raver119 320924278d
Legacy API changes (#441)
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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

125 lines
5.8 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
******************************************************************************/
// lstmBlock: Full LSTM layer in one op
// @author Alex Black
//
#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_lstmBlock)
#include <ops/declarable/CustomOperations.h>
#include<ops/declarable/helpers/lstmBlock.h>
namespace sd {
namespace ops {
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(lstmBlock, 9, 7, false, 2, 2) {
auto maxTSLength = INPUT_VARIABLE(0);
auto x = INPUT_VARIABLE(1); // input [seqLen, bS, nIn] at time t
auto cLast = INPUT_VARIABLE(2); // previous cell state [bS, nOut], time t-1
auto yLast = INPUT_VARIABLE(3); // previous output [bS, nOut], time t-1
auto W = INPUT_VARIABLE(4); // Weights - concatenated (input-to-hidden, hidden-to-hidden weights) weights, [(nIn+nOut), 4*nOut]
auto Wci = INPUT_VARIABLE(5); // weights - cell peephole (t-1) connections to input modulation gate, [nOut]
auto Wcf = INPUT_VARIABLE(6); // weights - cell peephole (t-1) connections to forget gate, [nOut]
auto Wco = INPUT_VARIABLE(7); // weights - cell peephole (t) connections to output gate, [nOut]
auto b = INPUT_VARIABLE(8); // biases, [4*nOut]
auto i = OUTPUT_VARIABLE(0); // Output - input modulation gate activations [seqLen, bS, nOut]
auto c = OUTPUT_VARIABLE(1); // Activations, cell state (pre tanh) [seqLen, bs, nOut]
auto f = OUTPUT_VARIABLE(2); // Output - forget gate activations [seqLen, bs, nOut]
auto o = OUTPUT_VARIABLE(3); // Output - output gate activations [seqLen, bs, nOut]
auto z = OUTPUT_VARIABLE(4); // Output - input gate activations [seqLen, bs, nOut]
auto h = OUTPUT_VARIABLE(5); // Cell state, post tanh [seqLen, bs, nOut]
auto y = OUTPUT_VARIABLE(6); // current cell output [seqLen, bS, numProj], time t
const int peephole = INT_ARG(0); // if 1, provide peephole connections
const int dataFormat = INT_ARG(1); // 0=TNS=[seqLen,bS,nIn]; 1=NST=[bS,nIn,seqLen]; 2=NTS=[bS,seqLen,nIn]
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(x->rankOf()==3, 0, "lstmBlock: Input array 1 (x) rank must be got input with rank %i", x->rankOf());
REQUIRE_TRUE(cLast->rankOf()==2 && yLast->rankOf()==2, 0, "lstmBlock: Input ranks must be 2 for inputs 2/3 (cLast, yLast) - got %i, %i", cLast->rankOf(), yLast->rankOf());
REQUIRE_TRUE(W->rankOf()==2, 0, "lstmBlock: Weights array rank must be 2");
REQUIRE_TRUE(b->rankOf()==1, 0, "lstmBlock: Biases must be rank 1");
REQUIRE_TRUE(i->rankOf()==3 && c->rankOf()==3 && f->rankOf()==3 && o->rankOf()==3 && z->rankOf()==3 && h->rankOf()==3 && y->rankOf()==3,
0, "lstmBlock: Output arrays must all be rank 3");
helpers::lstmBlockTimeLoop(maxTSLength, x, cLast, yLast, W, Wci, Wcf, Wco, b, i, c, f, o, z, h, y, {(double)peephole, forgetBias, clippingCellValue}, dataFormat);
return Status::OK();
}
DECLARE_TYPES(lstmBlock) {
getOpDescriptor()
->setAllowedInputTypes(sd::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(lstmBlock) {
auto x = inputShape->at(1);
auto cLast = inputShape->at(2);
auto yLast = inputShape->at(3);
auto W = inputShape->at(4);
auto b = inputShape->at(8);
REQUIRE_TRUE(shape::rank(x)==3, 0, "lstmBlock: Input array 1 (x) rank must be got input with rank %i", shape::rank(x));
REQUIRE_TRUE(shape::rank(cLast)==2 && shape::rank(yLast)==2, 0, "lstmBlock: Input ranks must be 2 for inputs 2/3 (cLast, yLast) - got %i, %i", shape::rank(cLast), shape::rank(yLast));
REQUIRE_TRUE(shape::rank(W)==2, 0, "lstmBlock: Weights array rank must be 2");
REQUIRE_TRUE(shape::rank(b)==1, 0, "lstmBlock: Biases must be rank 1");
const int dataFormat = INT_ARG(1); // 0=TNS=[seqLen,bS,size]; 1=NST=[bS,size,seqLen]; 2=NTS=[bS,seqLen,size]
int bs;
int t;
int nOut = cLast[2]; //rank, bs, nOut, ...]
Nd4jLong *s(nullptr);
ALLOCATE(s, block.getWorkspace(), shape::shapeInfoLength(3), Nd4jLong); // [time, bS, nOut]
s[0] = 3;
if(dataFormat == 0){
//[rank, seqLen, bs, nIn, ...]
s[1] = x[1]; //seqLen
s[2] = x[2]; //bS
s[3] = nOut;
} else if(dataFormat==1){
//[rank, bs, nIn, seqLen, ...]
s[1] = x[1]; //bS
s[2] = nOut;
s[3] = x[3]; //seqLen
} else {
//[rank, bs, seqLen, nIn, ...]
s[1] = x[1]; //bS
s[2] = x[2]; //seqLen
s[3] = nOut;
}
ShapeUtils::updateStridesAndType(s, x, 'c');
auto s1 = CONSTANT(s);
//7 outputs, all same shape/type
return SHAPELIST(s1, s1, s1, s1, s1, s1, s1);
}
}
}
#endif