/******************************************************************************* * 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 03.04.2018 // #include #include #include #include namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(static_bidirectional_rnn, 7, 3, false, 0, 0) { auto x = INPUT_VARIABLE(0); // input [time x bS x inSize] 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 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 h = OUTPUT_VARIABLE(0); // cell outputs [time x bS x (numUnitsFW + numUnitsBW)], that is per each time step auto hFWFinal = OUTPUT_VARIABLE(1); // final cell out for forward RNN [bS x numUnitsFW] auto hBWFinal = OUTPUT_VARIABLE(2); // final cell out for backward RNN [bS x numUnitsBF] REQUIRE_TRUE(x->rankOf() == 3, 0, "STATIC_BIDIRECTIONAL_RNN custom operation: input array must have rank = 3, but got %i instead !", x->rankOf()); 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()); 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()); const Nd4jLong inRank = x->rankOf(); const Nd4jLong time = x->sizeAt(0); const Nd4jLong bS = x->sizeAt(1); const Nd4jLong numUnitsFW = WxFW->sizeAt(1); const Nd4jLong numUnitsBW = WxBW->sizeAt(1); const std::vector expectedWhFWshape = {numUnitsFW, numUnitsFW}; const std::vector expectedWhBWshape = {numUnitsBW, numUnitsBW}; const std::vector expectedbFWshape = {2 * numUnitsFW}; const std::vector expectedbBWshape = {2 * numUnitsBW}; 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()); 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()); 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()); 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()); if(h0FW) { const std::vector expectedh0FWshape = {bS, numUnitsFW}; 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()); } if(h0BW) { const std::vector expectedh0BWshape = {bS, numUnitsBW}; 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()); } if(maxTimeStep) 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()); // forward steps auto hFW = new NDArray(x->ordering(), {time, bS, numUnitsFW}, x->dataType(), block.launchContext()); helpers::rnnTimeLoop(block.launchContext(), x, WxFW, WhFW, bFW, h0FW, maxTimeStep, hFW, hFWFinal); auto seqLen = maxTimeStep; if(seqLen == nullptr) { // seqLen = new NDArray(x->ordering(), {x->sizeAt(1)}, x->dataType(), block.launchContext()); // [bS] seqLen = new NDArray(x->ordering(), {x->sizeAt(1)}, sd::DataType::INT64, block.launchContext()); // [bS] *seqLen = x->sizeAt(0); // set each element of seqLen to be equal to time } // reverse x auto revOut = new NDArray(x, false, block.launchContext()); helpers::reverseSequence(block.launchContext(), x, seqLen, revOut, 0, 1); // backward steps auto hBW = new NDArray(x->ordering(), {time, bS, numUnitsBW}, x->dataType(), block.launchContext()); helpers::rnnTimeLoop(block.launchContext(), revOut, WxBW, WhBW, bBW, h0BW, maxTimeStep, hBW, hBWFinal); // reverse hBW auto hBWcopy = new NDArray(*hBW); helpers::reverseSequence(block.launchContext(), hBWcopy, seqLen, hBW, 0, 1); // concatenate hFW and hBW along last third dimension // NDArrayFactory::concat({hFW, hBW}, 2, h); helpers::concat(block.launchContext(), {hFW, hBW}, *h, 2); delete hBW; delete hFW; delete hBWcopy; delete revOut; if(seqLen != maxTimeStep) delete seqLen; return Status::OK(); } DECLARE_TYPES(static_bidirectional_rnn) { getOpDescriptor() ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(static_bidirectional_rnn) { auto xShapeInfo = inputShape->at(0); // input [time x bS x inSize] auto WxFWShapeInfo = inputShape->at(1); // input-to-hidden weights for forward RNN, [inSize x numUnitsFW] auto WhFWShapeInfo = inputShape->at(2); // hidden-to-hidden weights for forward RNN, [numUnitsFW x numUnitsFW] auto bFWShapeInfo = inputShape->at(3); // biases for forward RNN, [2*numUnitsFW] auto WxBWShapeInfo = inputShape->at(4); // input-to-hidden weights for backward RNN, [inSize x numUnitsBW] auto WhBWShapeInfo = inputShape->at(5); // hidden-to-hidden weights for backward RNN, [numUnitsBW x numUnitsBW] auto bBWShapeInfo = inputShape->at(6); // biases for backward RNN, [2*numUnitsBW] Nd4jLong const* h0FWShapeInfo = nullptr; // initial cell output for forward RNN (at time step = 0) [bS x numUnitsFW] Nd4jLong const* h0BWShapeInfo = nullptr; // initial cell output for backward RNN (at time step = 0) [bS x numUnitsBW] 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 switch(block.width()) { case 8: maxTimeStepShapeInfo = inputShape->at(7); break; case 9: h0FWShapeInfo = inputShape->at(7); h0BWShapeInfo = inputShape->at(8); break; case 10: h0FWShapeInfo = inputShape->at(7); h0BWShapeInfo = inputShape->at(8); maxTimeStepShapeInfo = inputShape->at(9); break; } REQUIRE_TRUE(xShapeInfo[0] == 3, 0, "STATIC_BIDIRECTIONAL_RNN custom operation: input array must have rank = 3, but got %i instead !", xShapeInfo[0]); 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]); 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]); const int inRank = xShapeInfo[0]; const int time = xShapeInfo[1]; const int bS = xShapeInfo[2]; const int numUnitsFW = WxFWShapeInfo[2]; const int numUnitsBW = WxBWShapeInfo[2]; const std::vector expectedWhFWshape = {numUnitsFW, numUnitsFW}; const std::vector expectedWhBWshape = {numUnitsBW, numUnitsBW}; const std::vector expectedbFWshape = {2 * numUnitsFW}; const std::vector expectedbBWshape = {2 * numUnitsBW}; 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()); 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()); 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()); 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()); if(h0FWShapeInfo) { const std::vector expectedh0FWshape = {bS, numUnitsFW}; 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()); } if(h0BWShapeInfo) { const std::vector expectedh0BWshape = {bS, numUnitsBW}; 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()); } if(maxTimeStepShapeInfo) 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()); // evaluate output shapeInfos Nd4jLong *hShapeInfo(nullptr), *hFWFinalPrevShapeInfo(nullptr), *hBWFinalPrevShapeInfo(nullptr); ALLOCATE(hShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank), Nd4jLong); ALLOCATE(hFWFinalPrevShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank-1), Nd4jLong); ALLOCATE(hBWFinalPrevShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank-1), Nd4jLong); hShapeInfo[0] = inRank; hFWFinalPrevShapeInfo[0] = hBWFinalPrevShapeInfo[0] = inRank-1; hShapeInfo[1] = time; hShapeInfo[2] = hFWFinalPrevShapeInfo[1] = hBWFinalPrevShapeInfo[1] = bS; hShapeInfo[3] = numUnitsFW + numUnitsBW; hFWFinalPrevShapeInfo[2] = numUnitsFW; hBWFinalPrevShapeInfo[2] = numUnitsBW; ShapeUtils::updateStridesAndType(hShapeInfo, xShapeInfo, shape::order(xShapeInfo)); ShapeUtils::updateStridesAndType(hFWFinalPrevShapeInfo, xShapeInfo, shape::order(xShapeInfo)); ShapeUtils::updateStridesAndType(hBWFinalPrevShapeInfo, xShapeInfo, shape::order(xShapeInfo)); return SHAPELIST(CONSTANT(hShapeInfo), CONSTANT(hFWFinalPrevShapeInfo), CONSTANT(hBWFinalPrevShapeInfo)); } } }