mirror of https://github.com/alibaba/MNN.git
90 lines
3.8 KiB
C++
90 lines
3.8 KiB
C++
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// CPUBatchMatMul.cpp
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// MNN
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//
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// Created by MNN on 2019/03/25.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/cpu/CPUBatchMatMul.hpp"
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#include "backend/cpu/CPUBackend.hpp"
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#include "math/Matrix.hpp"
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namespace MNN {
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CPUBatchMatMul::CPUBatchMatMul(Backend* backend, bool adjX, bool adjY) : Execution(backend) {
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mMatMul.reset(new CPUMatMul(backend, adjX, adjY, true));
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}
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ErrorCode CPUBatchMatMul::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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auto input0 = inputs[0];
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auto input1 = inputs[1];
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auto output = outputs[0];
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// Fill output by zero if one of inputs is empty.
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if (input0->elementSize() == 0 || input1->elementSize() == 0) {
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return NO_ERROR;
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}
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auto dimensions = input0->dimensions();
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mMatrixA.reset(Tensor::createDevice<float>({input0->length(input0->dimensions()-2), input0->length(input0->dimensions()-1)}));
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mMatrixB.reset(Tensor::createDevice<float>({input1->length(input1->dimensions()-2), input1->length(input0->dimensions()-1)}));
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mMatrixC.reset(Tensor::createDevice<float>({output->length(output->dimensions()-2), output->length(output->dimensions()-1)}));
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mTempInputs = {mMatrixA.get(), mMatrixB.get()};
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mTempOutputs = {mMatrixC.get()};
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auto res = backend()->onAcquireBuffer(mMatrixA.get(), Backend::DYNAMIC);
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res = res && backend()->onAcquireBuffer(mMatrixB.get(), Backend::DYNAMIC);
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res = res && backend()->onAcquireBuffer(mMatrixC.get(), Backend::DYNAMIC);
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if (!res) {
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return OUT_OF_MEMORY;
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}
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int batch = 1;
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for (int i = 0; i < dimensions - 2; ++i) {
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batch *= input0->length(i);
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}
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mBatch = batch;
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auto code = mMatMul->onResize(mTempInputs, mTempOutputs);
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backend()->onReleaseBuffer(mMatrixA.get(), Backend::DYNAMIC);
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backend()->onReleaseBuffer(mMatrixB.get(), Backend::DYNAMIC);
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backend()->onReleaseBuffer(mMatrixC.get(), Backend::DYNAMIC);
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return code;
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}
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ErrorCode CPUBatchMatMul::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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auto input0 = inputs[0];
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auto input1 = inputs[1];
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auto output = outputs[0];
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// Fill output by zero if one of inputs is empty.
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if (input0->elementSize() == 0 || input1->elementSize() == 0) {
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::memset(output->host<float>(), 0, output->size());
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return NO_ERROR;
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}
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const int dimensions = input0->dimensions();
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MNN_ASSERT(dimensions >= 3);
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const int input0Stride = input0->length(dimensions - 1) * input0->length(dimensions - 2);
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const int input1Stride = input1->length(dimensions - 1) * input1->length(dimensions - 2);
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const int outputStride = output->length(dimensions - 1) * output->length(dimensions - 2);
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const auto input0Ptr = input0->host<float>();
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const auto input1Ptr = input1->host<float>();
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float* const outputPtr = output->host<float>();
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for (int i = 0; i < mBatch; ++i) {
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::memcpy(mMatrixA->host<float>(), input0Ptr + i * input0Stride, input0Stride * sizeof(float));
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::memcpy(mMatrixB->host<float>(), input1Ptr + i * input1Stride, input1Stride * sizeof(float));
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mMatMul->onExecute(mTempInputs, mTempOutputs);
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::memcpy(outputPtr + i * outputStride, mMatrixC->host<float>(), outputStride * sizeof(float));
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}
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return NO_ERROR;
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}
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class CPUBatchMatMulCreator : public CPUBackend::Creator {
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public:
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virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
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const MNN::Op* op, Backend* backend) const override {
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return new CPUBatchMatMul(backend, op->main_as_BatchMatMulParam()->adjX(), op->main_as_BatchMatMulParam()->adjY());
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUBatchMatMulCreator, OpType_BatchMatMul);
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} // namespace MNN
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