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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "MatMul.h"
#include "ONNXHelpers.h"
#include "mir/ops/FullyConnectedOp.h"
namespace mir_onnx
{
void convertMatMulV1(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
mir::Graph *graph = context->getGraph();
assert(inputs.size() == 2);
auto A = inputs[0];
auto B = inputs[1];
// MatMul multiply N-dimentional matrix
// FullyConnected layer multiply only 2-dimentional matrix
if (A->getShape().rank() != 2 || B->getShape().rank() != 2)
throw std::runtime_error("Supported only 2D matrix multiplying!");
// Calculate A * B.
auto result = createOp<mir::ops::FullyConnectedOp>(graph, A, B)->getOutput(0);
context->setNodeOutputs(onnx_node, {result});
}
void convertMatMulV9(const onnx::NodeProto &onnx_node, ConverterContext *context)
{
// Other type constraints
convertMatMulV1(onnx_node, context);
}
} // namespace mir_onnx
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