/* ****************************************************************************** * * * 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. * * See the NOTICE file distributed with this work for additional * information regarding copyright ownership. * 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 ******************************************************************************/ // // implementation of operations for Simple Recurrent Unit: arXiv:1709.02755v2 [cs.CL] 12 Sep 2017 // //@author Yurii Shyrma // #include #if NOT_EXCLUDED(OP_sru) #include #include #include #include namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(sru, 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 [3*inSize x inSize] auto b = INPUT_VARIABLE(2); // B, row of biases with twice length [2*inSize] auto c0 = INPUT_VARIABLE(3); // C_{0}, 2d tensor of initial state [bS x inSize] at time t=0 auto mask = block.width() > 4 ? INPUT_VARIABLE(4) : nullptr; // optional, 2d tensor of dropout mask [bS x inSize] auto h = OUTPUT_VARIABLE(0); // cell outputs, [bS x inSize x time] auto c = OUTPUT_VARIABLE(1); // cell states, [bS x inSize x time] const int rank = x->rankOf(); // = 3 const auto bS = x->sizeAt(0); const auto inSize = x->sizeAt(1); const auto time = x->sizeAt(2); // input shapes validation 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()); REQUIRE_TRUE(b->rankOf() == 1, 0, "SRU operation: wrong rank of biases array, expected is %i, but got %i instead !", 1, b->rankOf()); 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()); if(mask) 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()); const std::vector wCorrectShape = {3*inSize, inSize}; const std::vector bCorrectShape = {2*inSize}; const std::vector c0CorrectShape = {bS, inSize}; 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()); 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()); 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()); if(mask) 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()); // xm = x * mask auto xm = x; if(mask) { xm = new NDArray(x->shapeInfo(), true, block.launchContext()); x->applyBroadcast(broadcast::Multiply, {0, 1}, *mask, *xm); } // time loop helpers::sruTimeLoop(block.launchContext(), xm, c0, w, b, h, c); if(mask) delete xm; return Status::OK(); } DECLARE_TYPES(sru) { getOpDescriptor() ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(sru) { 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 auto wShapeInfo = inputShape->at(1); // W, 2d tensor of weights [3*inSize x inSize] auto bShapeInfo = inputShape->at(2); // B, row of biases with twice length [2*inSize] auto c0ShapeInfo = inputShape->at(3); // C_{0}, 2d tensor of initial state [bS x inSize] at time t=0 auto maskShapeInfo = block.width() > 4 ? inputShape->at(4) : nullptr; // optional, 2d tensor of dropout mask [bS x inSize] const int rank = xShapeInfo[0]; // = 3 const int bS = xShapeInfo[1]; const int inSize = xShapeInfo[2]; const int time = xShapeInfo[3]; // input shapes validation 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]); REQUIRE_TRUE(bShapeInfo[0] == 1, 0, "SRU operation: wrong rank of biases array, expected is %i, but got %i instead !", 1, bShapeInfo[0]); 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]); if(maskShapeInfo) 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]); const std::vector wCorrectShape = {3*inSize, inSize}; const std::vector bCorrectShape = {2*inSize}; const std::vector c0CorrectShape = {bS, inSize}; 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()); 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()); 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()); if(maskShapeInfo) 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()); Nd4jLong* newShapeInfo1 = nullptr; ALLOCATE(newShapeInfo1, block.getWorkspace(), shape::shapeInfoLength(rank), Nd4jLong); // [bS x inSize x time] newShapeInfo1[0] = rank; newShapeInfo1[1] = bS; newShapeInfo1[2] = inSize; newShapeInfo1[3] = time; ShapeUtils::updateStridesAndType(newShapeInfo1, xShapeInfo, shape::order(xShapeInfo)); ShapeDescriptor descriptor(newShapeInfo1); RELEASE(newShapeInfo1, block.getWorkspace()); auto result = ConstantShapeHelper::getInstance().createShapeInfo(descriptor); return SHAPELIST(result, result); } ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(sru_bp, 8, 4, true, 0, 0) { 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 auto w = INPUT_VARIABLE(1); // W, 2d tensor of weights [3K x K] auto b = INPUT_VARIABLE(2); // B, row of biases with twice length [1 x 2*K] auto c0 = INPUT_VARIABLE(3); // C_{0}, 2d tensor of initial state [bS x K] at time t=0 auto c = INPUT_VARIABLE(4); // C, [bS x K x N] auto inGradCt = INPUT_VARIABLE(5); // [bS x K] auto inGradH = INPUT_VARIABLE(6); // [bS x K x N] NDArray* mask = nullptr; // optional, 2d tensor of dropout mask [bS x K] bool applyMask = false; if (block.width() > 7) { mask = INPUT_VARIABLE(7); applyMask = true; } auto gradX = OUTPUT_VARIABLE(0); // [bS x K x N] auto gradW = OUTPUT_VARIABLE(1); // [bS x 3K x K] auto gradB = OUTPUT_VARIABLE(2); // [1 x 2K] auto gradInit = OUTPUT_VARIABLE(3); // [bS x K] const int bS = x->shapeOf()[0]; const int K = x->shapeOf()[1]; const int N = x->shapeOf()[2]; // N - number of time steps auto gradBias = NDArrayFactory::create_(x->ordering(), {bS, 2*K, N}, gradX->dataType(), block.launchContext()); auto gradU = NDArrayFactory::create_(x->ordering(), {bS, 3*K, N}, gradX->dataType(), block.launchContext()); auto gradHX = NDArrayFactory::create_(x->ordering(), {bS, K, N}, gradX->dataType(), block.launchContext()); auto gct = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext()); auto gradTanh = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext()); auto gradCt = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext()); auto ftMinus = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext()); auto rtMinus = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext()); auto temp1 = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext()); auto temp2 = NDArrayFactory::create_(c->ordering(), {bS, K}, gradX->dataType(), block.launchContext()); // x = x * mask if(applyMask) x->applyBroadcast(broadcast::Multiply, {0, 1}, *mask, *x); // apply mask // multiplication matrix wi = matmul(w,x), U = WX auto wi = MmulHelper::mmul(w, x, nullptr, 1., 0.); // U [bS x 3K x N] auto wiZ = (*wi)({0,0, 0,K, 0,0}, true); // [bS x K x N] auto wiF = (*wi)({0,0, K,2*K, 0,0}, true); // forget gate [bS x K x N] auto wiR = (*wi)({0,0, 2*K,3*K, 0,0}, true); // reset gate [bS x K x N] auto bF = (*b) ({0,0, 0,K }, true); // biases for forget gate [1 x K] auto bR = (*b) ({0,0, K,2*K}, true); // biases for reset gate [1 x K] auto gradBF = (*gradBias)({0,0, 0,K, 0,0}, true); // [bS x K x N] auto gradBR = (*gradBias)({0,0, K,2*K, 0,0}, true); // [bS x K x N] auto gradUZ = (*gradU) ({0,0, 0,K, 0,0}, true ); // [bS x K x N] auto gradUF = (*gradU) ({0,0, K,2*K, 0,0}, true ); // [bS x K x N] auto gradUR = (*gradU) ({0,0, 2*K,3*K, 0,0}, true ); // [bS x K x N] NDArray* ct_1 = nullptr; std::vector idx = {0,0, 0,0, 0,0}; for (int t = N-1; t >=0 ; --t) { // initialization idx[4] = t; idx[5] = t + 1; auto xt = (*x)(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] auto zt = wiZ(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] auto ft = wiF(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] auto rt = wiR(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] auto ct = (*c)(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] auto inGradHt = (*inGradH)(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] auto gradBRt = gradBR(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] auto gradBFt = gradBF(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] auto gradHXt = (*gradHX)(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] auto gradUZt = gradUZ(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] auto gradUFt = gradUF(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] auto gradURt = gradUR(idx); // [bS x K x N] -> [bS x K x 1] -> [bS x K] if(t != 0) { idx[4] = t - 1; idx[5] = t; ct_1 = new NDArray((*c)(idx)); // previous c_{t-1} } else ct_1 = c0; ///////////////// forward // ft = sigmoid(ft + bf), rt = sigmoid(rt + bR) ft.addRowVector(bF, ft); rt.addRowVector(bR, rt); ft.applyTransform(transform::Sigmoid, ft); rt.applyTransform(transform::Sigmoid, rt); // TODO T val = (activation_type == 1) ? tanh(cur) : ((activation_type == 2) ? reluf(cur) : cur ); ct.applyTransform(transform::Tanh, *gct); // ftMinus = 1-ft, rtMinus = 1-rt ft.applyTransform(transform::OneMinus, *ftMinus); rt.applyTransform(transform::OneMinus, *rtMinus); ///////////////// backward // bR, *grad_brt_ptr = inGradHt * (g_ct - xt) * (1.0f - rt) * rt; gct->applyPairwiseTransform(pairwise::Subtract, xt, *temp1); // temp1 = (g_ct - xt) rtMinus->applyPairwiseTransform(pairwise::Multiply, rt, *temp2); // temp2 = (1.0f - rt) * rt; temp1->applyPairwiseTransform(pairwise::Multiply, *temp2); // temp1 = (g_ct - xt) * (1.0f - rt) * rt; inGradHt.applyPairwiseTransform(pairwise::Multiply, *temp1, gradBRt); // = inGradHt * (g_ct - xt) * (1.0f - rt) * rt; // bF, TODO - tanh // gradTanh = (1.0f - g_ct * g_ct); gct->applyPairwiseTransform(pairwise::Multiply, *gct, *gradTanh); // gradTanh = g_ct * g_ct gradTanh->applyTransform(transform::OneMinus, *gradTanh); // gradTanh = (1.0f - g_ct * g_ct) // gradCt = inGradHt * rt * gradTanh rt.applyPairwiseTransform(pairwise::Multiply, *gradTanh, *gradCt); // gradCt = rt * gradTanh inGradHt.applyPairwiseTransform(pairwise::Multiply, *gradCt, *gradCt); // gradCt = inGradHt * rt * gradTanh // gradBFt = (gradCt + inGradCt) * (ct_1 - zt) * (1 - ft) * ft; gradCt->applyPairwiseTransform(pairwise::Add, *inGradCt, *temp1); // temp1 = (gradCt + inGradCt) ct_1->applyPairwiseTransform(pairwise::Subtract, zt, *temp2); // temp2 = (ct_1 - zt) temp1->applyPairwiseTransform(pairwise::Multiply, *ftMinus, *temp1); // temp1 = (gradCt + inGradCt)*(1-ft) temp1->applyPairwiseTransform(pairwise::Multiply, ft, *temp1); // temp1 = (gradCt + inGradCt)*(1-ft)*ft temp1->applyPairwiseTransform(pairwise::Multiply, *temp2, gradBFt); // gradBFt = (gradCt + inGradCt) * (ct_1 - zt) * (1 - ft) * ft; // x_t (highway connection), gradHXt = inGradHt * (1.0f - rt); inGradHt.applyPairwiseTransform(pairwise::Multiply, *rtMinus, gradHXt); // U_t, gradUZt = (inGradHt * rt * grad_tanh + inGradCt) * (1.0f - ft); rt.applyPairwiseTransform(pairwise::Multiply, *gradTanh, *temp1); // temp1 = rt * grad_tanh inGradHt.applyPairwiseTransform(pairwise::Multiply, *temp1, *temp1); // temp1 = inGradHt * rt * grad_tanh temp1->applyPairwiseTransform(pairwise::Add, *inGradCt, *temp1); // temp1 = inGradHt * rt * grad_tanh + inGradCt temp1->applyPairwiseTransform(pairwise::Multiply, *ftMinus, gradUZt); // gradUZt = (inGradHt * rt * grad_tanh + inGradCt) * (1.0f - ft); gradUFt.assign(&gradBFt); gradURt.assign(&gradBRt); // c_{t-1}, inGradCt = (gradCt + inGradCt) * ft; gradCt->applyPairwiseTransform(pairwise::Add, *inGradCt, *temp1); // temp1 = (gradCt + inGradCt) temp1->applyPairwiseTransform(pairwise::Multiply, ft, *inGradCt); // inGradCt = (gradCt + inGradCt) * ft; if(t != 0) delete ct_1; } // gradInit gradInit->assign(inGradCt); // gradX auto weightsT = w->transpose(); // [K x 3K] MmulHelper::mmul(&weightsT, gradU, gradX, 1., 0.); // [bS x K x N] gradX->applyPairwiseTransform(pairwise::Add, *gradHX, *gradX); // + grad_highway_x if(applyMask) gradX->applyBroadcast(broadcast::Multiply, {0,1}, *mask, *gradX); // apply mask // gradB auto gradB2 = gradB->reshape(gradB->ordering(), {2*K}); gradBias->reduceAlongDimension(reduce::Sum, gradB2, {0,2}); // [1 x 2K] // gradW [bS x 3K x K] x->permutei({0, 2, 1}); // [bS x N x K] MmulHelper::mmul(gradU, x, gradW, 1., 0.); // [bS x 3K x K] delete gct; delete gradU; delete gradHX; delete temp1; delete temp2; delete gradCt; delete wi; delete gradTanh; delete ftMinus; delete rtMinus; delete gradBias; return Status::OK(); } DECLARE_TYPES(sru_bp) { getOpDescriptor() ->setAllowedInputTypes(sd::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}); } DECLARE_SHAPE_FN(sru_bp) { auto inShape = inputShape->at(0); // [bS x inSize x time] auto bS = inShape[1]; auto inSize = inShape[2]; auto time = inShape[3]; char order = (char)(inShape[9]); ShapeDescriptor descriptor1(ArrayOptions::dataType(inShape), order, {bS, inSize, time}); ShapeDescriptor descriptor2(ArrayOptions::dataType(inShape), order, {bS, 3 * inSize, inSize}); ShapeDescriptor descriptor3(ArrayOptions::dataType(inShape), order, {1, 2 * inSize}); ShapeDescriptor descriptor4(ArrayOptions::dataType(inShape), order, {bS, inSize}); return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(descriptor1), ConstantShapeHelper::getInstance().createShapeInfo(descriptor2), ConstantShapeHelper::getInstance().createShapeInfo(descriptor3), ConstantShapeHelper::getInstance().createShapeInfo(descriptor4)); } ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(sru_bi, 5, 2, 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 [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 wCorrectShape = {2*inSize, 6*inSize}; const std::vector bCorrectShape = {4*inSize}; const std::vector 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); auto 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 wCorrectShape = {2*inSize, 6*inSize}; const std::vector bCorrectShape = {4*inSize}; const std::vector 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 wCorrectShape = {2*inSize, 6*inSize}; const std::vector bCorrectShape = {4*inSize}; const std::vector c0CorrectShape = {bS, 2*inSize}; const std::vector 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); auto 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 wCorrectShape = {2*inSize, 6*inSize}; const std::vector bCorrectShape = {4*inSize}; const std::vector c0CorrectShape = {bS, 2*inSize}; const std::vector ctCorrectShape = {time, bS, 2*inSize}; const std::vector inGradC0CorrectShape = {bS, 2*inSize}; const std::vector 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.shapeInfo(), false, arr.getContext()); // (const_cast(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] // auto bS = inShape[1]; // auto inSize = inShape[2]; // auto time = inShape[3]; // char order = shape::order(inShape); // ShapeDescriptor descriptor1(ArrayOptions::dataType(inShape), order, {bS, inSize, time}); // ShapeDescriptor descriptor2(ArrayOptions::dataType(inShape), order, {bS, 3 * inSize, inSize}); // ShapeDescriptor descriptor3(ArrayOptions::dataType(inShape), order, {1, 2 * inSize}); // ShapeDescriptor descriptor4(ArrayOptions::dataType(inShape), order, {bS, inSize}); // return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(descriptor1), ConstantShapeHelper::getInstance().createShapeInfo(descriptor2), ConstantShapeHelper::getInstance().createShapeInfo(descriptor3), ConstantShapeHelper::getInstance().createShapeInfo(descriptor4)); // }