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Legacy API changes (#441)
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* another initial commit

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* another initial commit

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* one more initial commit

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* next step

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* next step

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* Refactored buffer() and shapeInfo() methods usage with NDArray class.

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* Adopt Graph class methods to use const shapes.

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* Adopt choose op to use constant shapes.

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* Adopt where op shape method to use constant shapes.

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* Adopt lstsq op to use constant empty shapes.

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* Adopt matrix_diag_part op shape routine to use constant shapes.

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* Adopt determinant ops to use constant shapes.

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* Adopt mean_pairwssqerr_loss ops to use constant shapes.

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* Adopt ops shape methods.

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* Adopt shape methods for loss ops.

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* Adopt log_loss op shape method.

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* Adopt shape methods for ops.

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* Adopt dilation2d ops shape methods.

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* Adopted deconv2d ops shape methods.

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* Adopted dynamicRNN op shape method.

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* Adopted shape methods for ops.

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* Adopted shape methods for lstm layer ops.

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* few updates

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* first cuda tweak

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* Adopt constant shapes for sconv2d ops.

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* Adopt constant shapes for gru ops.

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* Adopt constant shapes with shape methods for segment ops and so on.

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* Adopted constant shapes with unsorted_segment_* ops.

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* Adopted constant shapes with gamma op shape method.

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* Adopted shape methods of reduce_stddev ops.

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* Adopted shape methods for reduce_* ops.

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* Adopt shape method for squeeze op.

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* Adopt strided_slice shape method.

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* Refactored concat op shape method to adopt constant shapes.

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* Adopted shape method for mirror_pad op.

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* Adopted split op shape method.

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* Adopted tile ops shape methods.

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* Added const cast for mkldnn routines handles.

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* Refactored logSoftMaxForVector_ routine to conform with proper data and shape pointer casts.

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* Cosmetic changes to proper usage of constant pointers.

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* Refactored a couple shape comparators for strides and addBias helpers to proper use data pointers with inplace option.

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* Refactored depthToSpace helpers.

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* Refactored histogram helpers.

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* Refactored im2col helpers.

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* Refactored gather and gatherND helpers.

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* Fixed buffer usage on percentile helper.

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* Fixed gather shape with helpers and range buffer usage.

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* Fixed buffer usage with space to depth helpers.

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* Fixed buffer usage and constant shapes.

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* Fixed buffer usage with LUP decomposition>

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* Refactored onehot_ helper.

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* Refactored pad and prefix to use constant shapes.

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* Refactoed softmax helpers.

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* Fixed space to batch helpers to use buffers properly.

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* Fixed stack and split helpers.

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* Fixed buffer usage with sparse to dense helpers.

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* Fixed buffer usage with mindistance_ helpers.

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* Fixed buffer usage with tile helper.

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* Fixed constant shape usage.

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* Fixed constant shape usage with legacy pairwise bool ops.

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* Refactored a couple of methods to adopt constant shape usage.

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* Fixed broadcasting with constant shape."

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* Fixed const usage with inplace reverse and constant shapes with legacy reduction.

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* Refactored legacy ops with const shapes.

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* Refactored sort to adopt constant shapes.

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* Corrected sort for constant shape usage.

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* Fixed constant shape usage with special methods.

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* Refactored Context to conform with constant shape usage.

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* CUDA broadcasting headers

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* pairwise/indexreduce/random headers

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* Refactored native ops to adopt constant shapes.

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* legacy reduce3/scalar headers

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* Corrected pullRow signature and tests.

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* Corrected routines to proper use of constant shapes.

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* Refactored tests to use constant shapes properly.

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* Refactored legacy ops tests to use constant shapes properly.

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* Refactored buffer usage with NDArray tests.

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* Fixed native ops tests.

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* Fixed special concat routine.

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* Fixed buffer usage with test.

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* Fixed buffer usage with a test.

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* Refactored TAD.h and tests.

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* Refactored calcStrides* routines to use constant shapes.

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* Fixed miscelaneous errors with constant shapes.

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* NativeOps const changes

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* Corrected definitions for declared functions.

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* NativeOps const changes

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* few more const changes

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* Fixed const shapes with shape routines.

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* few more const changes

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* Fixed shape method for broadcastable case.

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* few more const changes

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* xw_plus_b BP shape fn restored

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* Fixed signatures with broadcasting.

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* Repaired backprops shape methods for a set of operations.

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* Refactored broadcast bool for cuda.

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* Refactored methods for 3 args with const qualifier.

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* Fixed a couple of kernel signatures for broadcasting.

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* Fixed kernels signatures for const buffers and shapes.

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* Refactored pairwise methods to persistent buffers and shapes usage.

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* Adopt const to buffers and shapes with kernels.

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* Adopt const to buffers and shapes with scalar kernels.

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* Refactored indexreduce kernels signatures to use const buffers and shapes.

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* Refactored pairwise kernels to adopt cons shapes and buffers.

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* Refactored pairwise bool kernels to adopt cons shapes and buffers.

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* Refactored random special ops to conform with const shapes and buffers.

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* Refactored native ops to conform with const shapes and buffers under cuda platform.

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* Cosmetical changes only.

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* Fixed const shapes and buffers error.

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* Corrected start pos routine.

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* Refactored methods to conform with const shapes and buffers.

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* Refactored helpers to use proper methods instead.

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* bunch of changes

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* next bunch of changes

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* next bunch of changes

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* Fixed execScalar declaration.

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* Fixed execScalar declaration.

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* Corrected const shape cases with sort and so on.

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* Fixed const shapes for sort.

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* Refactored kernel declarations to adopt const shapes.

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* Fixed kernels declarations to adopt const shapes.

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* Corrected kernel declarations to adopt const shapes and buffers.

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* Fixed kernels declarations to adopt const shapes.

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* Fixed segment helpers kernels declarations and so on to adopt const shapes.

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* Fixed const shape usage with segment and solve helpers.

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* Fixed kernel declaration with adjustWeight helper.

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* Fixed cuda implementations for constant shape helpers.

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* Adopted const shape usage with kernels.

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* Adopted top_k kernels to use const shapes and buffers.

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* Corrected kernels declarations to adopt const shapes with helpers.

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* Refactored NDArray definitions to adopt const shapes and buffers.

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* Fixed const shapes with image suppression helpers.

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* Slight improvement with buffers.

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* Refactored buffer usage.

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* Refactored buffer usage with tests.

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* Fixed const shape usage with definitions.

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* minor updates on cpu side

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* Refactored const shape usage with ConstantDescritor and native ops with cuda platform.

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* Refactored tear and tile kernels to adopt with const shapes.

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* softmax_loop fix

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* update missing signature

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* softmax again

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* few more missing consts

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* new methods updated

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Co-authored-by: shugeo <sgazeos@gmail.com>
2020-05-09 08:06:14 +03:00

127 lines
7.3 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Alex Black
//
#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_lstmBlockCell)
#include <ops/declarable/CustomOperations.h>
#include<ops/declarable/helpers/lstmBlock.h>
namespace sd {
namespace ops {
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(lstmBlockCell, 8, 7, false, 2, 1) {
//Notation: mostly following https://arxiv.org/pdf/1503.04069.pdf
auto xt = INPUT_VARIABLE(0); // input [bS, inSize] at time t
auto cLast = INPUT_VARIABLE(1); // previous cell state [bS, numUnits], time t-1
auto yLast = INPUT_VARIABLE(2); // previous output [bS, numUnits], time t-1
auto W = INPUT_VARIABLE(3); // Weights - concatenated (input-to-hidden, hidden-to-hidden weights) weights, [(inSize+numUnits), 4*numUnits]
auto Wci = INPUT_VARIABLE(4); // weights - cell peephole (t-1) connections to input modulation gate, [numUnits]
auto Wcf = INPUT_VARIABLE(5); // weights - cell peephole (t-1) connections to forget gate, [numUnits]
auto Wco = INPUT_VARIABLE(6); // weights - cell peephole (t) connections to output gate, [numUnits]
auto b = INPUT_VARIABLE(7); // biases, [4*numUnits]
auto i = OUTPUT_VARIABLE(0); // Output - input modulation gate activations [bS, numUnits]
auto c = OUTPUT_VARIABLE(1); // Activations, cell state (pre tanh) [bs, numUnits]
auto f = OUTPUT_VARIABLE(2); // Output - forget gate activations [bs, numUnits]
auto o = OUTPUT_VARIABLE(3); // Output - output gate activations [bs, numUnits]
auto z = OUTPUT_VARIABLE(4); // Output - input gate activations [bs, numUnits]
auto h = OUTPUT_VARIABLE(5); // Cell state, post tanh [bs, numUnits]
auto y = OUTPUT_VARIABLE(6); // current cell output [bS, numProj], time t
const int peephole = INT_ARG(0); // if 1, provide peephole connections
const double forgetBias = T_ARG(0);
const double clippingCellValue = T_ARG(1); // clipping value for ct, if it is not equal to zero, then cell state is clipped
REQUIRE_TRUE(xt->rankOf()==2 && cLast->rankOf()==2 && yLast->rankOf()==2, 0, "lstmBlockCell: Input ranks must be 2 for inputs 0/1/2 (x, cLast, outLast) - got %i, %i, %i", xt->rankOf(), cLast->rankOf(), yLast->rankOf());
const int rank = xt->rankOf();
const int bS = xt->sizeAt(0);
const int inSize = xt->sizeAt(1);
const int numUnits = cLast->sizeAt(1);
REQUIRE_TRUE(xt->sizeAt(0) == yLast->sizeAt(0) && xt->sizeAt(0) == cLast->sizeAt(0), 0, "lstmBlockCell: Input minibatch sizes (dimension 0) must be same for xt, cLast, yLast");
REQUIRE_TRUE(W->rankOf()==2, 0, "lstmBlockCell: Weights array rank must be 2");
REQUIRE_TRUE(W->sizeAt(0)==(inSize+numUnits), 0, "lstmBlockCell: Weights size(0) must be equal to inSize + numUnits, got %i", W->sizeAt(0));
REQUIRE_TRUE(W->sizeAt(1)==(4*numUnits), 0, "lstmBlockCell: Weights size(1) must be equal to 4*numUnits, got %i", W->sizeAt(1));
REQUIRE_TRUE(b->rankOf()==1 && b->sizeAt(0)==(4*numUnits), 0, "lstmBlockCell: Biases must be rank 1, size 4*numUnits");
REQUIRE_TRUE(i->rankOf()==2 && c->rankOf()==2 && f->rankOf()==2 && o->rankOf()==2 && z->rankOf()==2 && h->rankOf()==2 && y->rankOf()==2 &&
i->sizeAt(0)==bS && c->sizeAt(0)==bS && f->sizeAt(0)==bS && o->sizeAt(0)==bS && z->sizeAt(0)==bS && h->sizeAt(0)==bS && y->sizeAt(0)==bS &&
i->sizeAt(1)==numUnits && c->sizeAt(1)==numUnits && f->sizeAt(1)==numUnits && o->sizeAt(1)==numUnits && z->sizeAt(1)==numUnits && h->sizeAt(1)==numUnits && y->sizeAt(1)==numUnits,
0, "lstmBlockCell: Output arrays must all be rank 2 with size(0) == batchSize and size(1) == numUnits");
// calculations
helpers::lstmBlockCell(xt, cLast, yLast, W, Wci, Wcf, Wco, b, i, c, f, o, z, h, y, {(double)peephole, forgetBias, clippingCellValue});
return Status::OK();
}
DECLARE_TYPES(lstmBlockCell) {
getOpDescriptor()
->setAllowedInputTypes(sd::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(lstmBlockCell) {
auto xt = inputShape->at(0); // input [bS, inSize] at time t
auto cLast = inputShape->at(1); // previous cell state [bS, numUnits], time t-1
auto yLast = inputShape->at(2); // previous output [bS, numUnits], time t-1
auto W = inputShape->at(3); // Weights - concatenated (input-to-hidden, hidden-to-hidden weights) weights, [(inSize+numUnits), 4*numUnits]
auto Wci = inputShape->at(4); // weights - cell peephole (t-1) connections to input modulation gate, [numUnits]
auto Wcf = inputShape->at(5); // weights - cell peephole (t-1) connections to forget gate, [numUnits]
auto Wco = inputShape->at(6); // weights - cell peephole (t) connections to output gate, [numUnits]
auto b = inputShape->at(7); // biases, [4*numUnits]
REQUIRE_TRUE(shape::rank(xt)==2 && shape::rank(cLast)==2 && shape::rank(yLast)==2, 0, "lstmBlockCell: Input ranks must be 2 for inputs 0/1/2 (x, cLast, outLast) - got %i, %i, %i", shape::rank(xt), shape::rank(cLast), shape::rank(yLast));
const int inSize = xt[2];
const int numUnits = cLast[2]; //[rank, bS, nOut, ...]
REQUIRE_TRUE(xt[1] == yLast[1] && xt[1] == cLast[1], 0, "lstmBlockCell: Input minibatch sizes (dimension 0) must be same for xt, cLast, yLast");
REQUIRE_TRUE(shape::rank(W)==2, 0, "lstmBlockCell: Weights array rank must be rank 2, got %i", shape::rank(W));
REQUIRE_TRUE(W[1]==(inSize+numUnits), 0, "lstmBlockCell: Weights size(0) must be equal to inSize + numUnits, got %i", W[1]);
REQUIRE_TRUE(W[2]==(4*numUnits), 0, "lstmBlockCell: Weights size(1) must be equal to 4*numUnits, got %i", W[2]);
REQUIRE_TRUE(shape::rank(b)==1 && b[1]==(4*numUnits), 0, "lstmBlockCell: Biases must be rank 1, size 4*numUnits");
// evaluate output shapeInfos
const int bS = xt[1];
Nd4jLong *s(nullptr);
ALLOCATE(s, block.getWorkspace(), shape::shapeInfoLength(2), Nd4jLong); // [bS, numUnits]
s[0] = 2;
s[1] = bS;
s[2] = numUnits;
ShapeUtils::updateStridesAndType(s, xt, 'c');
auto s1 = CONSTANT(s);
//7 outputs, all same shape: z, i, f, o, h, c, y
return SHAPELIST(s1, s1, s1, s1, s1, s1, s1);
}
}
}
#endif