mirror of https://github.com/alibaba/MNN.git
126 lines
4.2 KiB
C++
126 lines
4.2 KiB
C++
//
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// ShapeLSTM.cpp
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// MNN
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//
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// Created by MNN on 2019/01/10.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "shape/SizeComputer.hpp"
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#include "core/Macro.h"
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#include "core/TensorUtils.hpp"
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namespace MNN {
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// Size Computer
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class LSTMComputer : public SizeComputer {
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virtual bool onComputeSize(const MNN::Op *op, const std::vector<Tensor *> &inputs,
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const std::vector<Tensor *> &outputs) const override {
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if (1 == outputs.size()) {
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// For compability for old version model
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MNN_ASSERT(1 == outputs.size());
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// copy dims
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auto &input = inputs[0]->buffer();
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auto &output = outputs[0]->buffer();
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memcpy(output.dim, input.dim, sizeof(halide_dimension_t) * input.dimensions);
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auto LSTM = op->main_as_LSTM();
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output.dimensions = 4;
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output.dim[3].extent = LSTM->outputCount();
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output.dim[2].extent = 1;
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output.type = halide_type_of<float>();
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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return true;
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}
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// Onnx's LSTM
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MNN_ASSERT(inputs.size() >= 4);
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MNN_ASSERT(outputs.size() == 3);
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auto X = inputs[0];
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auto seqLength = X->length(0);
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auto batchSize = X->length(1);
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auto hiddenSize = op->main_as_LSTM()->outputCount();
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auto Y = outputs[0];
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auto ht = outputs[1];
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auto ct = outputs[2];
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Y->buffer().dimensions = 4;
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ht->buffer().dimensions = 3;
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ct->buffer().dimensions = 3;
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Y->setLength(0, seqLength);
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int direction = inputs[1]->length(0);
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MNN_ASSERT(1 == direction || 2 == direction);
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Y->setLength(1, direction);
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Y->setLength(2, batchSize);
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Y->setLength(3, hiddenSize);
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ht->setLength(0, direction);
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ht->setLength(1, batchSize);
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ht->setLength(2, hiddenSize);
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ct->setLength(0, direction);
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ct->setLength(1, batchSize);
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ct->setLength(2, hiddenSize);
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TensorUtils::getDescribe(Y)->dimensionFormat = TensorUtils::getDescribe(X)->dimensionFormat;
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TensorUtils::getDescribe(ht)->dimensionFormat = TensorUtils::getDescribe(X)->dimensionFormat;
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TensorUtils::getDescribe(ct)->dimensionFormat = TensorUtils::getDescribe(X)->dimensionFormat;
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return true;
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}
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};
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REGISTER_SHAPE(LSTMComputer, OpType_LSTM);
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// LSTMCellBlock Size Computer
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class LSTMBlockCellComputer : public SizeComputer {
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virtual bool onComputeSize(const MNN::Op *op, const std::vector<Tensor *> &inputs,
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const std::vector<Tensor *> &outputs) const override {
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MNN_ASSERT(inputs.size() == 8);
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MNN_ASSERT(outputs.size() == 7);
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for (int i = 0; i < outputs.size(); i++) {
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TensorUtils::copyShape(inputs[1], outputs[i]);
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}
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return true;
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}
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};
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REGISTER_SHAPE(LSTMBlockCellComputer, OpType_LSTMBlockCell);
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// Size Computer
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class RNNComputer : public SizeComputer {
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virtual bool onComputeSize(const MNN::Op *op, const std::vector<Tensor *> &inputs,
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const std::vector<Tensor *> &outputs) const override {
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MNN_ASSERT(inputs.size() >= 4 && outputs.size() == 2);
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auto X = inputs[0];
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auto seqLength = X->length(0), batchSize = X->length(1);
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auto hiddenSize = op->main_as_LSTM()->outputCount();
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auto Y = outputs[0], ht = outputs[1];
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Y->buffer().dimensions = 4;
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ht->buffer().dimensions = 3;
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Y->setLength(0, seqLength);
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int direction = inputs[1]->length(0);
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MNN_ASSERT(1 == direction || 2 == direction);
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Y->setLength(1, direction);
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Y->setLength(2, batchSize);
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Y->setLength(3, hiddenSize);
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ht->setLength(0, direction);
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ht->setLength(1, batchSize);
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ht->setLength(2, hiddenSize);
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TensorUtils::getDescribe(Y)->dimensionFormat = TensorUtils::getDescribe(X)->dimensionFormat;
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TensorUtils::getDescribe(ht)->dimensionFormat = TensorUtils::getDescribe(X)->dimensionFormat;
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return true;
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}
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};
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REGISTER_SHAPE(RNNComputer, OpType_RNN);
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} // namespace MNN
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