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//
// 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;
armnn::BackendId setBackend;
auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC("BATCH_MATMUL",
tfLiteContext,
IsBatchMatMulSupported,
delegateData.m_Backends,
isSupported,
setBackend,
armnnLHSInputTensorInfo,
armnnRHSInputTensorInfo,
outputTensorInfo,
descriptor);
};
if (!delegateData.m_Network)
{
validateFunc(outputTensorInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
armnn::IConnectableLayer* layer = delegateData.m_Network->AddBatchMatMulLayer(descriptor);
layer->SetBackendId(setBackend);
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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