1 files changed, 126 insertions, 0 deletions
diff --git a/delegate/opaque/src/BatchMatMul.hpp b/delegate/opaque/src/BatchMatMul.hpp
index e16969768e..5da6e5ac6a 100644
--- a/delegate/opaque/src/BatchMatMul.hpp
+++ b/delegate/opaque/src/BatchMatMul.hpp
@@ -2,3 +2,129 @@
 // Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
+
+#pragma once
+
+#include <OpaqueDelegateUtils.hpp>
+
+namespace armnnOpaqueDelegate
+{
+TfLiteStatus VisitBatchMatMulOperator(DelegateData& delegateData,
+                                      TfLiteOpaqueContext* tfLiteContext,
+                                      TfLiteOpaqueNode* tfLiteNode,
+                                      int nodeIndex,
+                                      int32_t operatorCode)
+{
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    // Gather input indices and use to get input tensor.
+    auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode);
+    const int* inputTensors;
+    if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
+                nodeIndex);
+        return kTfLiteError;
+    }
+
+    const TfLiteOpaqueTensor* kTfLiteLHSInputTensor =
+            TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
+    const TfLiteOpaqueTensor* kTfLiteRHSInputTensor =
+            TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
+
+    if (!IsValid(tfLiteContext, kTfLiteLHSInputTensor, operatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+    if (!IsValid(tfLiteContext, kTfLiteRHSInputTensor, operatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    if (IsDynamicTensor(kTfLiteLHSInputTensor) || IsDynamicTensor(kTfLiteRHSInputTensor))
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+                operatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // Gather output indices and use to get output tensors.
+    int numOutputs = 0;
+    const int* outputTensors;
+    if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
+                nodeIndex);
+        return kTfLiteError;
+    }
+
+    const TfLiteOpaqueTensor* kTfLiteOutputTensor =
+            TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
+    if (IsDynamicTensor(kTfLiteOutputTensor))
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
+                operatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& armnnLHSInputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(kTfLiteLHSInputTensor);
+    const armnn::TensorInfo& armnnRHSInputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(kTfLiteRHSInputTensor);
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(kTfLiteOutputTensor, true);
+
+    armnn::BatchMatMulDescriptor descriptor;
+    auto* params = reinterpret_cast<TfLiteBatchMatMulParams *>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
+
+    // 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_OPAQUE_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);
+
+    // try to connect the Constant Inputs if there are any
+    if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
+    {
+        return kTfLiteError;
+    }
+
+    return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
+}
+
+} // namespace armnnOpaqueDelegate