229 lines
15 KiB
C++
229 lines
15 KiB
C++
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/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author Yurii Shyrma, created on 05.04.2018
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//
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#include <ops/declarable/CustomOperations.h>
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(dynamic_bidirectional_rnn, 7, 4, false, 0, 0) {
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auto x = INPUT_VARIABLE(0); // input [time x bS x inSize] or [bS x time x inSize], shape depends on timeMajor parameter
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auto WxFW = INPUT_VARIABLE(1); // input-to-hidden weights for forward RNN, [inSize x numUnitsFW]
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auto WhFW = INPUT_VARIABLE(2); // hidden-to-hidden weights for forward RNN, [numUnitsFW x numUnitsFW]
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auto bFW = INPUT_VARIABLE(3); // biases for forward RNN, [2*numUnitsFW]
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auto WxBW = INPUT_VARIABLE(4); // input-to-hidden weights for backward RNN, [inSize x numUnitsBW]
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auto WhBW = INPUT_VARIABLE(5); // hidden-to-hidden weights for backward RNN, [numUnitsBW x numUnitsBW]
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auto bBW = INPUT_VARIABLE(6); // biases for backward RNN, [2*v]
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NDArray* h0FW = nullptr; // initial cell output for forward RNN (at time step = 0) [bS x numUnitsFW]
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NDArray* h0BW = nullptr; // initial cell output for backward RNN (at time step = 0) [bS x numUnitsBW]
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NDArray* maxTimeStep = nullptr; // vector [bS] containing integer values within [0,time), each element of this vector set max time step per each input in batch, this means there are no calculations for time >= maxTimeStep
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const int timeMajor = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0; // if non zero then [time, bS, ...], else [bS, time, ...]
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switch(block.width()) {
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case 8:
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maxTimeStep = INPUT_VARIABLE(7);
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break;
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case 9:
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h0FW = INPUT_VARIABLE(7);
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h0BW = INPUT_VARIABLE(8);
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break;
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case 10:
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h0FW = INPUT_VARIABLE(7);
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h0BW = INPUT_VARIABLE(8);
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maxTimeStep = INPUT_VARIABLE(9);
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break;
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}
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auto hFW = OUTPUT_VARIABLE(0); // cell outputs for forward RNN [time x bS x numUnitsFW] or [bS x time x numUnitsFW], shape depends on timeMajor parameter
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auto hBW = OUTPUT_VARIABLE(1); // cell outputs for backward RNN [time x bS x numUnitsBW] or [bS x time x numUnitsBW], shape depends on timeMajor parameter
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auto hFWFinal = OUTPUT_VARIABLE(2); // final cell out for forward RNN [bS x numUnitsFW]
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auto hBWFinal = OUTPUT_VARIABLE(3); // final cell out for backward RNN [bS x numUnitsBF]
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REQUIRE_TRUE(x->rankOf() == 3, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input array must have rank = 3, but got %i instead !", x->rankOf());
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REQUIRE_TRUE(WxFW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for forward RNN) must have rank = 2, but got %i instead !", WxFW->rankOf());
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REQUIRE_TRUE(WxBW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for backward RNN) must have rank = 2, but got %i instead !", WxBW->rankOf());
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const int inRank = x->rankOf();
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const int time = timeMajor ? x->sizeAt(0) : x->sizeAt(1);
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const int bS = timeMajor ? x->sizeAt(1) : x->sizeAt(0);
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const int numUnitsFW = WxFW->sizeAt(1);
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const int numUnitsBW = WxBW->sizeAt(1);
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std::vector<Nd4jLong> expectedWhFWshape = {numUnitsFW, numUnitsFW};
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std::vector<Nd4jLong> expectedWhBWshape = {numUnitsBW, numUnitsBW};
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std::vector<Nd4jLong> expectedbFWshape = {2*numUnitsFW};
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std::vector<Nd4jLong> expectedbBWshape = {2*numUnitsBW};
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REQUIRE_TRUE(WhFW->isSameShape(expectedWhFWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhFWshape).c_str(), ShapeUtils::shapeAsString(WhFW).c_str());
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REQUIRE_TRUE(WhBW->isSameShape(expectedWhBWshape) , 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhBWshape).c_str(), ShapeUtils::shapeAsString(WhBW).c_str());
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REQUIRE_TRUE(bFW->isSameShape(expectedbFWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbFWshape).c_str(), ShapeUtils::shapeAsString(bFW).c_str());
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REQUIRE_TRUE(bBW->isSameShape(expectedbBWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbBWshape).c_str(), ShapeUtils::shapeAsString(bBW).c_str());
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if(h0FW) {
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std::vector<Nd4jLong> expectedh0FWshape = {bS, numUnitsFW};
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REQUIRE_TRUE(h0FW->isSameShape(expectedh0FWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0FWshape).c_str(), ShapeUtils::shapeAsString(h0FW).c_str());
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}
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if(h0BW) {
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std::vector<Nd4jLong> expectedh0BWshape = {bS, numUnitsBW};
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REQUIRE_TRUE(h0BW->isSameShape(expectedh0BWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0BWshape).c_str(), ShapeUtils::shapeAsString(h0BW).c_str());
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}
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if(maxTimeStep) {
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std::vector<Nd4jLong> expectedmaxTimeStepshape = {bS};
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REQUIRE_TRUE(maxTimeStep->isSameShape(expectedmaxTimeStepshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStep).c_str());
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}
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// forward steps
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sd::ops::dynamic_rnn dynamicRnn;
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auto resultsFW = dynamicRnn.evaluate({x, WxFW, WhFW, bFW, h0FW, maxTimeStep}, {timeMajor});
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hFW->assign(resultsFW.at(0)); // [time x bS x numUnitsFW] or [bS x time x numUnitsFW]
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hFWFinal->assign(resultsFW.at(1));
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auto seqLen = maxTimeStep;
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if(seqLen == nullptr) {
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// FIXME: which datatype should be used here?
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seqLen = new NDArray(x->ordering(), {bS}, sd::DataType::INT64, block.launchContext());
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seqLen->assign(time); // set each element of seqLen to be equal to time
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}
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// reverse x
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sd::ops::reverse_sequence reverse;
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auto resultsIn = timeMajor ? reverse.evaluate({x, seqLen}, {0, 1}) : reverse.evaluate({x, seqLen}, {1, 0});
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REQUIRE_TRUE (resultsIn.status() == ND4J_STATUS_OK, 0, "dynamic_bidirectional_rnn: there is a problem with reverse on the sequence.");
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auto revInput = resultsIn.at(0);
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// backward steps
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auto resultsBW = dynamicRnn.evaluate({revInput, WxBW, WhBW, bBW, h0BW, maxTimeStep}, {timeMajor});
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auto hBWtemp = resultsBW.at(0); // [time x bS x numUnitsBW] or [ bS x time xnumUnitsBW]
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hBWFinal->assign(resultsBW.at(1));
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// reverse hBWtemp
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auto resultsOut = timeMajor ? reverse.evaluate({hBWtemp, seqLen}, {0, 1}) : reverse.evaluate({hBWtemp, seqLen}, {1, 0});
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hBW->assign(resultsOut.at(0));
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if(seqLen != maxTimeStep)
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delete seqLen;
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return Status::OK();
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}
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DECLARE_TYPES(dynamic_bidirectional_rnn) {
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getOpDescriptor()
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->setAllowedInputTypes(sd::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(dynamic_bidirectional_rnn) {
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auto x = INPUT_VARIABLE(0); // input [time x bS x inSize] or [bS x time x inSize], shape depends on timeMajor parameter
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auto WxFW = INPUT_VARIABLE(1); // input-to-hidden weights for forward RNN, [inSize x numUnitsFW]
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auto WhFW = INPUT_VARIABLE(2); // hidden-to-hidden weights for forward RNN, [numUnitsFW x numUnitsFW]
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auto bFW = INPUT_VARIABLE(3); // biases for forward RNN, [2*numUnitsFW]
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auto WxBW = INPUT_VARIABLE(4); // input-to-hidden weights for backward RNN, [inSize x numUnitsBW]
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auto WhBW = INPUT_VARIABLE(5); // hidden-to-hidden weights for backward RNN, [numUnitsBW x numUnitsBW]
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auto bBW = INPUT_VARIABLE(6); // biases for backward RNN, [2*numUnitsBW]
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NDArray* h0FW = nullptr; // initial cell output for forward RNN (at time step = 0) [bS x numUnitsFW]
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NDArray* h0BW = nullptr; // initial cell output for backward RNN (at time step = 0) [bS x numUnitsBW]
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NDArray* maxTimeStep = nullptr; // vector [bS] containing integer values within [0,time), each element of this vector set max time step per each input in batch, this means there are no calculations for time >= maxTimeStep
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const int timeMajor = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0; // if true then [time, bS, ...], else [bS, time, ...]
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switch(block.width()) {
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case 8:
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maxTimeStep = INPUT_VARIABLE(7);
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break;
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case 9:
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h0FW = INPUT_VARIABLE(7);
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h0BW = INPUT_VARIABLE(8);
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break;
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case 10:
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h0FW = INPUT_VARIABLE(7);
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h0BW = INPUT_VARIABLE(8);
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maxTimeStep = INPUT_VARIABLE(9);
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break;
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}
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REQUIRE_TRUE(x->rankOf() == 3, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input array must have rank = 3, but got %i instead !", x->rankOf());
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REQUIRE_TRUE(WxFW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for forward RNN) must have rank = 2, but got %i instead !", WxFW->rankOf());
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REQUIRE_TRUE(WxBW->rankOf() == 2, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for backward RNN) must have rank = 2, but got %i instead !", WxBW->rankOf());
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const int inRank = x->rankOf();
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const int time = timeMajor ? x->sizeAt(0) : x->sizeAt(1);
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const int bS = timeMajor ? x->sizeAt(1) : x->sizeAt(0);
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const int numUnitsFW = WxFW->sizeAt(1);
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const int numUnitsBW = WxBW->sizeAt(1);
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std::vector<Nd4jLong> expectedWhFWshape = {numUnitsFW, numUnitsFW};
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std::vector<Nd4jLong> expectedWhBWshape = {numUnitsBW, numUnitsBW};
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std::vector<Nd4jLong> expectedbFWshape = {2*numUnitsFW};
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std::vector<Nd4jLong> expectedbBWshape = {2*numUnitsBW};
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REQUIRE_TRUE(WhFW->isSameShape(expectedWhFWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhFWshape).c_str(), ShapeUtils::shapeAsString(WhFW).c_str());
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REQUIRE_TRUE(WhBW->isSameShape(expectedWhBWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of hidden-to-hidden weights array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedWhBWshape).c_str(), ShapeUtils::shapeAsString(WhBW).c_str());
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REQUIRE_TRUE(bFW->isSameShape(expectedbFWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbFWshape).c_str(), ShapeUtils::shapeAsString(bFW).c_str());
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REQUIRE_TRUE(bBW->isSameShape(expectedbBWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedbBWshape).c_str(), ShapeUtils::shapeAsString(bBW).c_str());
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if(h0FW) {
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std::vector<Nd4jLong> expectedh0FWshape = {bS, numUnitsFW};
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REQUIRE_TRUE(h0FW->isSameShape(expectedh0FWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for forward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0FWshape).c_str(), ShapeUtils::shapeAsString(h0FW).c_str());
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}
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if(h0BW) {
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std::vector<Nd4jLong> expectedh0BWshape = {bS, numUnitsBW};
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REQUIRE_TRUE(h0BW->isSameShape(expectedh0BWshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of initial cell output array (for backward RNN), expected is %s but got %s instead !", ShapeUtils::shapeAsString(expectedh0BWshape).c_str(), ShapeUtils::shapeAsString(h0BW).c_str());
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}
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if(maxTimeStep) {
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std::vector<Nd4jLong> expectedmaxTimeStepshape = {bS};
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REQUIRE_TRUE(maxTimeStep->isSameShape(expectedmaxTimeStepshape), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStep).c_str());
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}
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// evaluate output shapeInfos
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Nd4jLong *hFWShapeInfo(nullptr), *hBWShapeInfo(nullptr), *hFWFinalPrevShapeInfo(nullptr), *hBWFinalPrevShapeInfo(nullptr);
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ALLOCATE(hFWShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank), Nd4jLong);
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ALLOCATE(hBWShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank), Nd4jLong);
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ALLOCATE(hFWFinalPrevShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank-1), Nd4jLong);
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ALLOCATE(hBWFinalPrevShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank-1), Nd4jLong);
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hFWShapeInfo[0] = hBWShapeInfo[0] = inRank;
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hFWShapeInfo[1] = hBWShapeInfo[1] = timeMajor ? time : bS;
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hFWShapeInfo[2] = hBWShapeInfo[2] = timeMajor ? bS : time;
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hFWShapeInfo[3] = numUnitsFW;
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hBWShapeInfo[3] = numUnitsBW;
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hFWFinalPrevShapeInfo[0] = hBWFinalPrevShapeInfo[0] = inRank-1;
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hFWFinalPrevShapeInfo[1] = hBWFinalPrevShapeInfo[1] = bS;
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hFWFinalPrevShapeInfo[2] = numUnitsFW;
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hBWFinalPrevShapeInfo[2] = numUnitsBW;
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ShapeUtils::updateStridesAndType(hFWShapeInfo, x->getShapeInfo(), x->ordering());
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ShapeUtils::updateStridesAndType(hBWShapeInfo, x->getShapeInfo(), x->ordering());
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ShapeUtils::updateStridesAndType(hFWFinalPrevShapeInfo, x->getShapeInfo(), x->ordering());
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ShapeUtils::updateStridesAndType(hBWFinalPrevShapeInfo, x->getShapeInfo(), x->ordering());
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return SHAPELIST(CONSTANT(hFWShapeInfo), CONSTANT(hBWShapeInfo), CONSTANT(hFWFinalPrevShapeInfo), CONSTANT(hBWFinalPrevShapeInfo));
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}
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}
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}
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