232 lines
14 KiB
C++
232 lines
14 KiB
C++
/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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 03.04.2018
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//
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#include <ops/declarable/CustomOperations.h>
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#include<ops/declarable/helpers/rnn.h>
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#include<ops/declarable/helpers/reverse.h>
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#include<ops/declarable/helpers/transforms.h>
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(static_bidirectional_rnn, 7, 3, false, 0, 0) {
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auto x = INPUT_VARIABLE(0); // input [time x bS x inSize]
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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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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 h = OUTPUT_VARIABLE(0); // cell outputs [time x bS x (numUnitsFW + numUnitsBW)], that is per each time step
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auto hFWFinal = OUTPUT_VARIABLE(1); // final cell out for forward RNN [bS x numUnitsFW]
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auto hBWFinal = OUTPUT_VARIABLE(2); // final cell out for backward RNN [bS x numUnitsBF]
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REQUIRE_TRUE(x->rankOf() == 3, 0, "STATIC_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, "STATIC_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, "STATIC_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 Nd4jLong inRank = x->rankOf();
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const Nd4jLong time = x->sizeAt(0);
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const Nd4jLong bS = x->sizeAt(1);
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const Nd4jLong numUnitsFW = WxFW->sizeAt(1);
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const Nd4jLong numUnitsBW = WxBW->sizeAt(1);
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const std::vector<Nd4jLong> expectedWhFWshape = {numUnitsFW, numUnitsFW};
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const std::vector<Nd4jLong> expectedWhBWshape = {numUnitsBW, numUnitsBW};
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const std::vector<Nd4jLong> expectedbFWshape = {2 * numUnitsFW};
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const std::vector<Nd4jLong> expectedbBWshape = {2 * numUnitsBW};
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REQUIRE_TRUE(WhFW->isSameShape(expectedWhFWshape), 0, "STATIC_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, "STATIC_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, "STATIC_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, "STATIC_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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const std::vector<Nd4jLong> expectedh0FWshape = {bS, numUnitsFW};
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REQUIRE_TRUE(h0FW->isSameShape(expectedh0FWshape), 0, "STATIC_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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const std::vector<Nd4jLong> expectedh0BWshape = {bS, numUnitsBW};
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REQUIRE_TRUE(h0BW->isSameShape(expectedh0BWshape), 0, "STATIC_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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REQUIRE_TRUE(maxTimeStep->isSameShape({bS}), 0, "STATIC_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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// forward steps
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auto hFW = new NDArray(x->ordering(), {time, bS, numUnitsFW}, x->dataType(), block.launchContext());
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helpers::rnnTimeLoop(block.launchContext(), x, WxFW, WhFW, bFW, h0FW, maxTimeStep, hFW, hFWFinal);
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auto seqLen = maxTimeStep;
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if(seqLen == nullptr) {
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// seqLen = new NDArray(x->ordering(), {x->sizeAt(1)}, x->dataType(), block.launchContext()); // [bS]
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seqLen = new NDArray(x->ordering(), {x->sizeAt(1)}, sd::DataType::INT64, block.launchContext()); // [bS]
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*seqLen = x->sizeAt(0); // set each element of seqLen to be equal to time
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}
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// reverse x
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auto revOut = new NDArray(x, false, block.launchContext());
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helpers::reverseSequence(block.launchContext(), x, seqLen, revOut, 0, 1);
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// backward steps
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auto hBW = new NDArray(x->ordering(), {time, bS, numUnitsBW}, x->dataType(), block.launchContext());
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helpers::rnnTimeLoop(block.launchContext(), revOut, WxBW, WhBW, bBW, h0BW, maxTimeStep, hBW, hBWFinal);
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// reverse hBW
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auto hBWcopy = new NDArray(*hBW);
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helpers::reverseSequence(block.launchContext(), hBWcopy, seqLen, hBW, 0, 1);
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// concatenate hFW and hBW along last third dimension
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// NDArrayFactory<T>::concat({hFW, hBW}, 2, h);
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helpers::concat(block.launchContext(), {hFW, hBW}, *h, 2);
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delete hBW;
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delete hFW;
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delete hBWcopy;
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delete revOut;
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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(static_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(static_bidirectional_rnn) {
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auto xShapeInfo = inputShape->at(0); // input [time x bS x inSize]
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auto WxFWShapeInfo = inputShape->at(1); // input-to-hidden weights for forward RNN, [inSize x numUnitsFW]
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auto WhFWShapeInfo = inputShape->at(2); // hidden-to-hidden weights for forward RNN, [numUnitsFW x numUnitsFW]
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auto bFWShapeInfo = inputShape->at(3); // biases for forward RNN, [2*numUnitsFW]
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auto WxBWShapeInfo = inputShape->at(4); // input-to-hidden weights for backward RNN, [inSize x numUnitsBW]
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auto WhBWShapeInfo = inputShape->at(5); // hidden-to-hidden weights for backward RNN, [numUnitsBW x numUnitsBW]
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auto bBWShapeInfo = inputShape->at(6); // biases for backward RNN, [2*numUnitsBW]
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Nd4jLong const* h0FWShapeInfo = nullptr; // initial cell output for forward RNN (at time step = 0) [bS x numUnitsFW]
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Nd4jLong const* h0BWShapeInfo = nullptr; // initial cell output for backward RNN (at time step = 0) [bS x numUnitsBW]
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Nd4jLong const* maxTimeStepShapeInfo = 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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switch(block.width()) {
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case 8:
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maxTimeStepShapeInfo = inputShape->at(7);
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break;
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case 9:
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h0FWShapeInfo = inputShape->at(7);
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h0BWShapeInfo = inputShape->at(8);
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break;
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case 10:
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h0FWShapeInfo = inputShape->at(7);
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h0BWShapeInfo = inputShape->at(8);
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maxTimeStepShapeInfo = inputShape->at(9);
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break;
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}
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REQUIRE_TRUE(xShapeInfo[0] == 3, 0, "STATIC_BIDIRECTIONAL_RNN custom operation: input array must have rank = 3, but got %i instead !", xShapeInfo[0]);
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REQUIRE_TRUE(WxFWShapeInfo[0] == 2, 0, "STATIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for forward RNN) must have rank = 2, but got %i instead !", WxFWShapeInfo[0]);
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REQUIRE_TRUE(WxBWShapeInfo[0] == 2, 0, "STATIC_BIDIRECTIONAL_RNN custom operation: input-to-hidden weights array (for backward RNN) must have rank = 2, but got %i instead !", WxBWShapeInfo[0]);
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const int inRank = xShapeInfo[0];
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const int time = xShapeInfo[1];
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const int bS = xShapeInfo[2];
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const int numUnitsFW = WxFWShapeInfo[2];
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const int numUnitsBW = WxBWShapeInfo[2];
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const std::vector<Nd4jLong> expectedWhFWshape = {numUnitsFW, numUnitsFW};
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const std::vector<Nd4jLong> expectedWhBWshape = {numUnitsBW, numUnitsBW};
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const std::vector<Nd4jLong> expectedbFWshape = {2 * numUnitsFW};
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const std::vector<Nd4jLong> expectedbBWshape = {2 * numUnitsBW};
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(WhFWShapeInfo, expectedWhFWshape), 0, "STATIC_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(WhFWShapeInfo).c_str());
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(WhBWShapeInfo, expectedWhBWshape), 0, "STATIC_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(WhBWShapeInfo).c_str());
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(bFWShapeInfo, expectedbFWshape), 0, "STATIC_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(bFWShapeInfo).c_str());
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(bBWShapeInfo, expectedbBWshape), 0, "STATIC_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(bBWShapeInfo).c_str());
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if(h0FWShapeInfo) {
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const std::vector<Nd4jLong> expectedh0FWshape = {bS, numUnitsFW};
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(h0FWShapeInfo, expectedh0FWshape), 0, "STATIC_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(h0FWShapeInfo).c_str());
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}
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if(h0BWShapeInfo) {
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const std::vector<Nd4jLong> expectedh0BWshape = {bS, numUnitsBW};
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(h0BWShapeInfo, expectedh0BWshape), 0, "STATIC_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(h0BWShapeInfo).c_str());
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}
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if(maxTimeStepShapeInfo)
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(maxTimeStepShapeInfo, {bS}), 0, "STATIC_BIDIRECTIONAL_RNN custom operation: wrong shape of maxTimeStep array, expected is [%i], but got %s instead !", bS, ShapeUtils::shapeAsString(maxTimeStepShapeInfo).c_str());
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// evaluate output shapeInfos
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Nd4jLong *hShapeInfo(nullptr), *hFWFinalPrevShapeInfo(nullptr), *hBWFinalPrevShapeInfo(nullptr);
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ALLOCATE(hShapeInfo, 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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hShapeInfo[0] = inRank;
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hFWFinalPrevShapeInfo[0] = hBWFinalPrevShapeInfo[0] = inRank-1;
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hShapeInfo[1] = time;
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hShapeInfo[2] = hFWFinalPrevShapeInfo[1] = hBWFinalPrevShapeInfo[1] = bS;
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hShapeInfo[3] = numUnitsFW + numUnitsBW;
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hFWFinalPrevShapeInfo[2] = numUnitsFW;
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hBWFinalPrevShapeInfo[2] = numUnitsBW;
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ShapeUtils::updateStridesAndType(hShapeInfo, xShapeInfo, shape::order(xShapeInfo));
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ShapeUtils::updateStridesAndType(hFWFinalPrevShapeInfo, xShapeInfo, shape::order(xShapeInfo));
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ShapeUtils::updateStridesAndType(hBWFinalPrevShapeInfo, xShapeInfo, shape::order(xShapeInfo));
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return SHAPELIST(CONSTANT(hShapeInfo), CONSTANT(hFWFinalPrevShapeInfo), CONSTANT(hBWFinalPrevShapeInfo));
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}
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}
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}
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