Kevin May | 1bea6be | 2023-12-12 11:18:46 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | // Copyright © 2020 The TensorFlow Authors. All Rights Reserved. |
| 6 | // SPDX-License-Identifier: Apache-2.0 |
| 7 | // |
| 8 | |
| 9 | #include "SplitOperator.hpp" |
| 10 | |
| 11 | // This function is paraphrased from: |
| 12 | // tensorflow/compiler/mlir/tosa/transforms/legalize_common.cc from function convertSplitOp |
| 13 | TosaSerializationBasicBlock* ConvertSplitToTosaOperator(const Layer* layer, |
| 14 | const std::vector<const TensorInfo*>& inputs, |
| 15 | const std::vector<const TensorInfo*>& outputs, |
| 16 | const SplitterDescriptor* splitDescriptor) |
| 17 | { |
| 18 | ARMNN_THROW_INVALIDARG_MSG_IF_FALSE( inputs.size() == 1, |
| 19 | "ConvertSplitToTosaOperator: Split must have only one input" ); |
| 20 | |
Kevin May | f0d8ec1 | 2023-12-14 14:57:59 +0000 | [diff] [blame] | 21 | ARMNN_THROW_INVALIDARG_MSG_IF_FALSE( outputs.size() >= 1, |
| 22 | "ConvertSplitToTosaOperator: Split must have at least one output" ); |
Kevin May | 1bea6be | 2023-12-12 11:18:46 +0000 | [diff] [blame] | 23 | |
| 24 | if (!inputs[0]->GetShape().AreAllDimensionsSpecified()) |
| 25 | { |
| 26 | throw armnn::Exception("ConvertSplitToTosaOperator: Dynamic input dimensions are unsupported."); |
| 27 | } |
| 28 | |
| 29 | std::string inputName = std::string("input0_"); |
| 30 | std::vector<std::string> outputNames; |
| 31 | std::string blockName = std::string("Op_SPLIT_block_") + GetUniqueTosaMappingID(); |
| 32 | |
| 33 | unsigned int numSplit = splitDescriptor->GetNumViews(); |
| 34 | // If a layer is present then the block will be used for execution, so input and output names need to be determined |
| 35 | // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter. |
| 36 | if(layer != nullptr) |
| 37 | { |
| 38 | // Get the layers connected to the input slots and determine unique tensor names. |
| 39 | Layer& connectedLayer = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); |
| 40 | inputName = GenerateUniqueName(connectedLayer, 0); |
| 41 | |
| 42 | for (unsigned int i=0; i < numSplit; ++i) |
| 43 | { |
| 44 | // Determine unique output(s) tensor name. |
| 45 | std::string outputName = GenerateUniqueOutputName(*layer, i); |
| 46 | outputNames.push_back(outputName); |
| 47 | } |
| 48 | } |
| 49 | else |
| 50 | { |
| 51 | for (unsigned int i=0; i < numSplit; ++i) |
| 52 | { |
| 53 | // Determine unique output(s) tensor name. |
| 54 | std::string outputName = "output" + std::to_string(i) + "_"; |
| 55 | outputNames.push_back(outputName); |
| 56 | } |
| 57 | } |
| 58 | |
| 59 | // Each slice op has a different beginning point. |
| 60 | // The size is the same for each slice op. |
| 61 | std::vector<int32_t> beginVals; |
| 62 | beginVals.reserve(inputs[0]->GetNumDimensions()); |
| 63 | std::vector<int32_t> sizeVals; |
| 64 | sizeVals.reserve(inputs[0]->GetNumDimensions()); |
| 65 | for (unsigned int j = 0; j < inputs[0]->GetNumDimensions(); ++j) |
| 66 | { |
| 67 | beginVals.emplace_back(0); |
| 68 | uint32_t dim = inputs[0]->GetShape()[j]; |
| 69 | sizeVals.emplace_back(dim); |
| 70 | } |
| 71 | |
| 72 | uint32_t axis = static_cast<uint32_t>(splitDescriptor->GetAxis()); |
| 73 | sizeVals[axis] = sizeVals[axis] / static_cast<int32_t>(numSplit); |
| 74 | |
| 75 | std::vector<TosaSerializationOperator*> ops; |
| 76 | for (unsigned int i=0; i < numSplit; ++i) |
| 77 | { |
| 78 | beginVals[axis] = static_cast<int>(i) * sizeVals[axis]; |
| 79 | TosaSliceAttribute attribute(beginVals, sizeVals); |
| 80 | auto* op = new TosaSerializationOperator(Op_SLICE, |
| 81 | Attribute_SliceAttribute, |
| 82 | &attribute, |
| 83 | {inputName}, |
| 84 | {outputNames[i]}); |
| 85 | |
| 86 | ops.push_back(op); |
| 87 | } |
| 88 | |
| 89 | std::vector<TosaSerializationTensor*> tensors; |
| 90 | // Only add input tensors if connected layer is an input layer. |
| 91 | // As intermediate or constant tensors will be created separately. |
| 92 | // There also can't be duplicate tensor. |
| 93 | if(inputName.find("input0_") != std::string::npos) |
| 94 | { |
| 95 | std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[0]->GetShape()); |
| 96 | DType inputDType = ArmNNToDType(inputs[0]->GetDataType()); |
| 97 | |
| 98 | tensors.push_back(new TosaSerializationTensor(inputName, inputShape, inputDType, {})); |
| 99 | } |
| 100 | |
| 101 | std::vector<int32_t> outputShape = GetTosaTensorShape(outputs[0]->GetShape()); |
| 102 | DType outputDType = ArmNNToDType(outputs[0]->GetDataType()); |
| 103 | |
| 104 | for (unsigned int i=0; i < numSplit; ++i) |
| 105 | { |
| 106 | tensors.push_back(new TosaSerializationTensor(outputNames[i], outputShape, outputDType, {})); |
| 107 | } |
| 108 | // operatorInputNames/operatorOutputNames ends up being the same as |
| 109 | // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings |
| 110 | return new TosaSerializationBasicBlock(blockName, // name |
| 111 | mainName, // region name |
| 112 | ops, // operators |
| 113 | tensors, // tensors |
| 114 | {inputName}, // inputs |
| 115 | outputNames); // outputs |
| 116 | } |