnUtils::Permuted(tensorInfo, permutationVector); |
| armnnUtils::Permute(tensorInfo.GetShape(), permutationVector, |
| tensor->GetConstTensor<void>(), permuteBuffer, |
| GetDataTypeSize(tensorInfo.GetDataType())); |
| } |
| else |
| { |
| ::memcpy(permuteBuffer, tensor->GetConstTensor<void>(), tensorInfo.GetNumBytes()); |
| } |
| |
| return ConstTensor(tensorInfo, permuteBuffer); |
| } |
| |
| void ReshapeWeightsForAcl(TensorInfo& weightInfo, DataLayout dataLayout) |
| { |
| // Reshape the weights in-place |
| const TensorShape& weightShape = weightInfo.GetShape(); |
| switch (dataLayout) |
| { |
| case DataLayout::NHWC: |
| // The data layout is NHWC, reshape from [ H, W, I, M ] to [ 1, H, W, I * M ] |
| weightInfo.SetShape({ 1, |
| weightShape[0], |
| weightShape[1], |
| weightShape[2] * weightShape[3] }); |
| weightInfo.SetShape({ 1, |
| weightShape[0] * weightShape[1], |
| weightShape[2], |
| weightShape[3] }); |
| break; |
| case DataLayout::NCHW: |
| default: |
| // The data layout is NCHW, reshape from [ M, I, H, W ] to [ 1, I * M, H, W, ] |
| weightInfo.SetShape({ 1, weightShape[0] * weightShape[1], weightShape[2], weightShape[3] }); |
| break; |
| } |
| } |
| |
| template <typename DataType> |
| ConstTensor ReorderWeightChannelsForAcl(const ConstTensor& weightHandle, DataLayout dataLayout, void* permuteBuffer) |
| { |
| DataType* weight = static_cast<DataType*>(permuteBuffer); |
| const TensorShape& weightShape = weightHandle.GetShape(); |
| unsigned int multiplier; |
| unsigned int height; |
| unsigned int width; |
| unsigned int inputChannels; |
| switch (dataLayout) |
| { |
| case DataLayout::NHWC: //It actually is [ H, W, I, M ] |
| height = weightShape[0]; |
| width = weightShape[1]; |
| inputChannels = weightShape[2]; |
| multiplier = weightShape[3]; |
| break; |
| case DataLayout::NCHW: //It actually is [ M, I, H, W ] |
| default: |
| height = weightShape[2]; |
| width = weightShape[3]; |
| inputChannels = weightShape[1]; |
| multiplier = weightShape[0]; |
| break; |
| } |
| |
| std::vector<DataType> weightAclOrder(height*width*inputChannels*multiplier); |
| unsigned int destinationWeightsChannel; |
| unsigned int totalChannels = inputChannels * multiplier; |
| unsigned int channelSize = height * width; |
| |
| for (unsigned int originWeightsChannel = 0; originWeightsChannel < totalChannels; originWeightsChannel++) |
| { |
| if (originWeightsChannel % inputChannels == 0) |
| { |
| destinationWeightsChannel = originWeightsChannel / inputChannels; |
| } |
| else |
| { |
| destinationWeightsChannel = (originWeightsChannel - 1) / inputChannels + multiplier; |
| } |
| |
| for (unsigned int i = 0; i < channelSize; i++) |
| { |
| weightAclOrder[i + destinationWeightsChannel * channelSize] = |
| weight[i + originWeightsChannel * channelSize]; |
| } |
| } |
| |
| ::memcpy(permuteBuffer, weightAclOrder.data(), weightHandle.GetInfo().GetNumBytes()); |
| return ConstTensor(weightHandle.GetInfo(), permuteBuffer); |
| } |
| |
| TensorInfo ConvertWeightTensorInfoFromArmnnToAcl(const TensorInfo& weightInfo, DataLayout dataLayout) |
| { |
| // Convert the weight format from ArmNN's [ M, I, H, W ] (does NOT depend on the data layout) to either |
| // [ 1, H, W, I * M ] (if NHWC) or [ 1, I * M, H, W ] (if NCHW), as required by the compute library |
| |
| // 1. Permute the weights if necessary |
| // If the data layout is NCHW no permutation is necessary, as a reshape to [ 1, I * M, H, W ] can be better done |
| // starting from the current shape of [ M, I, H, W ] |
| TensorInfo weightPermutedInfo(weightInfo); |
| if (dataLayout == DataLayout::NHWC) |
| { |
| // The data layout is NHWC, then permute the weights from [ M, I, H, W ] to [ H, W, I, M ] |
| PermutationVector permutationVector{ 3, 2, 0, 1 }; |
| weightPermutedInfo = armnnUtils::Permuted(weightInfo, permutationVector); |
| } |
| |
| // 2. Reshape the weights |
| ReshapeWeightsForAcl(weightPermutedInfo, dataLayout); |
| |
| // 3. Return the permuted weight info |
| return weightPermutedInfo; |
| } |
| |
| armnn::ConstTensor ConvertWeightTensorFromArmnnToAcl(const ConstCpuTensorHandle* weightTensor, |
| DataLayout dataLayout, |
| void* permuteBuffer) |
| { |
| BOOST_ASSERT_MSG(weightTensor, "Invalid input tensor"); |
| BOOST_ASSERT_MSG(permuteBuffer, "Invalid permute buffer"); |
| |
| auto multiplier = weightTensor->GetTensorInfo().GetShape()[0]; |
| auto inputChannels = weightTensor->GetTensorInfo().GetShape()[1]; |
| |
| // Convert the weight format from ArmNN's [ M, I, H, W ] (does NOT depend on the data layout) to either |
| // [ 1, H, W, I * M ] (if NHWC) or [ 1, I * M, H, W ] (if NCHW), as required by the compute library |
| |
| // 1. Permute the weights if necessary |
| // If the data layout is NCHW no permutation is necessary, as a reshape to [ 1, I * M, H, W ] can be better done |
| // starting from the current shape of [ M, I, H, W ] |
| // If no permutation is necessary, leave the permutation vector empty |
| PermutationVector permutationVector{}; |
| if (dataLayout == DataLayout::NHWC) |
| { |
| // The data layout is NHWC, then permute the weights from [ M, I, H, W ] to [ H, W, I, M ] |
| permutationVector = { 3, 2, 0, 1 }; |
| } |
| ConstTensor weightPermuted = PermuteTensor(weightTensor, permutationVector, permuteBuffer); |
| |
| // Shuffle the weights data to obtain the channel order needed used by Acl |
| if (multiplier > 1 && inputChannels > 1 && dataLayout == DataLayout::NCHW) |
| { |
| switch (weightPermuted.GetDataType()) |
| { |
| case DataType::Float32: |
| weightPermuted = ReorderWeightChannelsForAcl<float>(weightPermuted, dataLayout, permuteBuffer); |
| break; |
| case DataType::Float16: |
| weightPermuted = |
| ReorderWeightChannelsForAcl<half_float::half>(weightPermuted, dataLayout, permuteBuffer); |
| break; |
| case DataType::QuantisedAsymm8: |
| weightPermuted = ReorderWeightChannelsForAcl<uint8_t>(weightPermuted, dataLayout, permuteBuffer); |
| break; |
| case DataType::QuantizedSymm8PerAxis: |
| weightPermuted = ReorderWeightChannelsForAcl<int8_t>(weightPermuted, dataLayout, permuteBuffer); |
| break; |
| default: |
| break; |
| } |
| } |
| |
| // 2. Reshape the weights |
| ReshapeWeightsForAcl(weightPermuted.GetInfo(), dataLayout); |
| |
| // 3. Return both the tensor and the allocated storage to ensure that the data stays alive |
| return weightPermuted; |
| } |
| |
| int32_t ConvertMaskToACLFormat(int32_t mask, int32_t numDim) |
| { |
| int32_t reversedMask = 0; |
| for (unsigned int i = 0; i < boost::numeric_cast<unsigned int>(numDim); ++i) |
| { |
| // Check if bit set in mask for each dimension |
| int32_t bit = (mask & 1 << i) != 0; |
| // Increment the new mask with the bits reversed |
| reversedMask += (bit << std::max(numDim-(boost::numeric_cast<int>(i)+1), 0)); |
| } |
| |
| return reversedMask; |
| } |
| |
| } // namespace armnn |