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Diffstat (limited to 'delegate/src/BatchMatMul.hpp')
| -rw-r--r-- | delegate/src/BatchMatMul.hpp | 99 |
1 files changed, 99 insertions, 0 deletions
diff --git a/delegate/src/BatchMatMul.hpp b/delegate/src/BatchMatMul.hpp new file mode 100644 index 0000000000..391301e4d7 --- /dev/null +++ b/delegate/src/BatchMatMul.hpp @@ -0,0 +1,99 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "DelegateUtils.hpp" +#include <algorithm> +#include <iterator> +#include <string> +#include <vector> + +namespace armnnDelegate +{ + TfLiteStatus VisitBatchMatMulOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) + { + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& kTfLiteLHSInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + const TfLiteTensor& kTfLiteRHSInputTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + + if (!IsValid(tfLiteContext, kTfLiteLHSInputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + if (!IsValid(tfLiteContext, kTfLiteRHSInputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + if (IsDynamicTensor(kTfLiteLHSInputTensor) || IsDynamicTensor(kTfLiteRHSInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& kTfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if (IsDynamicTensor(kTfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& armnnLHSInputTensorInfo = GetTensorInfoForTfLiteTensor(kTfLiteLHSInputTensor); + const armnn::TensorInfo& armnnRHSInputTensorInfo = GetTensorInfoForTfLiteTensor(kTfLiteRHSInputTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(kTfLiteOutputTensor, true); + + armnn::BatchMatMulDescriptor descriptor; + auto* params = reinterpret_cast<TfLiteBatchMatMulParams *>(tfLiteNode->builtin_data); + + // Tensorflow params are called adjoint, however they are actually just transposes behind the scene. They do + // not perform ajoint. + descriptor.m_TransposeX = params->adj_x; + descriptor.m_TransposeY = params->adj_y; + + // Check if supported + bool isSupported = false; + auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) + { + FORWARD_LAYER_SUPPORT_FUNC("BATCH_MATMUL", + tfLiteContext, + IsBatchMatMulSupported, + delegateData.m_Backends, + isSupported, + armnnLHSInputTensorInfo, + armnnRHSInputTensorInfo, + outputTensorInfo, + descriptor); + }; + + if (!delegateData.m_Network) + { + validateFunc(outputTensorInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* layer = delegateData.m_Network->AddBatchMatMulLayer(descriptor); + ARMNN_ASSERT(layer != nullptr); + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + Connect(layer, tfLiteNode, delegateData); + + return kTfLiteOk; + } +} // namespace armnnDelegate
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