cavis/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests15.cpp
raver119 53ca9a76e8
[WIP] multi-device support (#80)
* fix pad javadoc and @see links. (#72)

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* [WIP] More fixes (#73)

* special tests for ConstantTadHelper/ConstantShapeHelper

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* release methods for data buffers

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* delete temporary buffer Java side

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* delete temporary buffer Java side

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* delete temporary TadPack C++/Java side (#74)

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* Zoo model TF import test updates (#75)

* argLine fix, update compression_gru comment

* updated comment for xception

* undid but commented argLine change

* updated xlnet comment

* copyright headers

* - new NDArray methods like()/ulike() (#77)

- fix for depthwise_conv2d_bp + special test

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* upsampling2d fix CUDA

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* DL4J trace logging (#79)

* MLN/CG trace logging for debugging

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* Tiny tweak

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* strided_slice_bp shape fn leak fix

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* SameDiff fixes and naming (#78)

* remove SDVariable inplace methods

* import methods

* npe fix in OpVal

* removed SameDiff inplace ops from tests

* Naming updates, moved to centralized methods in SameDiff, should use op_#:# for everything

* quick fixes

* javadoc

* SDVariable eval with placeholders

* use regex match

* better matching

* initial commit

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* initial commit

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* fix javadoc. (#76)

* fix javadoc.

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* replace most @see with @link s.

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* 4 additional tests

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* launch context reorganization

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* LaunchContext reorganization

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* per-device LaunchContext

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* Various DL4J/ND4J fixes (#81)

* #7954 Force refresh of UI when switching tabs on overview page

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* #8017 Concurrent modification exception (synchronize) fix

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* #8033 Don't initialize updater in middle of writing memory crash dump

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* #8208 Fix shape checks for ND4J int[] creator methods

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* #6385 #7992 Keras import naming fixes + cleanup

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* #8016 Upsampling3D - add NDHWC format support

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* ContextBuffers as separate entity

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* Refactor NativeOps.h to export C functions

* Actually export functions from NativeOps.h

* Adapt the Java wrappers in ND4J generated with JavaCPP

* Create C wrappers for some of the C++ classes currently used by ND4J

* ContextBuffers as separate entity

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* remove duplicate code in createBufferDetached. (#83)

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* Keras model import - updater lr fix (#84)

* Keras model import - updater lr fix

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* Keras model import - updater lr fix, cleanup

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* ContextBuffers as separate entity

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* ContextBuffers as separate entity

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* Fix functions of OpaqueVariablesSet

* thread-local buffers/affinity

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* thread safety for LaunchContext

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* more of thread safety

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* one more multi threaded test

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* SameDiff Convolution Config validation, better output methods (#82)

* Conv Config validation & tests

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* stackOutputs utility method

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* use constructor for validation, support negative kernel sizes (infered from weights)

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* better output methods

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* move output to be with fit and evaluate

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* fixes

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* more fixes

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* refactor duplicate code from pad methods. (#86)

* refactor duplicate code from pad methods.

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* replace switch with if.

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* Various ND4J/DL4J fixes and improvements (#87)

* Reshape and reallocate - small fixes

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* Reshape and reallocate - small fixes

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* #6488 ElementWiseVertex broadcast support

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* Constructors and broadcast supported it Transforms.max/min

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* #8054 ElementWiseVertex now supports broadcast inputs

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* #8057 Nd4j.create overload dtype fix

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* #7551 ND4J Shape validation fix

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* [WIP] Numpy boolean import (#91)

* numpy bool type

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* numpy bool java side

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* remove create method with unused parameter. (#89)

* remove create method with unused parameter.

* removed more unused methods.

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* removing more unused code.

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* last removal of unused code.

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* remove createSparse methods. (#92)

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* Various ND4J/DL4J fixes (#90)

* Deprecate Old*Op instances

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* #8063 #8054 Broadcast exceptions + cleanup inplace ops

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* Small fix

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* Remove bad test condition

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* #7993 Fix shape function issue in crop_and_resize op

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* DL4J SameDiff lambda layer fix

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* #8029 Fix for pnorm backprop math

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* #8038 Fix Op profiler NaN/Inf triggering + add tests (#93)

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* createUninitializedDetached refactoring. (#94)

* wip

* update interface, add null implementations.

* Breaking one test in a weird way.

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* createUninitializedDetached refactored.

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* cuda build fix for issues introduced by recent refactoring

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* [WIP] More of CUDA (#95)

* initial commit

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* Implementation of hashcode cuda helper. Working edition.

* Fixed parallel test input arangements.

* Fixed tests for hashcode op.

* Fixed shape calculation for image:crop_and_resize op and test.

* NativeOps tests. Initial test suite.

* Added tests for indexReduce methods.

* Added test on execBroadcast with NDArray as dimensions.

* Added test on execBroadcastBool with NDArray as dimensions.

* Added tests on execPairwiseTransform and execPairwiseTransofrmBool.

* Added tests for execReduce with scalar results.

* Added reduce tests for non-empty dims array.

* Added tests for reduce3.

* Added tests for execScalar.

* Added tests for execSummaryStats.

* - provide cpu/cuda code for batch_to_space
- testing it

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* - remove old test for batch_to_space (had wrong format and numbers were not checked)

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* Fixed complilation errors with test.

* Added test for execTransformFloat.

* Added test for execTransformSame.

* Added test for execTransformBool.

* Added test for execTransformStrict.

* Added tests for execScalar/execScalarBool with TADs.

* Added test for flatten.

* - provide cpu/cuda code for space_to_Batch operaion

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* Added test for concat.

* comment unnecessary stuff in s_t_b

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* Added test for specialConcat.

* Added tests for memcpy/set routines.

* Fixed pullRow cuda test.

* Added pullRow test.

* Added average test.

* - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...)

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* - debugging and fixing cuda tests in JavaInteropTests file

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* - correct some tests

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* Added test for shuffle.

* Fixed ops declarations.

* Restored omp and added shuffle test.

* Added convertTypes test.

* Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps.

* Added sort tests.

* Added tests for execCustomOp.

* - further debuging and fixing tests terminated with crash

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* Added tests for calculateOutputShapes.

* Addded Benchmarks test.

* Commented benchmark tests.

* change assertion

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* Added tests for apply_sgd op. Added cpu helper for that op.

* Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps.

* Added test for assign broadcastable.

* Added tests for assign_bp op.

* Added tests for axpy op.

* - assign/execScalar/execTransformAny signature change
- minor test fix

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* Fixed axpy op.

* meh

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* - fix tests for nativeOps::concat

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* sequential transform/scalar

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* allow nested parallelism

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* assign_bp leak fix

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* block setRNG fix

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* enable parallelism by default

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* enable nested parallelism by default

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* Added cuda implementation for row_count helper.

* Added implementation for tnse gains op helper.

* - take into account possible situations when input arrays are empty in reduce_ cuda stuff

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* Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces.

* Added kernel for tsne/symmetrized op heleper.

* Implementation of tsne/symmetrized op cuda helper. Working edition.

* Eliminated waste printfs.

* Added test for broadcastgradientargs op.

* host-only fallback for empty reduce float

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* - some tests fixes

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* - correct the rest of reduce_ stuff

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* - further correction of reduce_ stuff

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* Added test for Cbow op. Also added cuda implementation for cbow helpers.

* - improve code of stack operation for scalar case

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* - provide cuda kernel for gatherND operation

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* Implementation of cbow helpers with cuda kernels.

* minor tests tweaks

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* minor tests tweaks

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* - further correction of cuda stuff

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* Implementatation of cbow op helper with cuda kernels. Working edition.

* Skip random testing for cudablas case.

* lstmBlockCell context fix

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* Added tests for ELU and ELU_BP ops.

* Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops.

* Added tests for neq_scalar.

* Added test for noop.

* - further work on clipbynorm_bp

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* - get rid of concat op call, use instead direct concat helper call

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* lstmBlockCell context fix

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* Added tests for lrelu and lrelu_bp.

* Added tests for selu and selu_bp.

* Fixed lrelu derivative helpers.

* - some corrections in lstm

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* operator * result shape fix

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* - correct typo in lstmCell

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* few tests fixed

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* CUDA inverse broadcast bool fix

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* disable MMAP test for CUDA

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* BooleanOp syncToDevice

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* meh

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* additional data types for im2col/col2im

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* Added test for firas_sparse op.

* one more RandomBuffer test excluded

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* Added tests for flatten op.

* Added test for Floor op.

* bunch of tests fixed

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* mmulDot tests fixed

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* more tests fixed

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* Implemented floordiv_bp op and tests.

* Fixed scalar case with cuda implementation for bds.

* - work on cuda kernel for clip_by_norm backprop op is completed

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* Eliminate cbow crach.

* more tests fixed

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* more tests fixed

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* Eliminated abortion with batched nlp test.

* more tests fixed

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* Fixed shared flag initializing.

* disabled bunch of cpu workspaces tests

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* scalar operators fix: missing registerSpecialUse call

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* Fixed logdet for cuda and tests.

* - correct clipBynorm_bp

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* Fixed crop_and_resize shape datatype.

* - correct some mmul tests

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* build fix

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* exclude two methods for JNI

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* exclude two methods for JNI

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* exclude two methods for JNI (#97)

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* temporary stack fix

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* round robin affinity test

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* get rid of legacy CudaContext methods

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* get rid of legacy ContextPool classes/methods

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* one legacy test removed

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* few more fields rearranged

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* OpaqueLaunchContext

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* OpaqueLaunchContext++

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* more of OpaqueLaunchContext methods

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* LaunchContext -> CudaContext

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* AffinityManger changes

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* cusolver handles

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* typo

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* cusolver method

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* cusolver handle propagated

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* blas/solver handles

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* one more test

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* legacy concat implementations replaced with new CustomOp

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* one more test

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* concat now uses way more blocks

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* print

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* no more triple template mmul

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* bunch of kernels have dtypes reconsidered

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* bunch of kernels have dtypes reconsidered

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* bitonic sort reorganized

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* bunch of cpu stuff removed from cuda scope

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* bunch of cpu stuff removed from cuda scope

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* type conversions moved to generic impl

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* cpu data types pass

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* non_max_suppression

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* sortByValue fix

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* ignore all mixed datatype tests for mmul

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* special handling of OpProfiler exceptions

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* - one failing concat test in cpp
- Nd4j.tile now uses op internally

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* get back dtype exception for legacy arrays deserialization

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2019-08-14 16:52:34 +03:00

508 lines
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C++

/*******************************************************************************
* 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
******************************************************************************/
//
// Created by raver on 8/4/2018.
//
#include "testlayers.h"
#include <ops/declarable/CustomOperations.h>
#include <NDArray.h>
#include <ops/ops.h>
#include <GradCheck.h>
#include <array>
using namespace nd4j;
class DeclarableOpsTests15 : public testing::Test {
public:
DeclarableOpsTests15() {
printf("\n");
fflush(stdout);
}
};
TEST_F(DeclarableOpsTests15, Test_NormalizeMoments_1) {
auto d = NDArrayFactory::create<double>('c', {10, 10});
auto w = NDArrayFactory::create<double>(10);
auto x = NDArrayFactory::create<double>('c', {10});
auto y = NDArrayFactory::create<double>('c', {10});
auto z0 = NDArrayFactory::create<double>('c', {10});
auto z1 = NDArrayFactory::create<double>('c', {10});
nd4j::ops::normalize_moments op;
auto result = op.execute({&w, &x, &y}, {&z0, &z1}, {1e-4}, {}, {});
ASSERT_EQ(Status::OK(), result);
}
TEST_F(DeclarableOpsTests15, Test_Add_1) {
auto x = NDArrayFactory::create<int>('c', {5}, {1, 1, 1, 1, 1});
auto y = NDArrayFactory::create<int>('c', {5}, {1, 1, 1, 1, 1});
auto e = NDArrayFactory::create<int>('c', {5}, {2, 2, 2, 2, 2});
nd4j::ops::add op;
auto result = op.execute({&x, &y}, {&x}, {}, {}, {});
ASSERT_EQ(Status::OK(), result);
ASSERT_EQ(e, x);
}
TEST_F(DeclarableOpsTests15, Test_Half_assign_1) {
auto x = NDArrayFactory::create<float16>('c', {2, 5});
int y = 1;
x.assign(y);
ASSERT_EQ(10, x.sumNumber().e<int>(0));
}
TEST_F(DeclarableOpsTests15, test_avgpooling_edge_1) {
int inOutH = 5;// 35;
int inOutW = 5;// 35;
int inOutC = 10;// 192;
auto x = NDArrayFactory::create<double>('c', {1, inOutH, inOutW, inOutC});
x.linspace(1.0);
nd4j::ops::avgpool2d op;
auto result = op.execute({&x}, {}, {3,3, 1,1, 0,0, 1,1, 1, 0, 1});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
int totalPadHeight = (inOutH - 1) * 1 + 3 - inOutH;
int padTop = totalPadHeight / 2;
int padBottom = totalPadHeight - totalPadHeight / 2;
int k = 3;
auto m = NDArrayFactory::create<double>('c', {1, inOutH, inOutW, inOutC});
auto c = NDArrayFactory::create<double>('c', {1, inOutH, inOutW, inOutC});
for (int h = 0; h < inOutH; h++) {
for (int w = 0; w < inOutW; w++) {
int hFrom = h - padTop;
int wFrom = w - padBottom;
int hTo = hFrom + k;
int wTo = wFrom + k;
hFrom = nd4j::math::nd4j_max<int>(0, hFrom);
wFrom = nd4j::math::nd4j_max<int>(0, wFrom);
hTo = nd4j::math::nd4j_min<int>(inOutH, hTo);
wTo = nd4j::math::nd4j_min<int>(inOutW, wTo);
int idxOut[4];
int idxIn[4];
for (int ch = 0; ch < inOutC; ch++) {
idxOut[1] = h;
idxOut[2] = w;
idxOut[3] = ch;
idxIn[3] = ch;
for (int kh = hFrom; kh < hTo; kh++) {
for (int kw = wFrom; kw < wTo; kw++) {
idxIn[1] = kh;
idxIn[2] = kw;
auto inVal = x.e<double>(0, kh, kw, ch);
m.p(0, h, w, ch, inVal + m.e<double>(0, h, w, ch));
c.p(0, h, w, ch, 1 + c.e<int>(0, h, w, ch));
}
}
}
}
}
m /= c;
ASSERT_EQ(m, *z);
delete result;
}
TEST_F(DeclarableOpsTests15, Test_standarize_1) {
auto x = NDArrayFactory::create<float>('c', {5}, {1, 1, 1, 1, 1});
auto e = NDArrayFactory::create<float>('c', {5}, {0, 0, 0, 0, 0});
nd4j::ops::standardize op;
auto result = op.execute({&x}, {&x}, {}, {0}, {});
ASSERT_EQ(Status::OK(), result);
ASSERT_EQ(e, x);
}
TEST_F(DeclarableOpsTests15, Test_standarize_bp_1) {
auto x = NDArrayFactory::create<float>('c', {5}, {1., 1., 1., 1., 1.});
auto eps = NDArrayFactory::create<float>('c', {5}, {0., 0., 0., 0., 0.});
nd4j::ops::standardize_bp op;
auto result = op.execute({&x, &eps}, {}, {0}, {});
ASSERT_EQ(Status::OK(), result->status());
delete result;
}
TEST_F(DeclarableOpsTests15, Test_depthwise_bp_1) {
auto in = NDArrayFactory::create<float>('c', {4, 8, 64, 64});
auto w = NDArrayFactory::create<float>('c', {2, 2, 8, 2});
auto b = NDArrayFactory::create<float>('c', {1, 16});
auto grad = NDArrayFactory::create<float>('c', {4, 16, 64, 64});
auto gradI = in.like();
auto gradW = w.like();
auto gradB = b.like();
nd4j:ops::depthwise_conv2d_bp op;
auto status = op.execute({&in, &w, &b, &grad}, {&gradI, &gradW, &gradB}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 1, 0}, {});
ASSERT_EQ(Status::OK(), status);
}
TEST_F(DeclarableOpsTests15, test_matmul_bp_1) {
auto a = NDArrayFactory::create<double>('c', {1, 3});
auto b = NDArrayFactory::create<double>('c', {1, 4});
auto gI = NDArrayFactory::create<double>('c', {3, 4});
auto gA = NDArrayFactory::create<double>('c', {1, 3});
auto gB = NDArrayFactory::create<double>('c', {1, 4});
nd4j::ops::matmul_bp op;
auto status = op.execute({&a, &b, &gI}, {&gA, &gB}, {}, {1, 0, 0}, {});
ASSERT_EQ(Status::OK(), status);
}
TEST_F(DeclarableOpsTests15, test_non_decreasing_1) {
auto x = NDArrayFactory::create<double>(1.0);
auto z = NDArrayFactory::create<bool>(false);
auto e = NDArrayFactory::create<bool>(true);
nd4j::ops::is_non_decreasing op;
Context ctx(1);
ctx.setInputArray(0, &x);
ctx.setOutputArray(0, &z);
auto status = op.execute(&ctx);
ASSERT_EQ(Status::OK(), status);
ASSERT_EQ(e, z);
}
TEST_F(DeclarableOpsTests15, test_check_numeric_1) {
auto x = NDArrayFactory::create<float>('c', {3},{1.f, 2.f, 3.f});
auto y = NDArrayFactory::string("shouldn't ever trigger");
nd4j::ops::check_numerics op;
auto result = op.execute({&x, &y}, {}, {});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_EQ(x, *z);
delete result;
}
TEST_F(DeclarableOpsTests15, test_check_numeric_2) {
auto x = NDArrayFactory::create<float>('c', {3},{1.f, 2.f, std::numeric_limits<float>::infinity()});
auto y = NDArrayFactory::string("should trigger");
auto z = NDArrayFactory::create<float>('c', {3} );
nd4j::ops::check_numerics op;
try {
auto status = op.execute({&x, &y}, {&z}, {}, {}, {});
ASSERT_TRUE(false);
} catch (std::invalid_argument &e) {
//
}
}
TEST_F(DeclarableOpsTests15, test_check_numeric_3) {
auto x = NDArrayFactory::create<float>('c', {3},{1.f, 2.f, std::numeric_limits<float>::quiet_NaN()});
auto y = NDArrayFactory::string("should trigger");
auto z = NDArrayFactory::create<float>('c', {3} );
nd4j::ops::check_numerics op;
try {
auto status = op.execute({&x, &y}, {&z}, {}, {}, {});
ASSERT_TRUE(false);
} catch (std::invalid_argument &e) {
//
}
}
TEST_F(DeclarableOpsTests15, Test_layer_norm_1) {
auto x = NDArrayFactory::create<float>('c', {1, 5}, {1., 2., 3., 4., 5.});
auto g = NDArrayFactory::create<float>('c', {1, 5}, {1., 2., 3., 4., 5.});
auto b = NDArrayFactory::create<float>('c', {1, 5}, {1., 2., 3., 4., 5.});
nd4j::ops::layer_norm op;
auto result = op.execute({&x, &g, &b}, {}, {0}, {});
ASSERT_EQ(Status::OK(), result->status());
delete result;
}
TEST_F(DeclarableOpsTests15, Test_layer_norm_bp_1) {
auto x = NDArrayFactory::create<float>('c', {1, 5}, {1., 2., 3., 4., 5.});
auto g = NDArrayFactory::create<float>('c', {1, 5}, {1., 2., 3., 4., 5.});
auto b = NDArrayFactory::create<float>('c', {1, 5}, {1., 2., 3., 4., 5.});
auto eps = NDArrayFactory::create<float>('c', {1, 5}, {0., 0., 0., 0., 0.});
nd4j::ops::layer_norm_bp op;
auto result = op.execute({&x, &g, &b, &eps}, {}, {0}, {});
ASSERT_EQ(Status::OK(), result->status());
delete result;
}
TEST_F(DeclarableOpsTests15, test_hashCode_1) {
auto x = NDArrayFactory::create<int>('c', {10});
auto y = NDArrayFactory::create<int>('c', {10});
x.linspace(1.);
y.linspace(2.);
nd4j::ops::hashcode op;
auto resultA0 = op.execute({&x}, {}, {}, {}, false, nd4j::DataType::INT64);
auto resultA1 = op.execute({&x}, {}, {}, {}, false, nd4j::DataType::INT64);
auto resultB0 = op.execute({&y}, {}, {}, {}, false, nd4j::DataType::INT64);
// resultA0->at(0)->printIndexedBuffer("A0");
// resultA1->at(0)->printIndexedBuffer("A1");
// resultB0->at(0)->printIndexedBuffer("B0");
ASSERT_EQ(*resultA0->at(0), *resultA1->at(0));
ASSERT_NE(*resultA0->at(0), *resultB0->at(0));
delete resultA0;
delete resultA1;
delete resultB0;
}
TEST_F(DeclarableOpsTests15, test_hashCode_2) {
auto x = NDArrayFactory::create<int>('c', {1027});
auto y = NDArrayFactory::create<int>('c', {1027});
x.linspace(1.);
y.linspace(2.);
nd4j::ops::hashcode op;
auto resultA0 = op.execute({&x}, {}, {}, {}, false, nd4j::DataType::INT64);
auto resultA1 = op.execute({&x}, {}, {}, {}, false, nd4j::DataType::INT64);
auto resultB0 = op.execute({&y}, {}, {}, {}, false, nd4j::DataType::INT64);
// resultA0->at(0)->printIndexedBuffer("A0");
// resultA1->at(0)->printIndexedBuffer("A1");
// resultB0->at(0)->printIndexedBuffer("B0");
ASSERT_EQ(*resultA0->at(0), *resultA1->at(0));
ASSERT_NE(*resultA0->at(0), *resultB0->at(0));
delete resultA0;
delete resultA1;
delete resultB0;
}
TEST_F(DeclarableOpsTests15, test_reshape_to_scalar_1) {
auto array = NDArrayFactory::create<float>(119.f);
auto e = NDArrayFactory::create<float>('c', {1, 1}, {119.f});
nd4j::ops::reshape op;
auto result = op.execute({&array}, {}, {1, 1});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_EQ(e, *z);
delete result;
}
TEST_F(DeclarableOpsTests15, test_reshape_to_scalar_2) {
auto array = NDArrayFactory::create<float>(119.f);
auto e = NDArrayFactory::create<float>('c', {1, 1}, {119.f});
auto z = NDArrayFactory::create<float>('c', {1, 1});
nd4j::ops::reshape op;
auto result = op.execute({&array}, {&z}, {}, {1, 1}, {});
ASSERT_EQ(Status::OK(), result);
ASSERT_EQ(e, z);
}
TEST_F(DeclarableOpsTests15, test_rank_1) {
auto array = NDArrayFactory::create<float>('c', {4, 64});
auto e = NDArrayFactory::create<int>('c', {}, {2});
auto z = NDArrayFactory::create<int>('c', {});
nd4j::ops::rank op;
auto result = op.execute({&array}, {&z}, {}, {}, {});
ASSERT_EQ(Status::OK(), result);
ASSERT_EQ(e, z);
}
TEST_F(DeclarableOpsTests15, test_rank_2) {
auto array = NDArrayFactory::create<float>('c', {4, 64});
auto e = NDArrayFactory::create<int>('c', {}, {2});
nd4j::ops::rank op;
auto result = op.execute({&array}, {}, {});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
ASSERT_EQ(e, *z);
delete result;
}
TEST_F(DeclarableOpsTests15, test_concat_column_1) {
auto x = NDArrayFactory::create<double>('c', {2, 1}, {1, 1});
auto y = NDArrayFactory::create<double>('c', {2, 1}, {0, 0});
auto e = NDArrayFactory::create<double>('c', {2, 2}, {1, 0, 1, 0});
auto z = NDArrayFactory::create<double>('c', {2, 2});
nd4j::ops::concat op;
auto status = op.execute({&x, &y}, {&z}, {}, {1}, {});
ASSERT_EQ(Status::OK(), status);
z.printIndexedBuffer("z");
ASSERT_EQ(e, z);
}
TEST_F(DeclarableOpsTests15, test_concat_large_1) {
std::array<NDArray*, 2000> arrays;
Context context(1);
Nd4jLong axis = 0;
// we crate bunch of arrays, filled with specific values
for (int e = 0; e < arrays.size(); e++) {
auto array = NDArrayFactory::create_<float>('c', {1, 300});
array->assign(e);
context.setInputArray(e, array, true);
}
auto z = NDArrayFactory::create<float>('c', {2000, 300});
context.setOutputArray(0, &z, false);
context.setIArguments(&axis, 1);
nd4j::ops::concat op;
op.execute(&context);
for (int e = 0; e < arrays.size(); e++) {
auto row = z.tensorAlongDimension(e, {1});
ASSERT_NEAR((float) e, row->e<float>(0), 1e-5f);
delete row;
}
}
TEST_F(DeclarableOpsTests15, test_concat_large_2) {
std::array<NDArray*, 10> arrays;
Context context(1);
Nd4jLong axis = 0;
// we crate bunch of arrays, filled with specific values
for (int e = 0; e < arrays.size(); e++) {
auto array = NDArrayFactory::create_<float>('c', {1, 5, 20});
array->assign(e);
context.setInputArray(e, array, true);
}
auto z = NDArrayFactory::create<float>('c', {arrays.size(), 5, 20});
context.setOutputArray(0, &z, false);
context.setIArguments(&axis, 1);
nd4j::ops::concat op;
op.execute(&context);
for (int e = 0; e < arrays.size(); e++) {
auto row = z.tensorAlongDimension(e, {1, 2});
ASSERT_NEAR((float) e, row->meanNumber().e<float>(0), 1e-5f);
delete row;
}
}
TEST_F(DeclarableOpsTests15, test_lstmBlock_1) {
auto x0 = NDArrayFactory::create<Nd4jLong>(5);
auto x1 = NDArrayFactory::create<float>('c', {5, 1, 4}, {0.7787856f, 0.80119777f, 0.72437465f, 0.23089433f, 0.72714126f, 0.18039072f, 0.50563407f, 0.89252293f, 0.5461209f, 0.92336726f, 0.085571885f, 0.7937801f, 0.65908563f, 0.55552566f, 0.15962744f, 0.30874777f, 0.15476847f, 0.46954823f, 0.9938899f, 0.6112741f});
auto x2 = NDArrayFactory::create<float>('c', {1, 3}, {0.7717289f, 0.9280778f, 0.98455656f});
auto x3 = NDArrayFactory::create<float>('c', {1, 3}, {0.94414854f, 0.5956861f, 0.8668989f});
auto x4 = NDArrayFactory::create<float>('c', {7, 12}, {0.460692f, 0.042572856f, 0.08420354f, -0.09538093f, -0.11416581f, -0.53166187f, 0.40133476f, -0.24381405f, 0.30778718f, 0.52713746f, 0.16253126f, -0.034891903f, 0.011679292f, -0.19076681f, 0.14710993f, -0.3704369f, 0.51872355f, 0.13536876f, -0.5568739f, -0.08727971f, 0.07601875f, -0.074174374f, -0.5345982f, -0.3581748f, -0.28263924f, -0.25141674f, 0.43328637f, -0.50227314f, -0.26641843f, -0.38241976f, -0.19636461f, -0.04020852f, -0.27312332f, 0.5207915f, -0.37247592f, -0.4713087f, -0.25670746f, -0.14942765f, -0.015806139f, -0.22531253f, 0.5582536f, 0.3093416f, 0.3221351f, -0.0964683f, 0.14318448f, 0.42279094f, -0.46992f, -0.43399644f, -0.51704615f, -0.11854091f, 0.21697259f, -0.049382925f, 0.14059627f, 0.3912331f, -0.41345632f, 0.5067368f, -0.3420229f, 0.485789f, 0.044918716f, 0.26209074f, 0.12357575f, 0.21778125f, -0.53791714f, 0.18346387f, 0.054183125f, 0.5480431f, 0.03675288f, -0.26656917f, -0.018610716f, 0.19917983f, 0.5566165f, 0.43570566f, -0.35720813f, 0.31097364f, -0.47134516f, -0.289197f, 0.091138184f, 0.13300979f, -0.36592877f, -0.17540845f, 0.21732038f, 0.4393713f, 0.42800313f, 0.5006979f});
auto x5 = NDArrayFactory::create<float>('c', {1, 3});
auto x6 = NDArrayFactory::create<float>('c', {1, 3});
auto x7 = NDArrayFactory::create<float>('c', {1, 3});
auto x8 = NDArrayFactory::create<float>('c', {12});
nd4j::ops::lstmBlock op;
auto result = op.execute({&x0, &x1, &x2, &x3, &x4, &x5, &x6, &x7, &x8}, {2.0, 0.3}, {0, 0});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
// z->printIndexedBuffer("Z");
delete result;
}
TEST_F(DeclarableOpsTests15, test_lstmBlock_2) {
int seqLen = 32;
int bS = 64;
int nIn = 32;
auto x0 = NDArrayFactory::create<Nd4jLong>(5);
auto x1 = NDArrayFactory::create<float>('f', {bS, nIn, seqLen});
auto x2 = NDArrayFactory::create<float>('f', {bS, nIn}); // nIn == nOut
auto x3 = NDArrayFactory::create<float>('f', {bS, nIn});
auto x4 = NDArrayFactory::create<float>('f', {2 * nIn, 4 * nIn});
auto x5 = NDArrayFactory::create<float>('f', {nIn});
auto x6 = NDArrayFactory::create<float>('f', {nIn});
auto x7 = NDArrayFactory::create<float>('f', {nIn});
auto x8 = NDArrayFactory::create<float>('f', {4 * nIn});
nd4j::ops::lstmBlock op;
auto result = op.execute({&x0, &x1, &x2, &x3, &x4, &x5, &x6, &x7, &x8}, {1.0, 0.0}, {0, 1});
ASSERT_EQ(Status::OK(), result->status());
auto z = result->at(0);
delete result;
}
TEST_F(DeclarableOpsTests15, test_lstmBlock_3) {
int seqLen = 3;
int bS = 2;
int nIn = 4;
NDArray f('f', {bS, nIn, seqLen}, nd4j::DataType::FLOAT32);
NDArray cLast('f', {bS, nIn}, nd4j::DataType::FLOAT32);
f = 2;
cLast = 3;
for (int t = 0; t < seqLen; ++t) {
//section 1
//auto ft = f({0,0, 0,0, t,t+1});
//auto temp = ft * cLast;
// section 2
auto ft = f({0,0, 0,0, t,t+1});
auto temp1 = ft.reshape('f', {bS, nIn});
auto temp2 = temp1 * cLast;
}
}