mirror of https://github.com/alibaba/MNN.git
52 lines
1.6 KiB
C++
Executable File
52 lines
1.6 KiB
C++
Executable File
//
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// TRTBatchMatMul.cpp
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// MNN
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//
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// Created by MNN on 2021/02/28.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "TRTBatchMatMul.hpp"
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#include <core/TensorUtils.hpp>
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#include "TRTBackend.hpp"
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using namespace std;
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namespace MNN {
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nvinfer1::MatrixOperation transposeFormat(nvinfer1::ITensor *x, bool transpose) {
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return transpose ? nvinfer1::MatrixOperation::kTRANSPOSE : nvinfer1::MatrixOperation::kNONE;
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}
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TRTBatchMatMul::TRTBatchMatMul(Backend *b, const Op *op, const std::vector<Tensor *> &inputs,
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const std::vector<Tensor *> &outputs)
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: MNN::TRTCommonExecution(b, op) {
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#ifdef TRT_LOG
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printf("TRTBatchMatMul in\n");
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#endif
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}
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std::vector<ITensor *> TRTBatchMatMul::onEncode(const std::vector<ITensor *> &xOp) {
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#ifdef TRT_LOG
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printf("TRTBatchMatMul in\n");
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#endif
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auto param = mOp->main_as_BatchMatMulParam();
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MNN_ASSERT(mInputs.size() == 2);
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bool isConst0 = TensorUtils::getDescribe(mInputs[0])->usage == Tensor::InsideDescribe::Usage::CONSTANT;
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bool isConst1 = TensorUtils::getDescribe(mInputs[1])->usage == Tensor::InsideDescribe::Usage::CONSTANT;
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auto dimSize0 = mInputs[0]->dimensions();
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auto dimSize1 = mInputs[1]->dimensions();
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auto transpose_a = transposeFormat(xOp[0], param->adjX());
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auto transpose_b = transposeFormat(xOp[1], param->adjY());
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auto matmul_layer = mTrtBackend->getNetwork()->addMatrixMultiply(*xOp[0], transpose_a, *xOp[1], transpose_b);
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return {matmul_layer->getOutput(0)};
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}
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TRTCreatorRegister<TypedCreator<TRTBatchMatMul>> __batch_matmul_op(OpType_BatchMatMul);
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} // namespace MNN
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