Alex Black 1170827c18 Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)

* Modified strided_slice op to properly work with empty-like shapes.

* Fixed test for reduce_mean with empty-like input.

* [WIP] Last merge (#15)

* 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

* [WIP] Fixing outstanding issues for NLP (#9)

* Avoid using not-inited objects

* Test fixed.

* Redundant method avoided for models like FastText

* KMeans++ implementation

* KMeans++ implementation

* Disable parallel execution

* KMeans++

* Tests

* Dev branch merge (#16)

* SameDiff: convertDataType and gradient check util improvements (#12)

* GradCheck util improvements

* StopGradient constructor + test

* SameDiff: Add datatype conversion

* Javadoc and add DataType.isNumerical()

* Small fix

* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)

* TFGraphTestAllHelper: check intermediates in execution order

* Add missing debug listener

* [WIP] lstmBlock fix + other changes (#13)

- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite

* Small test fix

* CheckNumerics op wrapper

* Fix some issues on master (#17)

* Fix DataVec test issue

* Fix issue with dl4j SameDiff output layer

* Dtype fix for lambda layers

* #7912 BertIterator dtype fix (use float32 not global default)

* [WIP] Next set of CUDA stuff (#7)

New CUDA implementations and improvements

* bad file

* Dev branch master merge (#23)

* SameDiff: convertDataType and gradient check util improvements (#12)

* GradCheck util improvements

* StopGradient constructor + test

* SameDiff: Add datatype conversion

* Javadoc and add DataType.isNumerical()

* Small fix

* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)

* TFGraphTestAllHelper: check intermediates in execution order

* Add missing debug listener

* [WIP] lstmBlock fix + other changes (#13)

- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite

* Small test fix

* CheckNumerics op wrapper

* Compatibility of deserialization (#18)

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* SameDiff: add activation gradient checking support for debugging (#19)

* SameDiff gradient checker: first pass on activation gradient checks

* Fixes + tests for activation gradient checking

* Javadoc

* [WIP] Some nd4j data type corrections (#20)

* Adjust data type

* Set correct Data type.

* Size of proper data type.

* fix averaged cpu load (#22)

* SameDiff ops, TF import and fixes (#24)

* CheckNumerics tests + fixes + misc fixes

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Fake quant

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* Fixes

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* FakeQuantWithMinMaxArgs

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* CheckNumerics fix

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* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)

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* Small fix

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* Javadoc

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* Exception tweak

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* fix

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* Fix for out of scope stack allocated var use

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* Ignores

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* Ignore for known failing test (already logged issue)

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* Merge upstream to fork (#25)

* Add thousand-separator commas to TotalParams (#7915)

* Add thousand-separator commas to TotalParams

The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.

* Add thousand-separator commas to MultiLayerNetwork

Corresponding change to MultiLayerNetwork

Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>

* Update contributing and issue/PR templates (#7934)

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Fix link to AdaDelta paper (#7942)

Fix link to AdaDelta paper hosted on matthewzeiler.com

Signed-off-by: Jxtps

* Fixes, and ignores for known/logged failing issues (#7943)

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* SameDiff + DL4J/SameDiff: Multiple fixes (#28)

* #7919 HDF5 attribute buffer length fix

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* #7909 Arbiter constructor exception ux improvements

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* #7925 RNN output layer length checks

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* #7939 Add listener for validating inputs are not incorrectly modified

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* #7939 Integrate NonInplaceValidationListener into tests

* #7844 DL4J SameDiff fixes for variable minibatch size

* DL4J SameDiff fixes - ensure gradient for input placeholder is available

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Tweaks to ExternalErrorsFunction - use placeholders, make more robust

* Another fix

* More fixes

* More SameDiff/DL4J fixes

* Scope out scalar array creation in BaseScalarOp

* Remove debug code

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* [WIP] Final dev branch merge (#29)

* SameDiff: convertDataType and gradient check util improvements (#12)

* GradCheck util improvements

* StopGradient constructor + test

* SameDiff: Add datatype conversion

* Javadoc and add DataType.isNumerical()

* Small fix

* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)

* TFGraphTestAllHelper: check intermediates in execution order

* Add missing debug listener

* [WIP] lstmBlock fix + other changes (#13)

- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite

* Small test fix

* CheckNumerics op wrapper

* Compatibility of deserialization (#18)

Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>

* SameDiff: add activation gradient checking support for debugging (#19)

* SameDiff gradient checker: first pass on activation gradient checks

* Fixes + tests for activation gradient checking

* Javadoc

* [WIP] Some nd4j data type corrections (#20)

* Adjust data type

* Set correct Data type.

* Size of proper data type.

* fix averaged cpu load (#22)

* [WIP] Multiple dataset iterators (#27)

* Splitting dataset into arbitrary number

* Fixes

* Multiple split of iterator

* Test

* Test

* Some fixes

* signature change

* one more tweak

Signed-off-by: raver119 <raver119@gmail.com>

* one more test for sequential use of DataSetIteratorSplitter

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* Fixes

* Fixes

* one more test for Alexander

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* Some fixes

* Some fixes

* one more test for Alexander

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* minor test fix

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* Some fixes

* Some fixes

* couple of assertions tweaked

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* MDS splitter test :/

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* Minor refactoring

* Multi dataset

* Some fixes

* More tests

* Small number of test fixes/improvements (failures on CI) (#31)

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* [WIP] More CUDA stuff (#26)

* initial commit

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* LRN BP CUDA

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* less memory

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* Fixed bug with crop_and_resize op helper.

* get rid of unnecessary index-calculation dunction

Signed-off-by: Yurii <yurii@skymind.io>

* Fixed sort with nth_element cuda-based helper.

* Refactored nth_element.

* Refactored nth_element op and tests.

* Modified usage of dim array with sortTad routine.

* Refactored main routine of helper for non_max_image_suppression op.

* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.

* fix vol2col cuda kernel

* meh

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* topK concept

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* unsorted topK with scanWitdh of 1

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* correct vol2col tests

* sorted/unsorted topK

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* implementation and fixing col2im/col2vol

* Corrected usage flags with input/output with reverse op.

* dup is const now

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* percentile op

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* group tests for mapool2d

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* special test for george

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* less threads for sortTad

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* provide conv2d for cuda

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* remove auther in sort tad kernel code

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* provide depthwise_conv2d for cuda

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* - max_pooling_with_argmax
- null check for special use

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* dts cuda

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* provide sconv2d for cuda

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* std cuda

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* Refactored non_max_suppression op to conform TF implementation.

* Improved suppression helper.

* provide pooling3d for cuda

Signed-off-by: Yurii <yurii@skymind.io>

* minor lstm rearrangements

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* more of minor lstm rearrangements

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* (bi)dynamic_rnn

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* templates init order

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* Refactored non_max_suppression op.

* Added cuda kernel for non_max_suppression.

* CPU sort by key/value

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* CPU sort TAD by key/value

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* CPU sort TAD by key/value tests

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* Eliminate compiler error with cuda implementation.

* - repaired gradCheck in cuda
- provide conv2d_bp for cuda

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* missed signature

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* provide depthwise_conv2d_bp for cuda

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* Implementation of lup helper with cuda kernel. Initial commit.

* further work on backprops for convolutions

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* CUDA linear sort by key/val

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* CUDA tad sort by key/val

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* start providing of backprop for pooling2d/3d

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* Added atomicAdd for bool datatype.

* dynamic partition concept

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* dynamic partition concept

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* dynamic partition scalar CUDA

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* important comment

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* fix pooling2d/3d backprop helpers

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* Added non-linear test with dynamic_partition.

* Improved test for dynamic_partition.

* dynamic_partition TAD concept

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* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix

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* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d

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* dynamic_stitch CUDA vector case

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* dynamic_stitch CUDA TAD case concept

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* dynamic_stitch CUDA TAD case impl

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* Added tests for dynamic_stitch 3D-4D cases.

* minor tests tweaks

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* Fixed type check for dynamic stitch.

* min/max bp

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* rewrite code for upsampling2d/3d cpu

Signed-off-by: Yurii <yurii@skymind.io>

* reduce min/max/norm_max bp

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* lup implementation. Additional enhancements.

* provide code for upsamling2d/3d backprop

Signed-off-by: Yurii <yurii@skymind.io>

* weightedCrossEntropyWithLogits

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* Fixed template math atomicMul for 64bit ints.

* Refactored dynamic_partition_bp op.

* inverseBroadcast fix

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* DynamicPartitionBP test datatype fixed.

* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA

Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 18:37:04 +03:00

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/*******************************************************************************
* Copyright (c) 2015-2018 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 Yurii Shyrma, created on 05.04.2018
//
#include <ops/declarable/CustomOperations.h>
#include<ops/declarable/helpers/rnn.h>
namespace nd4j {
namespace ops {
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(dynamic_rnn, 4, 2, false, 0, 0) {
auto x = INPUT_VARIABLE(0); // input [time x bS x inSize] or [bS x time x inSize], depends on timeMajor parameter
auto Wx = INPUT_VARIABLE(1); // input-to-hidden weights, [inSize x numUnits]
auto Wh = INPUT_VARIABLE(2); // hidden-to-hidden weights, [numUnits x numUnits]
auto b = INPUT_VARIABLE(3); // biases for, [2*numUnits]
NDArray* h0 = nullptr; // initial cell output (at time step = 0) [bS x numUnits]
NDArray* maxTimeStep = nullptr; // vector [bS] containing integer values within [0,time), each element of this vector set max time step per each input in batch, this means there are no calculations for time >= maxTimeStep
const int timeMajor = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0; // if true then [time, bS, ...], else [bS, time, ...]
if(block.width() == 5) {
if ((*INPUT_VARIABLE(4)).rankOf() == 2)
h0 = INPUT_VARIABLE(4);
else
maxTimeStep = INPUT_VARIABLE(4);
}
else if(block.width() == 6) {
h0 = INPUT_VARIABLE(4);
maxTimeStep = INPUT_VARIABLE(5);
}
auto h = OUTPUT_VARIABLE(0); // cell outputs [time x bS x numUnits] or [bS x time x numUnits], depends on timeMajor parameter
auto hFinal = OUTPUT_VARIABLE(1); // at the end it will store cell final non-zero output [bS x numUnits]
REQUIRE_TRUE(x->rankOf() == 3, 0, "DYNAMIC_RNN custom operation: input array x must have rank = 3, but got %i instead !", x->rankOf());
REQUIRE_TRUE(Wx->rankOf() == 2, 0, "DYNAMIC_RNN custom operation: input-to-hidden weights array must have rank = 2, but got %i instead !", Wx->rankOf());
const int inRank = x->rankOf();
const int time = timeMajor ? x->sizeAt(0) : x->sizeAt(1);
const int bS = timeMajor ? x->sizeAt(1) : x->sizeAt(0);
const int numUnits = Wx->sizeAt(1);
REQUIRE_TRUE(ShapeUtils::shapeAsString(Wh) == ShapeUtils::shapeAsString({numUnits, numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({numUnits, numUnits}).c_str(), ShapeUtils::shapeAsString(Wh).c_str());
REQUIRE_TRUE(ShapeUtils::shapeAsString(b) == ShapeUtils::shapeAsString({2*numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnits}).c_str(), ShapeUtils::shapeAsString(b).c_str());
if(h0)
REQUIRE_TRUE(ShapeUtils::shapeAsString(h0) == ShapeUtils::shapeAsString({bS, numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of initial cell output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnits}).c_str(), ShapeUtils::shapeAsString(h0).c_str());
if(maxTimeStep)
REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStep) == ShapeUtils::shapeAsString({bS}), 0, "DYNAMIC_RNN custom operation: wrong shape of maxTimeStep array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({bS}).c_str(), ShapeUtils::shapeAsString(maxTimeStep).c_str());
if(timeMajor == false) {
x = new NDArray(x->permute({1, 0, 2})); // [bS x time x inSize] -> [time x bS x inSize]
h = new NDArray(h->permute({1, 0, 2})); // [bS x time x numUnits] -> [time x bS x numUnits]
}
helpers::rnnTimeLoop(block.launchContext(), x, Wx, Wh, b, h0, maxTimeStep, h, hFinal);
if(timeMajor == false) {
delete x;
delete h;
}
return Status::OK();
}
DECLARE_TYPES(dynamic_rnn) {
getOpDescriptor()
->setAllowedInputTypes(0, nd4j::DataType::ANY)
->setAllowedInputTypes(1, {ALL_FLOATS})
->setAllowedInputTypes(2, {ALL_FLOATS})
->setAllowedInputTypes(3, {ALL_FLOATS})
->setAllowedInputTypes(4, {ALL_FLOATS, ALL_INTS})
->setAllowedInputTypes(5, {ALL_FLOATS, ALL_INTS})
->setAllowedOutputTypes(0, {ALL_FLOATS})
->setAllowedOutputTypes(1, {ALL_FLOATS});
}
DECLARE_SHAPE_FN(dynamic_rnn) {
auto xShapeInfo = inputShape->at(0); // input [time x bS x inSize] or [bS x time x inSize], depends on timeMajor parameter
auto WxShapeInfo = inputShape->at(1); // input-to-hidden weights, [inSize x numUnits]
auto WhShapeInfo = inputShape->at(2); // hidden-to-hidden weights, [numUnits x numUnits]
auto bShapeInfo = inputShape->at(3); // biases for, [2*numUnits]
Nd4jLong* h0ShapeInfo = nullptr; // initial cell output (at time step = 0) [bS x numUnits]
Nd4jLong* maxTimeStepShapeInfo = nullptr; // vector [bS] containing integer values within [0,time), each element of this vector set max time step per each input in batch, this means there are no calculations for time >= maxTimeStep
const int timeMajor = block.getIArguments()->size() > 0 ? INT_ARG(0) : 0; // if true then [time, bS, ...], else [bS, time, ...]
if(block.width() == 5) {
if (inputShape->at(4)[0] == 2)
h0ShapeInfo = inputShape->at(4);
else
maxTimeStepShapeInfo = inputShape->at(4);
}
else if(block.width() == 6) {
h0ShapeInfo = inputShape->at(4);
maxTimeStepShapeInfo = inputShape->at(5);
}
REQUIRE_TRUE(xShapeInfo[0] == 3, 0, "DYNAMIC_RNN custom operation: input array x must have rank = 3, but got %i instead !", xShapeInfo[0]);
REQUIRE_TRUE(WxShapeInfo[0] == 2, 0, "DYNAMIC_RNN custom operation: input-to-hidden weights array must have rank = 2, but got %i instead !", WxShapeInfo[0]);
const int inRank = xShapeInfo[0];
const int time = timeMajor ? xShapeInfo[1] : xShapeInfo[2];
const int bS = timeMajor ? xShapeInfo[2] : xShapeInfo[1];
const int numUnits = WxShapeInfo[2];
REQUIRE_TRUE(ShapeUtils::shapeAsString(WhShapeInfo) == ShapeUtils::shapeAsString({numUnits, numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of hidden-to-hidden weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({numUnits, numUnits}).c_str(), ShapeUtils::shapeAsString(WhShapeInfo).c_str());
REQUIRE_TRUE(ShapeUtils::shapeAsString(bShapeInfo) == ShapeUtils::shapeAsString({2*numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of biases array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({2*numUnits}).c_str(), ShapeUtils::shapeAsString(bShapeInfo).c_str());
if(h0ShapeInfo)
REQUIRE_TRUE(ShapeUtils::shapeAsString(h0ShapeInfo) == ShapeUtils::shapeAsString({bS, numUnits}), 0, "DYNAMIC_RNN custom operation: wrong shape of initial cell output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString({bS, numUnits}).c_str(), ShapeUtils::shapeAsString(h0ShapeInfo).c_str());
if(maxTimeStepShapeInfo)
REQUIRE_TRUE(ShapeUtils::shapeAsString(maxTimeStepShapeInfo) == ShapeUtils::shapeAsString({bS}), 0, "DYNAMIC_RNN custom operation: wrong shape of maxTimeStep array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString({bS}).c_str(), ShapeUtils::shapeAsString(maxTimeStepShapeInfo).c_str());
// evaluate output shapeInfos
Nd4jLong *hShapeInfo(nullptr), *hPrevShapeInfo(nullptr);
ALLOCATE(hShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank), Nd4jLong);
ALLOCATE(hPrevShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inRank-1), Nd4jLong);
hShapeInfo[0] = inRank;
hPrevShapeInfo[0] = inRank-1;
hShapeInfo[1] = timeMajor ? time : bS;
hShapeInfo[2] = timeMajor ? bS : time;
hPrevShapeInfo[1] = bS;
hShapeInfo[3] = hPrevShapeInfo[2] = numUnits;
ShapeUtils::updateStridesAndType(hShapeInfo, WhShapeInfo, shape::order(xShapeInfo));
ShapeUtils::updateStridesAndType(hPrevShapeInfo, WhShapeInfo, shape::order(xShapeInfo));
return SHAPELIST(CONSTANT(hShapeInfo), CONSTANT(hPrevShapeInfo));
}
}
}