Alex Black 68ea5f3688
Dev branch merge: dev_20190606 (#7904)
* correct logsoftmax looss (#2)

* Small SameDiff listener fix (#4)

* Various fixes (#6)

* #7839 Fix for asXMatrix and tests

* #7866 EmbeddingSequenceLayer dtype fix + test

* #7856 SameDiff save/load stream methods

* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration

* EvaluationBinary 3d/4d

* More evaluation 3d/4d tests

* #7847 Evaluation empty checks

* Small test ifx

* #7848 Fix median edge case

* Improve DL4J samediff layer tests

* [WIP] FastText wrapper implemented (#8)

* FastText implemented

* Some fixes

* Fix shapes for wordsNearest

* Validation of input vectors

* Fixes

* Fixed test

* Thread tagged

* Some tweaks

* setContextClassLoader for DeallocatorServiceThread

* Numpy format tests (#1)

* Various fixes (#11)

* #7852 SameDiff gather fix

* #7892 SameDiff placeholder to constant conversion

* #7890 validate input rank for MLN/CG init methods

* Fix broken permute shape calculation

* Permute and gather fixes

* Tests

* #7850 LogSumExp fix + test

* Handful of test fixes

* Empty arrays with non-scalar shapes (#10)

* minor rearrangements for lambdas

* empty tensors with non-scalar shapes

* numpy empty tensors with non-scalar shapes

* few more empty tweaks

* Small fixes

* conv3d signature update

* micro fix in batchnorm mkldnn

* Import fixes

* Fix

* MKL-DNN update

* Small fill fix

* fill with empty input + test

* Fixes

* Small error improvement

* Fix

* one special test

* couple of fixes for lstm

* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone

* Fixes

* FP16

* Unsigned

* BFloat16

* Fill op - empty tweaks

* - couple of fixes for empty arrays construction
- stack updated

* strided slice fix

* one transform test

* provide method for reducing shapeInfo in case of input array is empty

* Fixed reduceAlongDimensions to use empty input properly.

* couple of broadcast tests

* couple of tests broadcast tests + tweak to make them pass

* add check of non-empty to methods producing sub-arrays

* Fixed reshapeC with zeros in shape.

* complete empty check in reduce_... legacy ops

* Concat and cumsum/prod

* Tweak to empty shape inference on import

* add empty check to the rest of reduce legacy ops

* one more test

* correct typo in evalReduceShapeInfoEmpty

* Added tests for reduce_* ops to tests with zero shapes.

* few more tests for empty reductions

* Fixed strided_slice op with empty case and tests.

* one more empty reduction test

* Fixed strided_slice test.

* add empty check to NDArray::reshapei

* infOrMax

* empty min/max with infinity tests

* made unstack working correctly with empty arrays

* few IndexReduce tests + tweaks for empty shapes

* add test for empty concat

* few tests fixed

* Validation fix for reductions on empty shapes

* Reverse fix

* Reduction shape calc fixes

* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs

* Range fix

* - NDArray constructor updated for scalars/empty arrays
- few tests fixed

* More fixes

* Empty creator fixes

* concat fix

* concat fix

* TF import tests: allow 'both all NaN' and 'both all inf' to pass

* Slice, zero fraction, and reshape fixes

* transpose, gather

* Zero fraction

* scalar cast fix

* Empty reduction axis support

* few more tests fixed

* Fixed input checks conforming with TF for concat op and tests.

* few tests fixed

* matmul scalar shape fix

* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.

* broadcast bool fix

* few more tests

* few more tests

* correct evalReduceShapeInfoEmpty

* argmax/argmin + tests

* one more empty edge case + one more test

* argmax/argmin/realdiv_bp tweaks

* empty reshape test + fix

* Helper fixes

* Small fixes

* Gather test fix

* Gather test fix

* Small fixes

* reduce scalar zero values

* scalar mean workaround

* Remove debug code

* along dim mean workaround

* one more test

* - equalsTo() tweak for empty arrays
- one more test

* broadcast tweaks
2019-06-15 21:34:34 +10:00

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 <op_boilerplate.h>
#if NOT_EXCLUDED(OP_lstmBlock)
#include <ops/declarable/CustomOperations.h>
#include<ops/declarable/helpers/lstmBlock.h>
namespace nd4j {
namespace ops {
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(lstmBlock, 9, 7, false, 2, 2) {
auto maxTSLength = INPUT_VARIABLE(0);
auto x = INPUT_VARIABLE(1); // input [seqLen, bS, inSize] at time t
auto cLast = INPUT_VARIABLE(2); // previous cell state [bS, numUnits], time t-1
auto yLast = INPUT_VARIABLE(3); // previous output [bS, numUnits], time t-1
auto W = INPUT_VARIABLE(4); // Weights - concatenated (input-to-hidden, hidden-to-hidden weights) weights, [(inSize+numUnits), 4*numUnits]
auto Wci = INPUT_VARIABLE(5); // weights - cell peephole (t-1) connections to input modulation gate, [numUnits]
auto Wcf = INPUT_VARIABLE(6); // weights - cell peephole (t-1) connections to forget gate, [numUnits]
auto Wco = INPUT_VARIABLE(7); // weights - cell peephole (t) connections to output gate, [numUnits]
auto b = INPUT_VARIABLE(8); // biases, [4*numUnits]
auto i = OUTPUT_VARIABLE(0); // Output - input modulation gate activations [seqLen, bS, numUnits]
auto c = OUTPUT_VARIABLE(1); // Activations, cell state (pre tanh) [seqLen, bs, numUnits]
auto f = OUTPUT_VARIABLE(2); // Output - forget gate activations [seqLen, bs, numUnits]
auto o = OUTPUT_VARIABLE(3); // Output - output gate activations [seqLen, bs, numUnits]
auto z = OUTPUT_VARIABLE(4); // Output - input gate activations [seqLen, bs, numUnits]
auto h = OUTPUT_VARIABLE(5); // Cell state, post tanh [seqLen, bs, numUnits]
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,mb,size]; 1=NST=[mb,size,seqLen]; 2=NTS=[mb,seqLen,size]
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(nd4j::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,mb,size]; 1=NST=[mb,size,seqLen]; 2=NTS=[mb,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');
Nd4jLong *s1 = CONSTANT(s);
//7 outputs, all same shape/type
return SHAPELIST(s1, s1, s1, s1, s1, s1, s1);
}
}
}
#endif