940 lines
57 KiB
C++
940 lines
57 KiB
C++
/*******************************************************************************
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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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// implementation of operations for Simple Recurrent Unit: arXiv:1709.02755v2 [cs.CL] 12 Sep 2017
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//
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//@author Yurii Shyrma
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_sru)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/sru.h>
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#include <helpers/MmulHelper.h>
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#include <helpers/PointersManager.h>
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(sru, 5, 2, false, 0, 0) {
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auto x = INPUT_VARIABLE(0); // X, input 3d tensor [bS x inSize x time], time - number of time steps, bS - batch size, inSize - number of features
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auto w = INPUT_VARIABLE(1); // W, 2d tensor of weights [3*inSize x inSize]
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auto b = INPUT_VARIABLE(2); // B, row of biases with twice length [2*inSize]
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auto c0 = INPUT_VARIABLE(3); // C_{0}, 2d tensor of initial state [bS x inSize] at time t=0
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auto mask = block.width() > 4 ? INPUT_VARIABLE(4) : nullptr; // optional, 2d tensor of dropout mask [bS x inSize]
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auto h = OUTPUT_VARIABLE(0); // cell outputs, [bS x inSize x time]
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auto c = OUTPUT_VARIABLE(1); // cell states, [bS x inSize x time]
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const int rank = x->rankOf(); // = 3
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const auto bS = x->sizeAt(0);
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const auto inSize = x->sizeAt(1);
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const auto time = x->sizeAt(2);
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// input shapes validation
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REQUIRE_TRUE(w->rankOf() == rank-1, 0, "SRU operation: wrong rank of weights array, expected is %i, but got %i instead !", rank-1, w->rankOf());
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REQUIRE_TRUE(b->rankOf() == 1, 0, "SRU operation: wrong rank of biases array, expected is %i, but got %i instead !", 1, b->rankOf());
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REQUIRE_TRUE(c0->rankOf() == rank-1, 0, "SRU operation: wrong rank of initial state array, expected is %i, but got %i instead !", rank-1, c0->rankOf());
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if(mask)
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REQUIRE_TRUE(mask->rankOf() == rank-1, 0, "SRU operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, mask->rankOf());
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const std::vector<Nd4jLong> wCorrectShape = {3*inSize, inSize};
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const std::vector<Nd4jLong> bCorrectShape = {2*inSize};
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const std::vector<Nd4jLong> c0CorrectShape = {bS, inSize};
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REQUIRE_TRUE(w->isSameShape(wCorrectShape), 0, "SRU operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(w).c_str());
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REQUIRE_TRUE(b->isSameShape(bCorrectShape), 0, "SRU operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(b).c_str());
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REQUIRE_TRUE(c0->isSameShape(c0CorrectShape), 0, "SRU operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0).c_str());
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if(mask)
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REQUIRE_TRUE(mask->isSameShape(c0CorrectShape), 0, "SRU operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(mask).c_str());
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// xm = x * mask
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auto xm = x;
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if(mask) {
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xm = new NDArray(x->getShapeInfo(), true, block.launchContext());
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x->applyBroadcast(broadcast::Multiply, {0, 1}, *mask, *xm);
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}
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// time loop
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helpers::sruTimeLoop(block.launchContext(), xm, c0, w, b, h, c);
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if(mask)
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delete xm;
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return Status::OK();
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}
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DECLARE_TYPES(sru) {
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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(sru) {
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auto xShapeInfo = inputShape->at(0); // X, input 3d tensor [bS x inSize x time], time - number of time steps, bS - batch size, inSize - number of features
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auto wShapeInfo = inputShape->at(1); // W, 2d tensor of weights [3*inSize x inSize]
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auto bShapeInfo = inputShape->at(2); // B, row of biases with twice length [2*inSize]
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auto c0ShapeInfo = inputShape->at(3); // C_{0}, 2d tensor of initial state [bS x inSize] at time t=0
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Nd4jLong* maskShapeInfo = block.width() > 4 ? inputShape->at(4) : nullptr; // optional, 2d tensor of dropout mask [bS x inSize]
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const int rank = xShapeInfo[0]; // = 3
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const int bS = xShapeInfo[1];
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const int inSize = xShapeInfo[2];
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const int time = xShapeInfo[3];
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// input shapes validation
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REQUIRE_TRUE(wShapeInfo[0] == rank-1, 0, "SRU operation: wrong rank of weights array, expected is %i, but got %i instead !", rank-1, wShapeInfo[0]);
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REQUIRE_TRUE(bShapeInfo[0] == 1, 0, "SRU operation: wrong rank of biases array, expected is %i, but got %i instead !", 1, bShapeInfo[0]);
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REQUIRE_TRUE(c0ShapeInfo[0] == rank-1, 0, "SRU operation: wrong rank of initial state array, expected is %i, but got %i instead !", rank-1, c0ShapeInfo[0]);
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if(maskShapeInfo)
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REQUIRE_TRUE(maskShapeInfo[0] == rank-1, 0, "SRU operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, maskShapeInfo[0]);
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const std::vector<Nd4jLong> wCorrectShape = {3*inSize, inSize};
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const std::vector<Nd4jLong> bCorrectShape = {2*inSize};
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const std::vector<Nd4jLong> c0CorrectShape = {bS, inSize};
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(wShapeInfo, wCorrectShape), 0, "SRU operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(wShapeInfo).c_str());
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, bCorrectShape), 0, "SRU operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str());
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(c0ShapeInfo, c0CorrectShape), 0, "SRU operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0ShapeInfo).c_str());
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if(maskShapeInfo)
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(maskShapeInfo, c0CorrectShape), 0, "SRU operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(maskShapeInfo).c_str());
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Nd4jLong* newShapeInfo1 = nullptr;
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ALLOCATE(newShapeInfo1, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); // [bS x inSize x time]
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newShapeInfo1[0] = rank;
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newShapeInfo1[1] = bS;
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newShapeInfo1[2] = inSize;
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newShapeInfo1[3] = time;
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ShapeUtils::updateStridesAndType(newShapeInfo1, xShapeInfo, shape::order(xShapeInfo));
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ShapeDescriptor descriptor(newShapeInfo1);
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RELEASE(newShapeInfo1, block.getWorkspace());
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auto result = ConstantShapeHelper::getInstance()->createShapeInfo(descriptor);
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return SHAPELIST(result, result);
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(sru_bp, 8, 4, true, 0, 0) {
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auto x = INPUT_VARIABLE(0); // X, input 3d tensor [bS x K x N], N - number of time steps, bS - batch size, K - number of features
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auto w = INPUT_VARIABLE(1); // W, 2d tensor of weights [3K x K]
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auto b = INPUT_VARIABLE(2); // B, row of biases with twice length [1 x 2*K]
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auto c0 = INPUT_VARIABLE(3); // C_{0}, 2d tensor of initial state [bS x K] at time t=0
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auto c = INPUT_VARIABLE(4); // C, [bS x K x N]
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auto inGradCt = INPUT_VARIABLE(5); // [bS x K]
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auto inGradH = INPUT_VARIABLE(6); // [bS x K x N]
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NDArray* mask = nullptr; // optional, 2d tensor of dropout mask [bS x K]
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bool applyMask = false;
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if (block.width() > 7) {
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mask = INPUT_VARIABLE(7);
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applyMask = true;
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}
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auto gradX = OUTPUT_VARIABLE(0); // [bS x K x N]
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auto gradW = OUTPUT_VARIABLE(1); // [bS x 3K x K]
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auto gradB = OUTPUT_VARIABLE(2); // [1 x 2K]
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auto gradInit = OUTPUT_VARIABLE(3); // [bS x K]
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const int bS = x->shapeOf()[0];
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const int K = x->shapeOf()[1];
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const int N = x->shapeOf()[2]; // N - number of time steps
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auto gradBias = NDArrayFactory::create_(x->ordering(), {bS, 2*K, N}, gradX->dataType(), block.launchContext());
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auto gradU = NDArrayFactory::create_(x->ordering(), {bS, 3*K, N}, gradX->dataType(), block.launchContext());
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auto gradHX = NDArrayFactory::create_(x->ordering(), {bS, K, N}, gradX->dataType(), block.launchContext());
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auto gct = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext());
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auto gradTanh = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext());
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auto gradCt = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext());
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auto ftMinus = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext());
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auto rtMinus = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext());
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auto temp1 = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext());
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auto temp2 = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext());
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// x = x * mask
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if(applyMask)
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x->applyBroadcast(broadcast::Multiply, {0, 1}, *mask, *x); // apply mask
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// multiplication matrix wi = matmul(w,x), U = WX
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auto wi = MmulHelper::mmul(w, x, nullptr, 1., 0.); // U [bS x 3K x N]
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auto wiZ = (*wi)({0,0, 0,K, 0,0}, true); // [bS x K x N]
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auto wiF = (*wi)({0,0, K,2*K, 0,0}, true); // forget gate [bS x K x N]
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auto wiR = (*wi)({0,0, 2*K,3*K, 0,0}, true); // reset gate [bS x K x N]
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auto bF = (*b) ({0,0, 0,K }, true); // biases for forget gate [1 x K]
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auto bR = (*b) ({0,0, K,2*K}, true); // biases for reset gate [1 x K]
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auto gradBF = (*gradBias)({0,0, 0,K, 0,0}, true); // [bS x K x N]
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auto gradBR = (*gradBias)({0,0, K,2*K, 0,0}, true); // [bS x K x N]
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auto gradUZ = (*gradU) ({0,0, 0,K, 0,0}, true ); // [bS x K x N]
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auto gradUF = (*gradU) ({0,0, K,2*K, 0,0}, true ); // [bS x K x N]
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auto gradUR = (*gradU) ({0,0, 2*K,3*K, 0,0}, true ); // [bS x K x N]
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NDArray* ct_1 = nullptr;
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std::vector<Nd4jLong> idx = {0,0, 0,0, 0,0};
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for (int t = N-1; t >=0 ; --t) {
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// initialization
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idx[4] = t;
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idx[5] = t + 1;
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auto xt = (*x)(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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auto zt = wiZ(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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auto ft = wiF(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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auto rt = wiR(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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auto ct = (*c)(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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auto inGradHt = (*inGradH)(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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auto gradBRt = gradBR(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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auto gradBFt = gradBF(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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auto gradHXt = (*gradHX)(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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auto gradUZt = gradUZ(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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auto gradUFt = gradUF(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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auto gradURt = gradUR(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
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if(t != 0) {
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idx[4] = t - 1;
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idx[5] = t;
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ct_1 = new NDArray((*c)(idx)); // previous c_{t-1}
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}
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else
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ct_1 = c0;
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///////////////// forward
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// ft = sigmoid(ft + bf), rt = sigmoid(rt + bR)
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ft.addRowVector(bF, ft);
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rt.addRowVector(bR, rt);
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ft.applyTransform(transform::Sigmoid, ft);
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rt.applyTransform(transform::Sigmoid, rt);
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// TODO T val = (activation_type == 1) ? tanh(cur) : ((activation_type == 2) ? reluf(cur) : cur );
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ct.applyTransform(transform::Tanh, *gct);
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// ftMinus = 1-ft, rtMinus = 1-rt
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ft.applyTransform(transform::OneMinus, *ftMinus);
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rt.applyTransform(transform::OneMinus, *rtMinus);
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///////////////// backward
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// bR, *grad_brt_ptr = inGradHt * (g_ct - xt) * (1.0f - rt) * rt;
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gct->applyPairwiseTransform(pairwise::Subtract, xt, *temp1); // temp1 = (g_ct - xt)
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rtMinus->applyPairwiseTransform(pairwise::Multiply, rt, *temp2); // temp2 = (1.0f - rt) * rt;
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temp1->applyPairwiseTransform(pairwise::Multiply, *temp2); // temp1 = (g_ct - xt) * (1.0f - rt) * rt;
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inGradHt.applyPairwiseTransform(pairwise::Multiply, *temp1, gradBRt); // = inGradHt * (g_ct - xt) * (1.0f - rt) * rt;
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// bF, TODO - tanh
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// gradTanh = (1.0f - g_ct * g_ct);
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gct->applyPairwiseTransform(pairwise::Multiply, *gct, *gradTanh); // gradTanh = g_ct * g_ct
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gradTanh->applyTransform(transform::OneMinus, *gradTanh); // gradTanh = (1.0f - g_ct * g_ct)
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// gradCt = inGradHt * rt * gradTanh
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rt.applyPairwiseTransform(pairwise::Multiply, *gradTanh, *gradCt); // gradCt = rt * gradTanh
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inGradHt.applyPairwiseTransform(pairwise::Multiply, *gradCt, *gradCt); // gradCt = inGradHt * rt * gradTanh
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// gradBFt = (gradCt + inGradCt) * (ct_1 - zt) * (1 - ft) * ft;
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gradCt->applyPairwiseTransform(pairwise::Add, *inGradCt, *temp1); // temp1 = (gradCt + inGradCt)
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ct_1->applyPairwiseTransform(pairwise::Subtract, zt, *temp2); // temp2 = (ct_1 - zt)
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temp1->applyPairwiseTransform(pairwise::Multiply, *ftMinus, *temp1); // temp1 = (gradCt + inGradCt)*(1-ft)
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temp1->applyPairwiseTransform(pairwise::Multiply, ft, *temp1); // temp1 = (gradCt + inGradCt)*(1-ft)*ft
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temp1->applyPairwiseTransform(pairwise::Multiply, *temp2, gradBFt); // gradBFt = (gradCt + inGradCt) * (ct_1 - zt) * (1 - ft) * ft;
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// x_t (highway connection), gradHXt = inGradHt * (1.0f - rt);
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inGradHt.applyPairwiseTransform(pairwise::Multiply, *rtMinus, gradHXt);
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// U_t, gradUZt = (inGradHt * rt * grad_tanh + inGradCt) * (1.0f - ft);
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rt.applyPairwiseTransform(pairwise::Multiply, *gradTanh, *temp1); // temp1 = rt * grad_tanh
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inGradHt.applyPairwiseTransform(pairwise::Multiply, *temp1, *temp1); // temp1 = inGradHt * rt * grad_tanh
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temp1->applyPairwiseTransform(pairwise::Add, *inGradCt, *temp1); // temp1 = inGradHt * rt * grad_tanh + inGradCt
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temp1->applyPairwiseTransform(pairwise::Multiply, *ftMinus, gradUZt); // gradUZt = (inGradHt * rt * grad_tanh + inGradCt) * (1.0f - ft);
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gradUFt.assign(&gradBFt);
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gradURt.assign(&gradBRt);
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// c_{t-1}, inGradCt = (gradCt + inGradCt) * ft;
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gradCt->applyPairwiseTransform(pairwise::Add, *inGradCt, *temp1); // temp1 = (gradCt + inGradCt)
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temp1->applyPairwiseTransform(pairwise::Multiply, ft, *inGradCt); // inGradCt = (gradCt + inGradCt) * ft;
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if(t != 0)
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delete ct_1;
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}
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// gradInit
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gradInit->assign(inGradCt);
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// gradX
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auto weightsT = w->transpose(); // [K x 3K]
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MmulHelper::mmul(&weightsT, gradU, gradX, 1., 0.); // [bS x K x N]
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gradX->applyPairwiseTransform(pairwise::Add, *gradHX, *gradX); // + grad_highway_x
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if(applyMask)
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gradX->applyBroadcast(broadcast::Multiply, {0,1}, *mask, *gradX); // apply mask
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// gradB
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auto temp3 = gradBias->reduceAlongDimension(reduce::Sum, {0,2}, false, true); // [1 x 2K]
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gradB->assign(temp3);
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// gradW [bS x 3K x K]
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x->permutei({0, 2, 1}); // [bS x N x K]
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MmulHelper::mmul(gradU, x, gradW, 1., 0.); // [bS x 3K x K]
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delete gct; delete gradU; delete gradHX;
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delete temp1; delete temp2; delete gradCt; delete wi;
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delete gradTanh; delete ftMinus; delete rtMinus; delete gradBias;
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return Status::OK();
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}
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DECLARE_TYPES(sru_bp) {
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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(sru_bp) {
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auto inShape = inputShape->at(0); // [bS x inSize x time]
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auto bS = inShape[1];
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auto inSize = inShape[2];
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auto time = inShape[3];
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char order = (char)(inShape[9]);
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ShapeDescriptor descriptor1(ArrayOptions::dataType(inShape), order, {bS, inSize, time});
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ShapeDescriptor descriptor2(ArrayOptions::dataType(inShape), order, {bS, 3 * inSize, inSize});
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ShapeDescriptor descriptor3(ArrayOptions::dataType(inShape), order, {1, 2 * inSize});
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ShapeDescriptor descriptor4(ArrayOptions::dataType(inShape), order, {bS, inSize});
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return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(descriptor1), ConstantShapeHelper::getInstance()->createShapeInfo(descriptor2), ConstantShapeHelper::getInstance()->createShapeInfo(descriptor3), ConstantShapeHelper::getInstance()->createShapeInfo(descriptor4));
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(sru_bi, 5, 2, true, 0, 0) {
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auto x = INPUT_VARIABLE(0); // X, input 3d tensor [time x bS x 2*inSize], time - number of time steps, bS - batch size, inSize - number of features
|
|
auto w = INPUT_VARIABLE(1); // W, 2d tensor of weights [2*inSize x 6*inSize]
|
|
auto b = INPUT_VARIABLE(2); // B, row of biases with twice length [1 x 4*inSize]
|
|
auto c0 = INPUT_VARIABLE(3); // C_{0}, 2d tensor of initial state [bS x 2*inSize] at time t=0
|
|
NDArray* mask = block.width() > 4 ? INPUT_VARIABLE(4) : nullptr; // optional, 2d tensor of dropout mask [bS x 2*inSize]
|
|
|
|
auto ht = OUTPUT_VARIABLE(0); // h_t, [time x bS x 2*inSize]
|
|
auto ct = OUTPUT_VARIABLE(1); // c_t, [time x bS x 2*inSize]
|
|
|
|
// input shapes validation
|
|
const int rank = x->rankOf();
|
|
const Nd4jLong bS = x->sizeAt(1);
|
|
const Nd4jLong inSize = x->sizeAt(2) / 2;
|
|
|
|
REQUIRE_TRUE(x->rankOf() == rank, 0, "SRU_BI operation: wrong rank of input array, expected is %i, but got %i instead !", rank, x->rankOf());
|
|
REQUIRE_TRUE(w->rankOf() == rank-1, 0, "SRU_BI operation: wrong rank of weights array, expected is %i, but got %i instead !", rank-1, w->rankOf());
|
|
REQUIRE_TRUE(b->rankOf() == 1, 0, "SRU_BI operation: wrong rank of biases array, expected is 1, but got %i instead !", b->rankOf());
|
|
REQUIRE_TRUE(c0->rankOf() == rank-1, 0, "SRU_BI operation: wrong rank of initial state array, expected is %i, but got %i instead !", rank-1, c0->rankOf());
|
|
if(mask)
|
|
REQUIRE_TRUE(mask->rankOf() == rank-1, 0, "SRU_BI operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, mask->rankOf());
|
|
|
|
const std::vector<Nd4jLong> wCorrectShape = {2*inSize, 6*inSize};
|
|
const std::vector<Nd4jLong> bCorrectShape = {4*inSize};
|
|
const std::vector<Nd4jLong> c0CorrectShape = {bS, 2*inSize};
|
|
|
|
REQUIRE_TRUE(w->isSameShape(wCorrectShape), 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(w).c_str());
|
|
REQUIRE_TRUE(b->isSameShape(bCorrectShape), 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(b).c_str());
|
|
REQUIRE_TRUE(c0->isSameShape(c0CorrectShape), 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0).c_str());
|
|
if(mask)
|
|
REQUIRE_TRUE(mask->isSameShape(c0CorrectShape), 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(mask).c_str());
|
|
|
|
helpers::sruBI(block.launchContext(), x, w, b, c0, mask, ht, ct);
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
DECLARE_TYPES(sru_bi) {
|
|
getOpDescriptor()
|
|
->setAllowedInputTypes(sd::DataType::ANY)
|
|
->setAllowedOutputTypes({ALL_FLOATS});
|
|
}
|
|
|
|
DECLARE_SHAPE_FN(sru_bi) {
|
|
|
|
auto xShapeInfo = inputShape->at(0); // [time x bS x 2K ]
|
|
auto wShapeInfo = inputShape->at(1);
|
|
auto bShapeInfo = inputShape->at(2);
|
|
auto c0ShapeInfo = inputShape->at(3);
|
|
Nd4jLong* maskShapeInfo = block.width() > 4 ? inputShape->at(4) : nullptr; // optional, 2d tensor of dropout mask [bS x inSize]
|
|
|
|
const int rank = xShapeInfo[0]; // = 3
|
|
const Nd4jLong time = xShapeInfo[1];
|
|
const Nd4jLong bS = xShapeInfo[2];
|
|
const Nd4jLong inSize = xShapeInfo[3] / 2;
|
|
|
|
|
|
// input shapes validation
|
|
REQUIRE_TRUE(wShapeInfo[0] == rank-1, 0, "SRU_BI operation: wrong rank of weights array, expected is %i, but got %i instead !", rank-1, wShapeInfo[0]);
|
|
REQUIRE_TRUE(bShapeInfo[0] == 1, 0, "SRU_BI operation: wrong rank of biases array, expected is 1, but got %i instead !", bShapeInfo[0]);
|
|
REQUIRE_TRUE(c0ShapeInfo[0] == rank-1, 0, "SRU_BI operation: wrong rank of initial state array, expected is %i, but got %i instead !", rank-1, c0ShapeInfo[0]);
|
|
if(maskShapeInfo)
|
|
REQUIRE_TRUE(maskShapeInfo[0] == rank-1, 0, "SRU_BI operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, maskShapeInfo[0]);
|
|
|
|
const std::vector<Nd4jLong> wCorrectShape = {2*inSize, 6*inSize};
|
|
const std::vector<Nd4jLong> bCorrectShape = {4*inSize};
|
|
const std::vector<Nd4jLong> c0CorrectShape = {bS, 2*inSize};
|
|
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(wShapeInfo, wCorrectShape), 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(wShapeInfo).c_str());
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, bCorrectShape), 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str());
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(c0ShapeInfo, c0CorrectShape), 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0ShapeInfo).c_str());
|
|
if(maskShapeInfo)
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(maskShapeInfo, c0CorrectShape), 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(maskShapeInfo).c_str());
|
|
|
|
|
|
char order = shape::order(xShapeInfo);
|
|
|
|
ShapeDescriptor descriptor(ArrayOptions::dataType(xShapeInfo), order, {time, bS, 2 * inSize});
|
|
auto result = ConstantShapeHelper::getInstance()->createShapeInfo(descriptor);
|
|
return SHAPELIST(result, result);
|
|
}
|
|
|
|
|
|
DECLARE_TYPES(sru_bi_bp) {
|
|
getOpDescriptor()
|
|
->setAllowedInputTypes(sd::DataType::ANY)
|
|
->setAllowedOutputTypes({ALL_FLOATS});
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
CUSTOM_OP_IMPL(sru_bi_bp, 8, 4, true, 0, 0) {
|
|
|
|
auto x = INPUT_VARIABLE(0); // X, input 3d tensor [time x bS x 2*inSize], time - number of time steps, bS - batch size, inSize - number of features
|
|
auto w = INPUT_VARIABLE(1); // W, 2d tensor of weights [2*inSize x 6*inSize]
|
|
auto b = INPUT_VARIABLE(2); // B, row of biases with twice length [4*inSize]
|
|
auto c0 = INPUT_VARIABLE(3); // C_{0}, 2d tensor of initial state [bS x 2*inSize] at time t=0
|
|
auto ct = INPUT_VARIABLE(4); // C, [time x bS x 2*inSize]
|
|
auto inGradC0 = INPUT_VARIABLE(5); // [bS x 2*inSize]
|
|
auto inGradHt = INPUT_VARIABLE(6); // [time x bS x 2*inSize]
|
|
NDArray* mask = block.width() > 7 ? INPUT_VARIABLE(7) : nullptr; // optional, 2d tensor of dropout mask [bS x 2*inSize]
|
|
|
|
// input shapes validation
|
|
const int rank = x->rankOf();
|
|
const Nd4jLong time = x->sizeAt(0);
|
|
const Nd4jLong bS = x->sizeAt(1);
|
|
const Nd4jLong inSize = x->sizeAt(2) / 2;
|
|
|
|
REQUIRE_TRUE(w->rankOf() == rank-1, 0, "SRU_BI_BP operation: wrong rank of weights array, expected is %i, but got %i instead !", rank-1, w->rankOf());
|
|
REQUIRE_TRUE(b->rankOf() == 1, 0, "SRU_BI_BP operation: wrong rank of biases array, expected is 1, but got %i instead !", b->rankOf());
|
|
REQUIRE_TRUE(c0->rankOf() == rank-1, 0, "SRU_BI_BP operation: wrong rank of initial state array, expected is %i, but got %i instead !", rank-1, c0->rankOf());
|
|
REQUIRE_TRUE(ct->rankOf() == rank, 0, "SRU_BI_BP operation: wrong rank of state array, expected is %i, but got %i instead !", rank, ct->rankOf());
|
|
REQUIRE_TRUE(inGradC0->rankOf() == rank-1, 0, "SRU_BI_BP operation: wrong rank of gradient c0, expected is %i, but got %i instead !", rank-1, inGradC0->rankOf());
|
|
REQUIRE_TRUE(inGradHt->rankOf() == rank, 0, "SRU_BI_BP operation: wrong rank of gradient ht, expected is %i, but got %i instead !", rank, inGradHt->rankOf());
|
|
if(mask)
|
|
REQUIRE_TRUE(mask->rankOf() == rank-1, 0, "SRU_BI_BP operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, mask->rankOf());
|
|
|
|
const std::vector<Nd4jLong> wCorrectShape = {2*inSize, 6*inSize};
|
|
const std::vector<Nd4jLong> bCorrectShape = {4*inSize};
|
|
const std::vector<Nd4jLong> c0CorrectShape = {bS, 2*inSize};
|
|
const std::vector<Nd4jLong> ctCorrectShape = {time, bS, 2*inSize};
|
|
|
|
REQUIRE_TRUE(w->isSameShape(wCorrectShape), 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(w).c_str());
|
|
REQUIRE_TRUE(b->isSameShape(bCorrectShape), 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(b).c_str());
|
|
REQUIRE_TRUE(c0->isSameShape(c0CorrectShape), 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0).c_str());
|
|
REQUIRE_TRUE(ct->isSameShape(ctCorrectShape), 0, "SRU_BI operation: wrong shape of state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(ctCorrectShape).c_str(), ShapeUtils::shapeAsString(ct).c_str());
|
|
if(mask)
|
|
REQUIRE_TRUE(mask->isSameShape(c0CorrectShape), 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(mask).c_str());
|
|
|
|
auto gradI = OUTPUT_VARIABLE(0); // [time x bS x 2*inSize]
|
|
auto gradW = OUTPUT_VARIABLE(1); // [time x 2*inSize x 6*inSize]
|
|
auto gradB = OUTPUT_VARIABLE(2); // [1 x 4*inSize]
|
|
auto gradC0 = OUTPUT_VARIABLE(3); // [bS x 2*inSize]
|
|
|
|
helpers::sruBIBP(block.launchContext(), x, w, b, c0, ct, inGradC0, inGradHt, mask, gradI, gradW, gradB, gradC0);
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
DECLARE_SHAPE_FN(sru_bi_bp) {
|
|
|
|
auto xShapeInfo = inputShape->at(0); // [time x bS x 2K ]
|
|
auto wShapeInfo = inputShape->at(1);
|
|
auto bShapeInfo = inputShape->at(2);
|
|
auto c0ShapeInfo = inputShape->at(3);
|
|
auto ctShapeInfo = inputShape->at(4);
|
|
auto inGradC0ShapeInfo = inputShape->at(5);
|
|
auto inGradHtShapeInfo = inputShape->at(6);
|
|
Nd4jLong* maskShapeInfo = block.width() > 7 ? inputShape->at(7) : nullptr; // optional, 2d tensor of dropout mask [bS x inSize]
|
|
|
|
// input shapes validation
|
|
const int rank = xShapeInfo[0];
|
|
const Nd4jLong time = xShapeInfo[1];
|
|
const Nd4jLong bS = xShapeInfo[2];
|
|
const Nd4jLong inSize = xShapeInfo[3] / 2;
|
|
|
|
REQUIRE_TRUE(wShapeInfo[0] == rank-1, 0, "SRU_BI_BP operation: wrong rank of weights array, expected is %i, but got %i instead !", rank-1, wShapeInfo[0]);
|
|
REQUIRE_TRUE(bShapeInfo[0] == 1, 0, "SRU_BI_BP operation: wrong rank of biases array, expected is 1, but got %i instead !", bShapeInfo[0]);
|
|
REQUIRE_TRUE(c0ShapeInfo[0] == rank-1, 0, "SRU_BI_BP operation: wrong rank of initial state array, expected is %i, but got %i instead !", rank-1, c0ShapeInfo);
|
|
REQUIRE_TRUE(ctShapeInfo[0] == rank, 0, "SRU_BI_BP operation: wrong rank of state array, expected is %i, but got %i instead !", rank, ctShapeInfo);
|
|
REQUIRE_TRUE(inGradC0ShapeInfo[0] == rank-1, 0, "SRU_BI_BP operation: wrong rank of gradient c0, expected is %i, but got %i instead !", rank-1, inGradC0ShapeInfo[0]);
|
|
REQUIRE_TRUE(inGradHtShapeInfo[0] == rank, 0, "SRU_BI_BP operation: wrong rank of gradient ht, expected is %i, but got %i instead !", rank, inGradHtShapeInfo[0]);
|
|
if(maskShapeInfo)
|
|
REQUIRE_TRUE(maskShapeInfo[0] == rank-1, 0, "SRU_BI_BP operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, maskShapeInfo[0]);
|
|
|
|
const std::vector<Nd4jLong> wCorrectShape = {2*inSize, 6*inSize};
|
|
const std::vector<Nd4jLong> bCorrectShape = {4*inSize};
|
|
const std::vector<Nd4jLong> c0CorrectShape = {bS, 2*inSize};
|
|
const std::vector<Nd4jLong> ctCorrectShape = {time, bS, 2*inSize};
|
|
const std::vector<Nd4jLong> inGradC0CorrectShape = {bS, 2*inSize};
|
|
const std::vector<Nd4jLong> inGradHtCorrectShape = {time, bS, 2*inSize};
|
|
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(wShapeInfo, wCorrectShape), 0, "SRU_BI operation: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(wCorrectShape).c_str(), ShapeUtils::shapeAsString(wShapeInfo).c_str());
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(bShapeInfo, bCorrectShape), 0, "SRU_BI operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(bCorrectShape).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str());
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(c0ShapeInfo, c0CorrectShape), 0, "SRU_BI operation: wrong shape of initial state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(c0ShapeInfo).c_str());
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(ctShapeInfo, ctCorrectShape), 0, "SRU_BI operation: wrong shape of state array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(ctCorrectShape).c_str(), ShapeUtils::shapeAsString(ctShapeInfo).c_str());
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(inGradC0ShapeInfo, inGradC0CorrectShape), 0, "SRU_BI operation: wrong shape of gradient c0 array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(inGradC0CorrectShape).c_str(), ShapeUtils::shapeAsString(inGradC0ShapeInfo).c_str());
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(inGradHtShapeInfo, inGradHtCorrectShape), 0, "SRU_BI operation: wrong shape of gradient ht array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(inGradHtCorrectShape).c_str(), ShapeUtils::shapeAsString(inGradHtShapeInfo).c_str());
|
|
if(maskShapeInfo)
|
|
REQUIRE_TRUE(ShapeUtils::areShapesEqual(maskShapeInfo, c0CorrectShape), 0, "SRU_BI operation: wrong shape of mask array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(c0CorrectShape).c_str(), ShapeUtils::shapeAsString(maskShapeInfo).c_str());
|
|
|
|
const char order = shape::order(xShapeInfo);
|
|
|
|
ShapeDescriptor descriptor1(ArrayOptions::dataType(xShapeInfo), order, {time, bS, 2 * inSize});
|
|
ShapeDescriptor descriptor2(ArrayOptions::dataType(xShapeInfo), order, {time, 2 * inSize, 6 * inSize});
|
|
ShapeDescriptor descriptor3(ArrayOptions::dataType(xShapeInfo), order, {4 * inSize});
|
|
ShapeDescriptor descriptor4(ArrayOptions::dataType(xShapeInfo), order, {bS, 2 * inSize});
|
|
|
|
return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(descriptor1), ConstantShapeHelper::getInstance()->createShapeInfo(descriptor2), ConstantShapeHelper::getInstance()->createShapeInfo(descriptor3), ConstantShapeHelper::getInstance()->createShapeInfo(descriptor4));
|
|
}
|
|
|
|
}
|
|
}
|
|
|
|
#endif
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
/**
|
|
* Implementation of operations for Simple Recurrent Unit: "Training RNNs as Fast as CNNs" Tao Lei, Yu Zhang, Yoav Artzi
|
|
*
|
|
* Input arrays:
|
|
* 0: input 3d tensor with shape [bS x K x N], N - number of time steps, bS - batch size, K - number of features
|
|
* 1: 2d tensor of weights [3K x K]
|
|
* 2: row of biases with twice length [1 x 2K]
|
|
* 3: 2d tensor of previous cell state [bS x K]
|
|
* 4: optional, 2d tensor of dropout mask [bS x K]
|
|
*
|
|
* Output arrays:
|
|
* 0: 3d tensor of cell output [bS x K x N]
|
|
* 1: 3d tensor of cell state [bS x K x N]
|
|
*/
|
|
// #if NOT_EXCLUDED(OP_sru)
|
|
// DECLARE_CUSTOM_OP(sru_old, 5, 2, false, 0, 0);
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
/**
|
|
* Implementation of operation for Simple Recurrent Unit: "Training RNNs as Fast as CNNs" Tao Lei, Yu Zhang, Yoav Artzi
|
|
*
|
|
* Input arrays:
|
|
* 0: input 3d tensor with shape [bS x K x N], N - number of time steps, bS - batch size, K - number of features
|
|
* 1: 2d tensor of weights [3K x K]
|
|
* 2: row of biases with twice length [1 x 2K]
|
|
* 3: 2d tensor of previous cell state [bS x K]
|
|
* 4: optional, 2d tensor of dropout mask [bS x K]
|
|
*
|
|
* Output arrays:
|
|
* 0: 3d tensor of cell output [bS x K x N]
|
|
* 1: 3d tensor of cell state [bS x K x N]
|
|
*/
|
|
// #if NOT_EXCLUDED(OP_sru_logic)
|
|
// DECLARE_CUSTOM_OP(sru_logic, 5, 2, false, 0, 0);
|
|
// #endif
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
/**
|
|
* Implementation of operation for back propagation in Simple Recurrent Unit: "Training RNNs as Fast as CNNs" Tao Lei, Yu Zhang, Yoav Artzi
|
|
*
|
|
* Input arrays:
|
|
* 0: input 3d tensor with shape [bS x K x N], N - number of time steps, bS - batch size, K - number of features
|
|
* 1: 2d tensor of weights [3K x K]
|
|
* 2: row of biases with twice length [1 x 2K]
|
|
* 3: 2d tensor of previous cell state [bS x K]
|
|
* 4: 3d tensor of cell state [bS x K x N]
|
|
* 5: 2d tensor of cell state gradients [bS x K]
|
|
* 6: 3d tensor of state output gradients [bS x K x N]
|
|
* 7: optional, 2d tensor of dropout mask [bS x K]
|
|
*
|
|
* Output arrays:
|
|
* 0: 3d tensor of input gradients [bS x K x N]
|
|
* 1: 3d tensor of weights gradients [bS x 3K x K]
|
|
* 2: 2d, row of biases gradients [1 x 2K]
|
|
* 3: 2d, tensor of state gradients [bS x K]
|
|
*/
|
|
// #if NOT_EXCLUDED(OP_sru_logic)
|
|
// DECLARE_CUSTOM_OP(sru_bp_logic,8, 4, true, 0, 0);
|
|
// #endif
|
|
|
|
// return 2d array evaluated though last dimension interval t1-t2
|
|
// static NDArray* timestep(const NDArray* arr, const int t1, const int t2) {
|
|
// NDArray* result = new NDArray((*arr)({0,0, 0,0, t1,t2}, true));
|
|
// result->reshapei(result->ordering(), {arr->shapeOf()[0], arr->shapeOf()[1]} );
|
|
|
|
// return result;
|
|
// }
|
|
|
|
/////////////////////////////////////////////////////////////////////////
|
|
// CUSTOM_OP_IMPL(sru_logic, 5, 2, false, 0, 0) {
|
|
|
|
// auto input = INPUT_VARIABLE(0); // X, input 3d tensor [bS x K x N], N - number of time steps, bS - batch size, K - number of features
|
|
// auto weights = INPUT_VARIABLE(1); // W, 2d tensor of weights [3K x K]
|
|
// auto bias = INPUT_VARIABLE(2); // B, row of biases with twice length [1 x 2*K]
|
|
// auto init = INPUT_VARIABLE(3); // C_{0}, 2d tensor of initial state [bS x K] at time t=0
|
|
// NDArray* mask = nullptr; // optional, 2d tensor of dropout mask [bS x K]
|
|
|
|
// bool applyMask = false;
|
|
// if (block.width() > 4) {
|
|
// mask = INPUT_VARIABLE(4);
|
|
// applyMask = true;
|
|
// }
|
|
|
|
// auto output = OUTPUT_VARIABLE(0); // h_t, [bS x K x N]
|
|
// auto state = OUTPUT_VARIABLE(1); // c_t, [bS x K x N]
|
|
|
|
// const int bS = input->shapeOf()[0]; // bS - batch size
|
|
// const int K = input->shapeOf()[1]; // K - number of features
|
|
// const int N = input->shapeOf()[2]; // N - number of time steps
|
|
|
|
// const auto wi = mmul(*weights, *input); // U [bS x 3K x N]
|
|
// const auto bF = (*bias)({0,0, 0, K}); // biases for forget gate [1 x K]
|
|
// const auto bR = (*bias)({0,0, K,2*K}); // biases for reset gate [1 x K]
|
|
|
|
// NDArray xt(input->dataType(), block.launchContext());
|
|
// NDArray zt(input->dataType(), block.launchContext());
|
|
// NDArray ft(input->dataType(), block.launchContext());
|
|
// NDArray rt(input->dataType(), block.launchContext());
|
|
// NDArray ht(input->dataType(), block.launchContext());
|
|
// NDArray ct = *init;
|
|
// NDArray gct(state->ordering(), {bS, K}, input->dataType(), block.launchContext());
|
|
// NDArray xmt = *input;
|
|
// // input = input * mask
|
|
// if(applyMask)
|
|
// xmt.applyBroadcast(broadcast::Multiply, {0, 1}, mask, &xmt, nullptr);
|
|
|
|
// for (int t = 0; t < N; ++t) {
|
|
// xt = xmt({0,0, 0,0, t,t+1}); xt.reshapei(xt.ordering(), {bS, K}); // [bS x K x N] -> [bS x K x 1] -> [bS x K]
|
|
// zt = wi({0,0, 0, K, t,t+1}); zt.reshapei(zt.ordering(), {bS, K}); // [bS x 3K x N] -> [bS x K x 1] -> [bS x K]
|
|
// ft = wi({0,0, K, 2*K, t,t+1}); ft.reshapei(ft.ordering(), {bS, K}); // [bS x 3K x N] -> [bS x K x 1] -> [bS x K]
|
|
// rt = wi({0,0, 2*K,3*K, t,t+1}); rt.reshapei(rt.ordering(), {bS, K}); // [bS x 3K x N] -> [bS x K x 1] -> [bS x K]
|
|
|
|
// ft = sigmoid_(ft + bF);
|
|
// rt = sigmoid_(rt + bR);
|
|
|
|
// ct = ft * (ct - zt) + zt;
|
|
// // TODO T val = (activation_type == 1) ? tanh(cur) : ((activation_type == 2) ? reluf(cur) : cur );
|
|
// ct.applyTransform(transform::Tanh, &gct);
|
|
// ht = rt * (gct - xt) + xt;
|
|
|
|
// // save results
|
|
// (*output)({0,0, 0,0, t,t+1}, true).assign(ht);
|
|
// (*state)({0,0, 0,0, t,t+1}, true).assign(ct);
|
|
// }
|
|
|
|
// return Status::OK();
|
|
// }
|
|
|
|
// DECLARE_TYPES(sru_logic) {
|
|
// getOpDescriptor()
|
|
// ->setAllowedInputTypes(sd::DataType::ANY)
|
|
// ->setAllowedOutputTypes({ALL_FLOATS});
|
|
// }
|
|
|
|
// DECLARE_SHAPE_FN(sru_logic) {
|
|
// auto inShape = inputShape->at(0); // [bS x K x N]
|
|
// int rank = inShape[0]; // = 3
|
|
// int size = rank*2 + 4;
|
|
// int bS = inShape[1];
|
|
// int K = inShape[2];
|
|
// int N = inShape[3];
|
|
// char order = (char)(inShape[size-1]);
|
|
|
|
// Nd4jLong* newShapeInfo1 = nullptr;
|
|
// ALLOCATE(newShapeInfo1, block.getWorkspace(), size, Nd4jLong);
|
|
|
|
// newShapeInfo1[0] = rank;
|
|
// newShapeInfo1[1] = bS;
|
|
// newShapeInfo1[2] = K;
|
|
// newShapeInfo1[3] = N;
|
|
|
|
// ShapeUtils::updateStridesAndType(newShapeInfo1, inShape, order);
|
|
// auto result = CONSTANT(newShapeInfo1);
|
|
// return SHAPELIST(result, result);
|
|
// }
|
|
|
|
|
|
// //////////////////////////////////////////////////////////////////////////
|
|
// CUSTOM_OP_IMPL(sru_old, 5, 2, false, 0, 0) {
|
|
// auto x = INPUT_VARIABLE(0); // X, input 3d tensor [bS x inSize x time], time - number of time steps, bS - batch size, inSize - number of features
|
|
// auto w = INPUT_VARIABLE(1); // W, 2d tensor of weights [3K x inSize]
|
|
// auto b = INPUT_VARIABLE(2); // B, row of biases with twice length [1 x 2*inSize]
|
|
// auto c0 = INPUT_VARIABLE(3); // C_{0}, 2d tensor of initial state [bS x inSize] at time t=0
|
|
// NDArray* mask = nullptr; // optional, 2d tensor of dropout mask [bS x inSize]
|
|
|
|
// bool applyMask = false;
|
|
// if (block.width() > 4) {
|
|
// mask = INPUT_VARIABLE(4);
|
|
// applyMask = true;
|
|
// }
|
|
|
|
// auto h = OUTPUT_VARIABLE(0); // h_t, [bS x inSize x time]
|
|
// auto state = OUTPUT_VARIABLE(1); // c_t, [bS x inSize x time]
|
|
|
|
// const int bS = x->shapeOf()[0]; // bS - batch size
|
|
// const int inSize = x->shapeOf()[1]; // inSize - number of features
|
|
// const int time = x->shapeOf()[2]; // time - number of time steps
|
|
|
|
// // multiplication matrix = matmul(w,x)
|
|
// auto wi = MmulHelper::mmul(w, x, nullptr, 1., 0.); // U [bS x 3K x time]
|
|
// auto wiZ = (*wi)({0,0, 0,inSize, 0,0}, true); // [bS x inSize x time]
|
|
// auto wiF = (*wi)({0,0, inSize,2*inSize, 0,0}, true); // forget gate [bS x inSize x time]
|
|
// auto wiR = (*wi)({0,0, 2*inSize,3*inSize, 0,0}, true); // reset gate [bS x inSize x time]
|
|
// auto bF = (*b) ({0,0, 0,inSize }, true); // biases for forget gate [1 x inSize]
|
|
// auto bR = (*b) ({0,0, inSize,2*inSize}, true); // biases for reset gate [1 x inSize]
|
|
|
|
// NDArray* xt(nullptr), *zt(nullptr), *ft(nullptr), *rt(nullptr), *ct(nullptr), *ht(nullptr);
|
|
// auto ct_1 = c0->dup(c0->ordering());
|
|
// auto gct = NDArrayFactory::create_(state->ordering(), {bS, inSize}, state->dataType(), state->getContext());
|
|
// auto xmt = x->dup(x->ordering());
|
|
// // x = x * mask
|
|
// if(applyMask)
|
|
// xmt->applyBroadcast(broadcast::Multiply, {0, 1}, mask, xmt, nullptr); // apply mask
|
|
|
|
// for (int t = 0; t < time; ++t) {
|
|
// xt = timestep(xmt, t, t+1); // [bS x inSize x time] -> [bS x inSize x 1] -> [bS x inSize]
|
|
// zt = timestep(&wiZ, t, t+1); // [bS x inSize x time] -> [bS x inSize x 1] -> [bS x inSize]
|
|
// ft = timestep(&wiF, t, t+1); // [bS x inSize x time] -> [bS x inSize x 1] -> [bS x inSize]
|
|
// rt = timestep(&wiR, t, t+1); // [bS x inSize x time] -> [bS x inSize x 1] -> [bS x inSize]
|
|
// ct = timestep(state, t, t+1); // [bS x inSize x time] -> [bS x inSize x 1] -> [bS x inSize]
|
|
// ht = timestep(h, t, t+1); // [bS x inSize x time] -> [bS x inSize x 1] -> [bS x inSize]
|
|
|
|
// // ft = sigmoid(ft + bf), rt = sigmoid(rt + bR)
|
|
// ft->addRowVector(&bF, ft);
|
|
// rt->addRowVector(&bR, rt);
|
|
// ft->applyTransform(transform::Sigmoid, ft, nullptr);
|
|
// rt->applyTransform(transform::Sigmoid, rt, nullptr);
|
|
// // ct = ft * c_t-1 + (1 - ft) * zt,
|
|
// ft->applyPairwiseTransform(pairwise::Multiply, ct_1, ct, nullptr);
|
|
// ft->applyTransform(transform::OneMinus, ft);
|
|
// ft->applyPairwiseTransform(pairwise::Multiply, *zt, nullptr);
|
|
// ct->applyPairwiseTransform(pairwise::Add, *ft, nullptr);
|
|
// // TODO T val = (activation_type == 1) ? tanh(cur) : ((activation_type == 2) ? reluf(cur) : cur );
|
|
// ct->applyTransform(transform::Tanh, gct);
|
|
|
|
// // ht = rt * gct + (1 - rt) * xt
|
|
// rt->applyPairwiseTransform(pairwise::Multiply, gct, ht, nullptr);
|
|
// rt->applyTransform(transform::OneMinus, rt);
|
|
// rt->applyPairwiseTransform(pairwise::Multiply, *xt, nullptr);
|
|
// ht->applyPairwiseTransform(pairwise::Add, *rt, nullptr);
|
|
|
|
// delete xt; delete zt; delete ft; delete rt; delete ht; delete ct_1;
|
|
// ct_1 = ct;
|
|
// }
|
|
|
|
// delete wi; delete ct_1; delete gct; delete xmt;
|
|
|
|
// return Status::OK();
|
|
// }
|
|
|
|
// DECLARE_TYPES(sru_old) {
|
|
// getOpDescriptor()
|
|
// ->setAllowedInputTypes(sd::DataType::ANY)
|
|
// ->setAllowedOutputTypes({ALL_FLOATS});
|
|
// }
|
|
|
|
// DECLARE_SHAPE_FN(sru_old) {
|
|
// auto inShape = inputShape->at(0); // [bS x inSize x time]
|
|
// int rank = inShape[0]; // = 3
|
|
// int size = rank*2 + 4;
|
|
// auto bS = inShape[1];
|
|
// auto inSize = inShape[2];
|
|
// int time = inShape[3];
|
|
// char order = (char)(inShape[size-1]);
|
|
|
|
// Nd4jLong *newShapeInfo1 = nullptr;
|
|
// ALLOCATE(newShapeInfo1, block.getWorkspace(), size, Nd4jLong);
|
|
|
|
// newShapeInfo1[0] = rank;
|
|
// newShapeInfo1[1] = bS;
|
|
// newShapeInfo1[2] = inSize;
|
|
// newShapeInfo1[3] = time;
|
|
|
|
// ShapeUtils::updateStridesAndType(newShapeInfo1, inShape, order);
|
|
|
|
// auto result = ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(newShapeInfo1));
|
|
// RELEASE(newShapeInfo1, block.getWorkspace());
|
|
// return SHAPELIST(result, result);
|
|
// }
|
|
|
|
// static NDArray sigmoid_(const NDArray& arr) {
|
|
// NDArray result(arr.getShapeInfo(), false, arr.getContext());
|
|
// (const_cast<NDArray&>(arr)).applyTransform(transform::Sigmoid, &result);
|
|
// return result;
|
|
// }
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// CUSTOM_OP_IMPL(sru_bp_logic, 8, 4, true, 0, 0) {
|
|
|
|
// auto x = INPUT_VARIABLE(0); // X, input 3d tensor [bS x inSize x time], time - number of time steps, bS - batch size, inSize - number of features
|
|
// auto w = INPUT_VARIABLE(1); // W, 2d tensor of weights [3*inSize x inSize]
|
|
// auto b = INPUT_VARIABLE(2); // B, row of biases with twice length [1 x 2*inSize]
|
|
// auto c0 = INPUT_VARIABLE(3); // C_{0}, 2d tensor of initial state [bS x inSize] at time t=0
|
|
// auto c = INPUT_VARIABLE(4); // C, [bS x inSize x time]
|
|
// auto inGradCt = INPUT_VARIABLE(5); // [bS x inSize]
|
|
// auto inGradH = INPUT_VARIABLE(6); // [bS x inSize x time]
|
|
// auto mask = block.width() > 7 ? INPUT_VARIABLE(7) : nullptr; // optional, 2d tensor of dropout mask [bS x inSize]
|
|
|
|
// auto gradX = OUTPUT_VARIABLE(0); // [bS x inSize x time]
|
|
// auto gradW = OUTPUT_VARIABLE(1); // [bS x 3*inSize x inSize]
|
|
// auto gradB = OUTPUT_VARIABLE(2); // [2*inSize]
|
|
// auto gradInit = OUTPUT_VARIABLE(3); // [bS x inSize]
|
|
|
|
// // input shapes validation
|
|
// const int rank = 3;
|
|
// REQUIRE_TRUE(x->rankOf() == rank, 0, "SRU_BP operation: wrong rank of input array, expected is %i, but got %i instead !", rank, x->rankOf());
|
|
// REQUIRE_TRUE(w->rankOf() == rank-1, 0, "SRU_BP operation: wrong rank of weights array, expected is %i, but got %i instead !", rank-1, w->rankOf());
|
|
// REQUIRE_TRUE(b->rankOf() <= 2, 0, "SRU_BP operation: wrong rank of biases array, expected is <=2, but got %i instead !", b->rankOf());
|
|
// REQUIRE_TRUE(c0->rankOf() == rank-1, 0, "SRU_BP operation: wrong rank of initial state array, expected is %i, but got %i instead !", rank-1, c0->rankOf());
|
|
// REQUIRE_TRUE(c->rankOf() == rank, 0, "SRU_BP operation: wrong rank of cell states array, expected is %i, but got %i instead !", rank, c->rankOf());
|
|
// REQUIRE_TRUE(inGradCt->rankOf() == rank-1, 0, "SRU_BP operation: wrong rank of array of cell state gradient, expected is %i, but got %i instead !", rank-1, inGradCt->rankOf());
|
|
// REQUIRE_TRUE(inGradH->rankOf() == rank, 0, "SRU_BP operation: wrong rank of array of cell outputs gradients, expected is %i, but got %i instead !", rank, inGradH->rankOf());
|
|
// if(mask)
|
|
// REQUIRE_TRUE(mask->rankOf() == rank-1, 0, "SRU_BP operation: wrong rank of mask array, expected is %i, but got %i instead !", rank-1, mask->rankOf());
|
|
|
|
// const int bS = x->shapeOf()[0];
|
|
// const int inSize = x->shapeOf()[1];
|
|
// const int time = x->shapeOf()[2]; // time - number of time steps
|
|
|
|
// const std::string wShape = ShapeUtils::shapeAsString(w);
|
|
// const std::string wCorrectShape = ShapeUtils::shapeAsString({3*inSize, inSize});
|
|
// // const std::string bShape = ShapeUtils::shapeAsString(b);
|
|
// // const std::string bCorrectShape = ShapeUtils::shapeAsString({2*inSize});
|
|
// const std::string c0Shape = ShapeUtils::shapeAsString(c0);
|
|
// const std::string c0CorrectShape = ShapeUtils::shapeAsString({bS, inSize});
|
|
// const std::string cShape = ShapeUtils::shapeAsString(c);
|
|
// const std::string cCorrectShape = ShapeUtils::shapeAsString({bS, inSize, time});
|
|
// const std::string inGradCtShape = ShapeUtils::shapeAsString(inGradCt);
|
|
// const std::string inGradCtCorrectShape = ShapeUtils::shapeAsString({bS, inSize});
|
|
// const std::string inGradHShape = ShapeUtils::shapeAsString(inGradH);
|
|
// const std::string inGradHCorrectShape = ShapeUtils::shapeAsString({bS, inSize, time});
|
|
|
|
// REQUIRE_TRUE(wShape == wCorrectShape, 0, "SRU_BP operation: wrong shape of weights array, expected is %s, but got %s instead !", wCorrectShape.c_str(), wShape.c_str());
|
|
// // REQUIRE_TRUE(bShape == bCorrectShape, 0, "SRU_BP operation: wrong shape of biases array, expected is %s, but got %s instead !", bCorrectShape.c_str(), bShape.c_str());
|
|
// REQUIRE_TRUE(c0Shape == c0CorrectShape, 0, "SRU_BP operation: wrong shape of initial state array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), c0Shape.c_str());
|
|
// REQUIRE_TRUE(cShape == cCorrectShape, 0, "SRU_BP operation: wrong shape of cell states array, expected is %s, but got %s instead !", cCorrectShape.c_str(), cShape.c_str());
|
|
// REQUIRE_TRUE(inGradCtShape == inGradCtCorrectShape, 0, "SRU_BP operation: wrong shape of array of cell state gradient, expected is %s, but got %s instead !", inGradCtCorrectShape.c_str(), inGradCtShape.c_str());
|
|
// REQUIRE_TRUE(inGradHShape == inGradHCorrectShape, 0, "SRU_BP operation: wrong shape of array of cell outputs gradients, expected is %s, but got %s instead !", inGradHCorrectShape.c_str(), inGradHShape.c_str());
|
|
// if(mask) {
|
|
// const std::string maskShape = ShapeUtils::shapeAsString(mask);
|
|
// REQUIRE_TRUE(maskShape == c0CorrectShape, 0, "SRU_BP operation: wrong shape of mask array, expected is %s, but got %s instead !", c0CorrectShape.c_str(), maskShape.c_str());
|
|
// }
|
|
|
|
|
|
// const auto bF = (*b)({0,0, 0, inSize}); // biases for forget gate [1 x inSize]
|
|
// const auto bR = (*b)({0,0, inSize,2*inSize}); // biases for reset gate [1 x inSize]
|
|
// NDArray gradBias(x->ordering(), {bS, 2*inSize, time}, x->dataType(), block.launchContext());
|
|
// NDArray gradU (x->ordering(), {bS, 3*inSize, time}, x->dataType(), block.launchContext());
|
|
// NDArray gradHX (x->ordering(), {bS, inSize, time}, x->dataType(), block.launchContext());
|
|
// NDArray gct (c->ordering(), {bS, inSize}, x->dataType(), block.launchContext());
|
|
|
|
// // x = x * mask
|
|
// if(mask)
|
|
// x->applyBroadcast(broadcast::Multiply, {0, 1}, mask, x, nullptr); // apply mask
|
|
// // multiplication matrix wi = matmul(w,x), U = WX
|
|
// const auto wi = mmul(*w, *x); // U [bS x 3K x time]
|
|
|
|
// for (int t = time-1; t >=0 ; --t) {
|
|
// // initialization
|
|
// auto xt = (*x)({0,0, 0,0, t,t+1}); // [bS x inSize x time] -> [bS x inSize]
|
|
// auto zt = wi({0,0, 0, inSize, t,t+1}); // [bS x 3K x time] -> [bS x inSize]
|
|
// auto ft = wi({0,0, inSize, 2*inSize, t,t+1}); // [bS x 3K x time] -> [bS x inSize]
|
|
// auto rt = wi({0,0, 2*inSize,3*inSize, t,t+1}); // [bS x 3K x time] -> [bS x inSize]
|
|
// auto ct = (*c)({0,0, 0,0, t,t+1}); // [bS x inSize x time] -> [bS x inSize]
|
|
// auto inGradHt = (*inGradH)({ 0,0, 0,0, t,t+1}); // [bS x inSize x time] -> [bS x inSize]
|
|
|
|
// auto ct_1 = t ? (*c)({ 0,0, 0,0, t-1,t}) : *c0; // previous c_{t-1}
|
|
|
|
// ///////////////// forward
|
|
// // ft = sigmoid(ft + bf), rt = sigmoid(rt + bR)
|
|
// ft = sigmoid_(ft + bF);
|
|
// rt = sigmoid_(rt + bR);
|
|
// // TODO T val = (activation_type == 1) ? tanh(cur) : ((activation_type == 2) ? reluf(cur) : cur );
|
|
// ct.applyTransform(transform::Tanh, &gct);
|
|
|
|
// ///////////////// backward
|
|
// // bR, *grad_brt_ptr = inGradHt * (g_ct - xt) * (1.0f - rt) * rt;
|
|
// // ftMinus = -ft + (T)1.;
|
|
// NDArray ftMinus = 1. - ft;
|
|
// NDArray rtMinus = 1. - rt;
|
|
// NDArray gradBRt = inGradHt * (gct - xt) * rtMinus * rt;
|
|
// // bF, TODO - tanh
|
|
// NDArray gradTanh = 1. - gct * gct;
|
|
// NDArray gradCt = inGradHt * rt * gradTanh;
|
|
// NDArray gradBFt = (gradCt + *inGradCt) * (ct_1 - zt) * ftMinus * ft;
|
|
// // x_t (highway connection), gradHXt = inGradHt * (1.0f - rt);
|
|
// NDArray gradHXt = inGradHt * rtMinus;
|
|
|
|
// // U_t, gradUZt = (inGradHt * rt * grad_tanh + inGradCt) * (1.0f - ft);
|
|
// NDArray gradUZt = (inGradHt * rt * gradTanh + *inGradCt) * ftMinus;
|
|
|
|
// // c_{t-1}, inGradCt = (gradCt + inGradCt) * ft;
|
|
// *inGradCt = (gradCt + *inGradCt) * ft;
|
|
|
|
// // save results
|
|
// gradBias({0,0, 0,inSize, t,t+1}, true).assign(gradBFt);
|
|
// gradBias({0,0, inSize,2*inSize, t,t+1}, true).assign(gradBRt);
|
|
// gradU({0,0, 0,inSize, t,t+1}, true).assign(gradUZt);
|
|
// gradU({0,0, inSize,2*inSize, t,t+1}, true).assign(gradBFt);
|
|
// gradU({0,0, 2*inSize, 3*inSize, t,t+1}, true).assign(gradBRt);
|
|
// gradHX({0,0, 0,0, t,t+1}, true).assign(gradHXt);
|
|
// }
|
|
|
|
// // gradInit
|
|
// gradInit->assign(inGradCt);
|
|
// // gradX
|
|
// w->transposei(); // [inSize x 3K]
|
|
// gradX->assign( mmul(*w, gradU) + gradHX);
|
|
// if(mask)
|
|
// gradX->applyBroadcast(broadcast::Multiply, {0,1}, mask, gradX, nullptr); // apply mask
|
|
|
|
// // gradB
|
|
// gradBias.reduceAlongDimension(reduce::Sum, *gradB, {0,2}, false, true); // [1 x 2K]
|
|
|
|
// // gradW [bS x 3K x inSize]
|
|
// x->permutei({0, 2, 1}); // [bS x time x inSize]
|
|
// gradW->assign( mmul(gradU, *x) );
|
|
|
|
// return Status::OK();
|
|
// }
|
|
|
|
// DECLARE_TYPES(sru_bp_logic) {
|
|
// getOpDescriptor()
|
|
// ->setAllowedInputTypes(sd::DataType::ANY)
|
|
// ->setAllowedOutputTypes({ALL_FLOATS});
|
|
// }
|
|
|
|
// DECLARE_SHAPE_FN(sru_bp_logic) {
|
|
// auto inShape = inputShape->at(0); // [bS x inSize x time]
|
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// auto bS = inShape[1];
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// auto inSize = inShape[2];
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// auto time = inShape[3];
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// char order = shape::order(inShape);
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// ShapeDescriptor descriptor1(ArrayOptions::dataType(inShape), order, {bS, inSize, time});
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// ShapeDescriptor descriptor2(ArrayOptions::dataType(inShape), order, {bS, 3 * inSize, inSize});
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// ShapeDescriptor descriptor3(ArrayOptions::dataType(inShape), order, {1, 2 * inSize});
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// ShapeDescriptor descriptor4(ArrayOptions::dataType(inShape), order, {bS, inSize});
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// return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(descriptor1), ConstantShapeHelper::getInstance()->createShapeInfo(descriptor2), ConstantShapeHelper::getInstance()->createShapeInfo(descriptor3), ConstantShapeHelper::getInstance()->createShapeInfo(descriptor4));
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// }
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