/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Yurii Shyrma, created on 05.04.2018
//
#include <ops/declarable/CustomOperations.h>
namespace nd4j {
namespace ops {
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(dynamic_bidirectional_rnn, 7, 4, false, 0, 0) {
auto x = INPUT_VARIABLE(0); // input [time x bS x inSize] or [bS x time x inSize], shape depends on timeMajor parameter
auto WxFW = INPUT_VARIABLE(1); // input-to-hidden weights for forward RNN, [inSize x numUnitsFW]
auto WhFW = INPUT_VARIABLE(2); // hidden-to-hidden weights for forward RNN, [numUnitsFW x numUnitsFW]
auto bFW = INPUT_VARIABLE(3); // biases for forward RNN, [2*numUnitsFW]
auto WxBW = INPUT_VARIABLE(4); // input-to-hidden weights for backward RNN, [inSize x numUnitsBW]
auto WhBW = INPUT_VARIABLE(5); // hidden-to-hidden weights for backward RNN, [numUnitsBW x numUnitsBW]
auto bBW = INPUT_VARIABLE(6); // biases for backward RNN, [2*v]
NDArray* h0FW = nullptr; // initial cell output for forward RNN (at time step = 0) [bS x numUnitsFW]
NDArray* h0BW = nullptr; // initial cell output for backward RNN (at time step = 0) [bS x numUnitsBW]
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
const int timeMajor = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0; // if non zero then [time, bS, ...], else [bS, time, ...]
switch(block.width()) {
case 8:
maxTimeStep = INPUT_VARIABLE(7);
break;
case 9:
h0FW = INPUT_VARIABLE(7);
h0BW = INPUT_VARIABLE(8);
break;
case 10:
h0FW = INPUT_VARIABLE(7);
h0BW = INPUT_VARIABLE(8);
maxTimeStep = INPUT_VARIABLE(9);
break;
}
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
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
auto hFWFinal = OUTPUT_VARIABLE(2); // final cell out for forward RNN [bS x numUnitsFW]
auto hBWFinal = OUTPUT_VARIABLE(3); // final cell out for backward RNN [bS x numUnitsBF]
REQUIRE_TRUE(x->rankOf() == 3, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input array must have rank = 3, but got %i instead !", x->rankOf());
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());
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());
const int inRank = x->rankOf();
const int time = timeMajor ? x->sizeAt(0) : x->sizeAt(1);
const int bS = timeMajor ? x->sizeAt(1) : x->sizeAt(0);
const int numUnitsFW = WxFW->sizeAt(1);
const int numUnitsBW = WxBW->sizeAt(1);
REQUIRE_TRUE(ShapeUtils::shapeAsString(WhFW) == ShapeUtils::shapeAsString({numUnitsFW, numUnitsFW}), 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({numUnitsFW, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(WhFW).c_str());
REQUIRE_TRUE(ShapeUtils::shapeAsString(WhBW) == ShapeUtils::shapeAsString({numUnitsBW, numUnitsBW}), 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({numUnitsBW, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(WhBW).c_str());
REQUIRE_TRUE(ShapeUtils::shapeAsString(bFW) == ShapeUtils::shapeAsString({2*numUnitsFW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsFW}).c_str(), ShapeUtils::shapeAsString(bFW).c_str());
REQUIRE_TRUE(ShapeUtils::shapeAsString(bBW) == ShapeUtils::shapeAsString({2*numUnitsBW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsBW}).c_str(), ShapeUtils::shapeAsString(bBW).c_str());
if(h0FW)
REQUIRE_TRUE(ShapeUtils::shapeAsString(h0FW) == ShapeUtils::shapeAsString({bS, numUnitsFW}), 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({bS, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(h0FW).c_str());
if(h0BW)
REQUIRE_TRUE(ShapeUtils::shapeAsString(h0BW) == ShapeUtils::shapeAsString({bS, numUnitsBW}), 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({bS, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(h0BW).c_str());
if(maxTimeStep)
REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStep) == ShapeUtils::shapeAsString({bS}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStep).c_str());
// forward steps
nd4j::ops::dynamic_rnn dynamicRnn;
auto resultsFW = dynamicRnn.evaluate({x, WxFW, WhFW, bFW, h0FW, maxTimeStep}, {timeMajor});
hFW->assign(resultsFW->at(0)); // [time x bS x numUnitsFW] or [bS x time x numUnitsFW]
hFWFinal->assign(resultsFW->at(1));
auto seqLen = maxTimeStep;
if(seqLen == nullptr) {
// FIXME: which datatype should be used here?
seqLen = new NDArray(x->ordering(), {bS}, nd4j::DataType::INT64, block.launchContext());
seqLen->assign(time); // set each element of seqLen to be equal to time
}
// reverse x
nd4j::ops::reverse_sequence reverse;
auto resultsIn = timeMajor ? reverse.evaluate({x, seqLen}, {0, 1}) : reverse.evaluate({x, seqLen}, {1, 0});
REQUIRE_TRUE (resultsIn->status() == ND4J_STATUS_OK, 0, "dynamic_bidirectional_rnn: there is a problem with reverse on the sequence.");
auto revInput = resultsIn->at(0);
// backward steps
auto resultsBW = dynamicRnn.evaluate({revInput, WxBW, WhBW, bBW, h0BW, maxTimeStep}, {timeMajor});
auto hBWtemp = resultsBW->at(0); // [time x bS x numUnitsBW] or [ bS x time xnumUnitsBW]
hBWFinal->assign(resultsBW->at(1));
// reverse hBWtemp
auto resultsOut = timeMajor ? reverse.evaluate({hBWtemp, seqLen}, {0, 1}) : reverse.evaluate({hBWtemp, seqLen}, {1, 0});
hBW->assign(resultsOut->at(0));
delete resultsOut;
delete resultsBW;
delete resultsIn;
delete resultsFW;
if(seqLen != maxTimeStep)
delete seqLen;
return Status::OK();
}
DECLARE_TYPES(dynamic_bidirectional_rnn) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(dynamic_bidirectional_rnn) {
auto x = INPUT_VARIABLE(0); // input [time x bS x inSize] or [bS x time x inSize], shape depends on timeMajor parameter
auto WxFW = INPUT_VARIABLE(1); // input-to-hidden weights for forward RNN, [inSize x numUnitsFW]
auto WhFW = INPUT_VARIABLE(2); // hidden-to-hidden weights for forward RNN, [numUnitsFW x numUnitsFW]
auto bFW = INPUT_VARIABLE(3); // biases for forward RNN, [2*numUnitsFW]
auto WxBW = INPUT_VARIABLE(4); // input-to-hidden weights for backward RNN, [inSize x numUnitsBW]
auto WhBW = INPUT_VARIABLE(5); // hidden-to-hidden weights for backward RNN, [numUnitsBW x numUnitsBW]
auto bBW = INPUT_VARIABLE(6); // biases for backward RNN, [2*numUnitsBW]
NDArray* h0FW = nullptr; // initial cell output for forward RNN (at time step = 0) [bS x numUnitsFW]
NDArray* h0BW = nullptr; // initial cell output for backward RNN (at time step = 0) [bS x numUnitsBW]
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
const int timeMajor = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0; // if true then [time, bS, ...], else [bS, time, ...]
switch(block.width()) {
case 8:
maxTimeStep = INPUT_VARIABLE(7);
break;
case 9:
h0FW = INPUT_VARIABLE(7);
h0BW = INPUT_VARIABLE(8);
break;
case 10:
h0FW = INPUT_VARIABLE(7);
h0BW = INPUT_VARIABLE(8);
maxTimeStep = INPUT_VARIABLE(9);
break;
}
REQUIRE_TRUE(x->rankOf() == 3, 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: input array must have rank = 3, but got %i instead !", x->rankOf());
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());
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());
const int inRank = x->rankOf();
const int time = timeMajor ? x->sizeAt(0) : x->sizeAt(1);
const int bS = timeMajor ? x->sizeAt(1) : x->sizeAt(0);
const int numUnitsFW = WxFW->sizeAt(1);
const int numUnitsBW = WxBW->sizeAt(1);
REQUIRE_TRUE(ShapeUtils::shapeAsString(WhFW) == ShapeUtils::shapeAsString({numUnitsFW, numUnitsFW}), 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({numUnitsFW, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(WhFW).c_str());
REQUIRE_TRUE(ShapeUtils::shapeAsString(WhBW) == ShapeUtils::shapeAsString({numUnitsBW, numUnitsBW}), 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({numUnitsBW, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(WhBW).c_str());
REQUIRE_TRUE(ShapeUtils::shapeAsString(bFW) == ShapeUtils::shapeAsString({2*numUnitsFW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for forward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsFW}).c_str(), ShapeUtils::shapeAsString(bFW).c_str());
REQUIRE_TRUE(ShapeUtils::shapeAsString(bBW) == ShapeUtils::shapeAsString({2*numUnitsBW}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of biases array (for backward RNN), expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnitsBW}).c_str(), ShapeUtils::shapeAsString(bBW).c_str());
if(h0FW)
REQUIRE_TRUE(ShapeUtils::shapeAsString(h0FW) == ShapeUtils::shapeAsString({bS, numUnitsFW}), 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({bS, numUnitsFW}).c_str(), ShapeUtils::shapeAsString(h0FW).c_str());
if(h0BW)
REQUIRE_TRUE(ShapeUtils::shapeAsString(h0BW) == ShapeUtils::shapeAsString({bS, numUnitsBW}), 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({bS, numUnitsBW}).c_str(), ShapeUtils::shapeAsString(h0BW).c_str());
if(maxTimeStep)
REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStep) == ShapeUtils::shapeAsString({bS}), 0, "DYNAMIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStep).c_str());
// evaluate output shapeInfos
Nd4jLong *hFWShapeInfo(nullptr), *hBWShapeInfo(nullptr), *hFWFinalPrevShapeInfo(nullptr), *hBWFinalPrevShapeInfo(nullptr);
ALLOCATE(hFWShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank), Nd4jLong);
ALLOCATE(hBWShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank), Nd4jLong);
ALLOCATE(hFWFinalPrevShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank-1), Nd4jLong);
ALLOCATE(hBWFinalPrevShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank-1), Nd4jLong);
hFWShapeInfo[0] = hBWShapeInfo[0] = inRank;
hFWShapeInfo[1] = hBWShapeInfo[1] = timeMajor ? time : bS;
hFWShapeInfo[2] = hBWShapeInfo[2] = timeMajor ? bS : time;
hFWShapeInfo[3] = numUnitsFW;
hBWShapeInfo[3] = numUnitsBW;
hFWFinalPrevShapeInfo[0] = hBWFinalPrevShapeInfo[0] = inRank-1;
hFWFinalPrevShapeInfo[1] = hBWFinalPrevShapeInfo[1] = bS;
hFWFinalPrevShapeInfo[2] = numUnitsFW;
hBWFinalPrevShapeInfo[2] = numUnitsBW;
ShapeUtils::updateStridesAndType(hFWShapeInfo, x->getShapeInfo(), x->ordering());
ShapeUtils::updateStridesAndType(hBWShapeInfo, x->getShapeInfo(), x->ordering());
ShapeUtils::updateStridesAndType(hFWFinalPrevShapeInfo, x->getShapeInfo(), x->ordering());
ShapeUtils::updateStridesAndType(hBWFinalPrevShapeInfo, x->getShapeInfo(), x->ordering());
return SHAPELIST(CONSTANT(hFWShapeInfo), CONSTANT(hBWShapeInfo), CONSTANT(hFWFinalPrevShapeInfo), CONSTANT(hBWFinalPrevShapeInfo));
}
}
}