| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include <backendsCommon/WorkloadUtils.hpp> |
| |
| #include <armnn/Utils.hpp> |
| #include <armnn/utility/NumericCast.hpp> |
| #include <armnnUtils/DataLayoutIndexed.hpp> |
| |
| #include <fmt/format.h> |
| #include <numeric> |
| |
| namespace armnn |
| { |
| |
| armnn::ConstTensor PermuteTensor(const ConstTensorHandle* tensor, |
| const PermutationVector& permutationVector, void* permuteBuffer) |
| { |
| ARMNN_ASSERT_MSG(tensor, "Invalid input tensor"); |
| ARMNN_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()); |
| } |
| tensorInfo.SetConstant(true); |
| 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; |
| unsigned int inputChannel = 0; |
| |
| for (unsigned int originWeightsChannel = 0; originWeightsChannel < totalChannels; originWeightsChannel++) |
| { |
| inputChannel = originWeightsChannel % inputChannels; |
| destinationWeightsChannel = (originWeightsChannel - inputChannel) / inputChannels + multiplier * inputChannel; |
| |
| 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; |
| } |
| |
| |
| std::tuple<ConstTensor, unsigned int> Convert1HWOTensorToAcl(const ConstTensorHandle* weightTensor, |
| const TensorInfo& inputInfo, |
| const DataLayout dataLayout, |
| void* permuteBuffer) |
| { |
| TensorInfo weightsInfo = weightTensor->GetTensorInfo(); |
| unsigned int depthMultiplier = 1; |
| PermutationVector permutationVector{}; |
| if (dataLayout == armnn::DataLayout::NHWC) |
| { |
| // No permutation required. Data layouts are the same. |
| |
| depthMultiplier = weightsInfo.GetShape()[3] / inputInfo.GetShape()[3]; |
| } |
| else if (dataLayout == armnn::DataLayout::NCHW) |
| { |
| // [ 1, H, W, I*M] --> [ 1, I * M, H, W ] |
| depthMultiplier = weightsInfo.GetShape()[3] / inputInfo.GetShape()[1]; |
| permutationVector = { 0, 2, 3, 1 }; |
| } |
| else |
| { |
| throw InvalidArgumentException(fmt::format("Unknown data layout for tensor conversion: {}", |
| GetDataLayoutName(dataLayout))); |
| } |
| |
| ConstTensor weightsPermuted = PermuteTensor(weightTensor, permutationVector, permuteBuffer); |
| |
| return std::make_tuple(weightsPermuted, depthMultiplier); |
| } |
| |
| std::tuple<TensorInfo, unsigned int> Convert1HWOTensorInfoToAcl(const TensorInfo& weightInfo, |
| const TensorInfo& inputInfo, |
| const DataLayout dataLayout) |
| { |
| unsigned int aclDepthMultiplier = 1; |
| TensorInfo weightsPermuted; |
| if (dataLayout == armnn::DataLayout::NHWC) |
| { |
| // No permutation required. Input and weights data layouts are the same. |
| aclDepthMultiplier = weightInfo.GetShape()[3] / inputInfo.GetShape()[3]; |
| weightsPermuted = weightInfo; |
| } |
| |
| else if (dataLayout == armnn::DataLayout::NCHW) |
| { |
| // Weights permutation required. Weights [N,H,W,C] and input [N,C,H,W] data layouts are different. |
| // [ 1, H, W, I*M] --> [ 1, I * M, H, W ] |
| aclDepthMultiplier = weightInfo.GetShape()[3] / inputInfo.GetShape()[1]; |
| PermutationVector permutationVector{ 0, 2, 3, 1 }; |
| weightsPermuted = armnnUtils::Permuted(weightInfo, permutationVector); |
| } |
| else |
| { |
| throw InvalidArgumentException(fmt::format("Unknown data layout for tensor info conversion: {}", |
| GetDataLayoutName(dataLayout))); |
| } |
| |
| return std::make_tuple(weightsPermuted, aclDepthMultiplier); |
| } |
| |
| |
| std::tuple<ConstTensor, unsigned int> Convert1HWOtoMIHW(const ConstTensorHandle* weightTensor, |
| const TensorInfo& inputInfo, |
| const DataLayout& dataLayout, |
| void* permuteBuffer) |
| { |
| TensorInfo weightsInfo = weightTensor->GetTensorInfo(); |
| |
| if (weightsInfo.HasPerAxisQuantization()) |
| { |
| throw InvalidArgumentException("Can't convert tensor from [1,H,W,Cout] to [M,Cin,H,W] when per channel " |
| "quantization is applied."); |
| } |
| |
| // Reshape weights [ 1, H, W, I*M ] --> [ H, W, I, M ] |
| auto weightsShape = weightsInfo.GetShape(); |
| auto channelIndex = armnnUtils::DataLayoutIndexed(dataLayout).GetChannelsIndex(); |
| unsigned int depthMultiplier = weightsShape[3] / inputInfo.GetShape()[channelIndex]; |
| weightsInfo.SetShape({ weightsShape[1], |
| weightsShape[2], |
| inputInfo.GetShape()[channelIndex], |
| depthMultiplier}); |
| |
| // Permute [ H, W, I, M ] --> [ M, I, H, W ] |
| PermutationVector permutationVector = { 2, 3, 1, 0 }; |
| ConstTensor weightsPermuted = PermuteTensor(weightTensor, permutationVector, permuteBuffer); |
| |
| return std::make_tuple(weightsPermuted, depthMultiplier); |
| } |
| |
| armnn::ConstTensor ConvertWeightTensorFromArmnnToAcl(const ConstTensorHandle* weightTensor, |
| DataLayout dataLayout, |
| void* permuteBuffer) |
| { |
| ARMNN_ASSERT_MSG(weightTensor, "Invalid input tensor"); |
| ARMNN_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::QAsymmS8: |
| case DataType::QAsymmU8: |
| weightPermuted = ReorderWeightChannelsForAcl<uint8_t>(weightPermuted, dataLayout, permuteBuffer); |
| break; |
| case DataType::QSymmS8: |
| 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 < armnn::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-(armnn::numeric_cast<int>(i)+1), 0)); |
| } |
| |
| return reversedMask; |
| } |
| |
| std::map<std::string, unsigned int> CalculateGatherNdKeyIndices(TensorInfo inputInfo0, TensorInfo inputInfo1) |
| { |
| std::vector<unsigned int> paramsShape; |
| for (unsigned int i = 0; i < inputInfo0.GetNumDimensions(); ++i) |
| { |
| paramsShape.push_back(inputInfo0.GetShape()[i]); |
| } |
| |
| std::vector<unsigned int> indicesShape; |
| for (unsigned int i = 0; i < inputInfo1.GetNumDimensions(); ++i) |
| { |
| indicesShape.push_back(inputInfo1.GetShape()[i]); |
| } |
| |
| std::map<std::string, unsigned int> keyIndices; |
| |
| // N: number of batches |
| keyIndices["N"] = 1; |
| |
| // ND: number of dimensions that are sliced from params |
| keyIndices["ND"] = indicesShape.back(); |
| |
| // W: number of indices in each batch (all but the last dimension) |
| keyIndices["W"] = |
| static_cast<unsigned int>(std::accumulate(std::begin(indicesShape), |
| std::end(indicesShape) - 1, |
| 1, |
| std::multiplies<>() )); |
| // K: range of each index |
| keyIndices["K"] = |
| static_cast<unsigned int>(std::accumulate(std::begin(paramsShape), |
| std::begin(paramsShape) + static_cast<int>(keyIndices["ND"]), |
| 1, |
| std::multiplies<>() )); |
| // C: number of channels for each index |
| keyIndices["C"] = |
| static_cast<unsigned int>(std::accumulate(std::begin(paramsShape) + static_cast<int>(keyIndices["ND"]), |
| std::end(paramsShape), |
| 1, |
| std::multiplies<>() )); |
| |
| return keyIndices; |
| } |
| |
| armnn::PermutationVector GeneratePermutationVectorOnLastTwoDimensions(unsigned int rank) |
| { |
| armnn::PermutationVector permutationVector{}; |
| switch (rank) |
| { |
| case 2: |
| permutationVector = {1U, 0U}; |
| break; |
| case 3: |
| permutationVector = {0U, 2U, 1U}; |
| break; |
| case 4: |
| permutationVector = {0U, 1U, 3U, 2U}; |
| break; |
| default: |
| throw Exception("Invalid number of dimensions."); |
| } |
| return permutationVector; |
| } |
| |
| } // namespace armnn |