2019-06-06 14:21:15 +02:00
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/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// Created by GS <sgazeos@gmail.com> on 2018-12-20.
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//
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#include <NDArrayFactory.h>
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#include <exceptions/cuda_exception.h>
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#include <ConstantHelper.h>
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#include <ConstantShapeHelper.h>
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#include <ShapeUtils.h>
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#include <type_traits>
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namespace nd4j {
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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
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* 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)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #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
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
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////////////////////////////////////////////////////////////////////////
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template <>
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2019-12-02 19:37:21 +01:00
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ND4J_EXPORT NDArray NDArrayFactory::create<bool>(const char order, const std::vector<Nd4jLong> &shape, const std::vector<bool> &data, nd4j::LaunchContext * context) {
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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
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* 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)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #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
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
|
|
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if ((int) shape.size() > MAX_RANK)
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throw std::invalid_argument("NDArrayFactory::create: rank of NDArray can't exceed 32 !");
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ShapeDescriptor descriptor(nd4j::DataType::BOOL, order, shape);
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if (descriptor.arrLength() != data.size()) {
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nd4j_printf("NDArrayFactory::create: data size [%i] doesn't match shape length [%lld]\n", data.size(), descriptor.arrLength());
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throw std::runtime_error("NDArrayFactory::create: data size doesn't match shape");
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}
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bool* hostBuffer = nullptr;
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ALLOCATE(hostBuffer, context->getWorkspace(), data.size(), bool);
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std::copy(data.begin(), data.end(), hostBuffer);
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std::shared_ptr<DataBuffer> buffer = std::make_shared<DataBuffer>(hostBuffer, data.size() * sizeof(bool), nd4j::DataType::BOOL, true, context->getWorkspace());
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NDArray result(buffer, descriptor, context);
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return result;
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}
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////////////////////////////////////////////////////////////////////////
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template <typename T>
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NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<T> &data, nd4j::LaunchContext * context) {
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if ((int) shape.size() > MAX_RANK)
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throw std::invalid_argument("NDArrayFactory::create: rank of NDArray can't exceed 32 !");
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ShapeDescriptor descriptor(DataTypeUtils::fromT<T>(), order, shape);
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if (descriptor.arrLength() != data.size()) {
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nd4j_printf("NDArrayFactory::create: data size [%i] doesn't match shape length [%lld]\n", data.size(), descriptor.arrLength());
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throw std::runtime_error("NDArrayFactory::create: data size doesn't match shape");
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}
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std::shared_ptr<DataBuffer> buffer = std::make_shared<DataBuffer>(data.data(), DataTypeUtils::fromT<T>(), descriptor.arrLength() * sizeof(T), context->getWorkspace());
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NDArray result(buffer, descriptor, context);
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return result;
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}
|
2019-12-02 19:37:21 +01:00
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template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<double>& data, nd4j::LaunchContext * context);
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template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<float>& data, nd4j::LaunchContext * context);
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template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<float16>& data, nd4j::LaunchContext * context);
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template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<bfloat16>& data, nd4j::LaunchContext * context);
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template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<Nd4jLong>& data, nd4j::LaunchContext * context);
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template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<uint64_t>& data, nd4j::LaunchContext * context);
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template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<int>& data, nd4j::LaunchContext * context);
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template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<unsigned int>& data, nd4j::LaunchContext * context);
|
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|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<int16_t>& data, nd4j::LaunchContext * context);
|
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|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<int8_t>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<uint8_t>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::vector<bool>& data, nd4j::LaunchContext * context);
|
2019-06-06 14:21:15 +02:00
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|
|
|
|
|
|
NDArray NDArrayFactory::string(const char *str, nd4j::LaunchContext * context) {
|
|
|
|
std::string s(str);
|
|
|
|
return string(s, context);
|
|
|
|
}
|
|
|
|
|
|
|
|
NDArray* NDArrayFactory::string_(const char *str, nd4j::LaunchContext * context) {
|
|
|
|
return string_(std::string(str), context);
|
|
|
|
}
|
|
|
|
|
|
|
|
NDArray NDArrayFactory::string(const std::string &str, nd4j::LaunchContext * context) {
|
|
|
|
|
|
|
|
auto headerLength = ShapeUtils::stringBufferHeaderRequirements(1);
|
|
|
|
|
|
|
|
std::shared_ptr<DataBuffer> pBuffer = std::make_shared<DataBuffer>(headerLength + str.length(), DataType::UTF8, context->getWorkspace(), true);
|
|
|
|
|
|
|
|
NDArray res(pBuffer, ShapeDescriptor::scalarDescriptor(DataType::UTF8), context);
|
|
|
|
|
|
|
|
int8_t* buffer = reinterpret_cast<int8_t*>(res.getBuffer());
|
|
|
|
|
|
|
|
auto offsets = reinterpret_cast<Nd4jLong *>(buffer);
|
|
|
|
offsets[0] = 0;
|
|
|
|
offsets[1] = str.length();
|
|
|
|
|
|
|
|
auto data = buffer + headerLength;
|
|
|
|
|
|
|
|
memcpy(data, str.c_str(), str.length());
|
|
|
|
|
|
|
|
res.tickWriteHost();
|
|
|
|
res.syncToDevice();
|
|
|
|
|
|
|
|
return res;
|
|
|
|
}
|
|
|
|
|
|
|
|
NDArray* NDArrayFactory::string_(const std::string &str, nd4j::LaunchContext * context) {
|
|
|
|
auto res = new NDArray();
|
|
|
|
*res = NDArrayFactory::string(str, context);
|
|
|
|
return res;
|
|
|
|
}
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template<typename T>
|
|
|
|
NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, nd4j::LaunchContext * context) {
|
|
|
|
return create_(order, shape, DataTypeUtils::fromT<T>(), context);
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray* NDArrayFactory::create_, (const char order, const std::vector<Nd4jLong> &shape, nd4j::LaunchContext * context), LIBND4J_TYPES);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
void NDArrayFactory::memcpyFromVector(void *ptr, const std::vector<T> &vector) {
|
|
|
|
|
|
|
|
memcpy(ptr, vector.data(), vector.size() * sizeof(T));
|
|
|
|
}
|
|
|
|
|
|
|
|
template <>
|
2019-12-02 19:37:21 +01:00
|
|
|
void ND4J_EXPORT NDArrayFactory::memcpyFromVector(void *ptr, const std::vector<bool> &vector) {
|
2019-06-06 14:21:15 +02:00
|
|
|
auto p = reinterpret_cast<bool *>(ptr);
|
|
|
|
for (Nd4jLong e = 0; e < vector.size(); e++)
|
|
|
|
p[e] = vector[e];
|
|
|
|
}
|
|
|
|
|
2019-12-02 19:37:21 +01:00
|
|
|
template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std::vector<double> &vector);
|
|
|
|
template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std::vector<float> &vector);
|
|
|
|
template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std::vector<float16> &vector);
|
|
|
|
template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std::vector<Nd4jLong> &vector);
|
|
|
|
template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std::vector<int> &vector);
|
|
|
|
template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std::vector<int16_t> &vector);
|
|
|
|
template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std::vector<uint8_t> &vector);
|
|
|
|
template ND4J_EXPORT void NDArrayFactory::memcpyFromVector(void *ptr, const std::vector<int8_t> &vector);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
|
|
|
|
#ifndef __JAVACPP_HACK__
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
NDArray* NDArrayFactory::valueOf(const std::initializer_list<Nd4jLong>& shape, const T value, const char order, nd4j::LaunchContext * context) {
|
|
|
|
return valueOf(std::vector<Nd4jLong>(shape), value, order);
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list<Nd4jLong>& shape, const double value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list<Nd4jLong>& shape, const float value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list<Nd4jLong>& shape, const float16 value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list<Nd4jLong>& shape, const bfloat16 value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list<Nd4jLong>& shape, const Nd4jLong value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list<Nd4jLong>& shape, const int value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list<Nd4jLong>& shape, const uint8_t value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list<Nd4jLong>& shape, const int8_t value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list<Nd4jLong>& shape, const int16_t value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::initializer_list<Nd4jLong>& shape, const bool value, const char order, nd4j::LaunchContext * context);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<T>& data, nd4j::LaunchContext * context) {
|
|
|
|
std::vector<T> vec(data);
|
|
|
|
return create<T>(order, shape, vec, context);
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<double>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<float>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<float16>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<bfloat16>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<Nd4jLong>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<uint64_t>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<int>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<unsigned int>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<int16_t>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<int8_t>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<uint8_t>& data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<bool>& data, nd4j::LaunchContext * context);
|
2019-06-06 14:21:15 +02:00
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|
|
#endif
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
NDArray* NDArrayFactory::create_(const T scalar, nd4j::LaunchContext * context) {
|
|
|
|
|
|
|
|
std::shared_ptr<DataBuffer> buffer = std::make_shared<DataBuffer>(1 * sizeof(T), DataTypeUtils::fromT<T>(), context->getWorkspace(), true);
|
|
|
|
|
|
|
|
NDArray* res = new NDArray(buffer, ShapeDescriptor::scalarDescriptor(DataTypeUtils::fromT<T>()), context);
|
|
|
|
|
|
|
|
res->bufferAsT<T>()[0] = scalar;
|
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|
|
|
|
|
|
res->tickWriteHost();
|
|
|
|
res->syncToDevice();
|
|
|
|
|
|
|
|
return res;
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const double scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const float scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const float16 scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const bfloat16 scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const Nd4jLong scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const int scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const bool scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const int8_t scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint8_t scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint16_t scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint32_t scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const uint64_t scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const int16_t scalar, nd4j::LaunchContext * context);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
template <typename T>
|
|
|
|
NDArray NDArrayFactory::create(nd4j::DataType type, const T scalar, nd4j::LaunchContext * context) {
|
|
|
|
|
|
|
|
if (type == DataTypeUtils::fromT<T>())
|
|
|
|
return NDArrayFactory::create(scalar, context);
|
|
|
|
|
|
|
|
NDArray res(type, context);
|
|
|
|
res.p(0, scalar);
|
|
|
|
res.syncToDevice();
|
|
|
|
|
|
|
|
return res;
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
// BUILD_DOUBLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::create, (DataType type, const T scalar, nd4j::LaunchContext * context), LIBND4J_TYPES);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const double scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const float scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const float16 scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const bfloat16 scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const Nd4jLong scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const int scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const int8_t scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint8_t scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint16_t scalar, nd4j::LaunchContext* workspace);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint32_t scalar, nd4j::LaunchContext* workspace);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const uint64_t scalar, nd4j::LaunchContext* workspace);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const int16_t scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(DataType type, const bool scalar, nd4j::LaunchContext * context);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
template <typename T>
|
|
|
|
NDArray NDArrayFactory::create(const T scalar, nd4j::LaunchContext * context) {
|
|
|
|
|
|
|
|
std::shared_ptr<DataBuffer> buffer = std::make_shared<DataBuffer>(1 * sizeof(T), DataTypeUtils::fromT<T>(), context->getWorkspace(), true);
|
|
|
|
|
|
|
|
NDArray res(buffer, ShapeDescriptor::scalarDescriptor(DataTypeUtils::fromT<T>()), context);
|
|
|
|
|
|
|
|
res.bufferAsT<T>()[0] = scalar;
|
|
|
|
|
|
|
|
res.tickWriteHost();
|
|
|
|
res.syncToDevice();
|
|
|
|
|
|
|
|
return res;
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const double scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const float scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const float16 scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const bfloat16 scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const Nd4jLong scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const int scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const int8_t scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const uint8_t scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const int16_t scalar, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const uint16_t scalar, nd4j::LaunchContext* workspace);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const uint32_t scalar, nd4j::LaunchContext* workspace);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const uint64_t scalar, nd4j::LaunchContext* workspace);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const bool scalar, nd4j::LaunchContext * context);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template<typename T>
|
|
|
|
NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<T> &data, nd4j::LaunchContext * context) {
|
|
|
|
|
|
|
|
return new NDArray(NDArrayFactory::create<T>(order, shape, data, context));
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<double> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<float> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<float16> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<bfloat16> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<int> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<unsigned int> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<unsigned long> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<Nd4jLong> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<int8_t> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<uint8_t> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<int16_t> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<uint16_t> &data, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::create_(const char order, const std::vector<Nd4jLong> &shape, const std::vector<bool> &data, nd4j::LaunchContext * context);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <>
|
2019-12-02 19:37:21 +01:00
|
|
|
ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, NDArray* value, const char order, nd4j::LaunchContext * context) {
|
2019-06-06 14:21:15 +02:00
|
|
|
auto result = create_(order, shape, value->dataType(), context);
|
|
|
|
result->assign(*value);
|
|
|
|
return result;
|
|
|
|
}
|
|
|
|
|
|
|
|
template <>
|
2019-12-02 19:37:21 +01:00
|
|
|
ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, NDArray& value, const char order, nd4j::LaunchContext * context) {
|
2019-06-06 14:21:15 +02:00
|
|
|
auto result = create_(order, shape, value.dataType(), context);
|
|
|
|
result->assign(value);
|
|
|
|
return result;
|
|
|
|
}
|
|
|
|
|
|
|
|
template <typename T>
|
|
|
|
NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const T value, const char order, nd4j::LaunchContext * context) {
|
|
|
|
auto result = create_(order, shape, DataTypeUtils::fromT<T>());
|
|
|
|
result->assign(value);
|
|
|
|
return result;
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const double value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const float value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const float16 value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const bfloat16 value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const Nd4jLong value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const int value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const int16_t value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const int8_t value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const uint8_t value, const char order, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const bool value, const char order, nd4j::LaunchContext * context);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
NDArray* NDArrayFactory::linspace(const T from, const T to, const Nd4jLong numElements) {
|
|
|
|
NDArray* result = NDArrayFactory::vector<T>(numElements);
|
|
|
|
//TO DO: linspace should be executed on DEVICE, but only CPU version implemnted!
|
|
|
|
for (Nd4jLong e = 0; e < numElements; e++) {
|
|
|
|
T step = (T) e / ((T) numElements - (T) 1);
|
|
|
|
result->p<T >(e, (from * ((T) 1 - step) + step * to));
|
|
|
|
}
|
|
|
|
result->syncToDevice();
|
|
|
|
|
|
|
|
return result;
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const double from, const double to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const float from, const float to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const float16 from, const float16 to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const bfloat16 from, const bfloat16 to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const Nd4jLong from, const Nd4jLong to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const int from, const int to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const int16_t from, const int16_t to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const uint8_t from, const uint8_t to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const uint16_t from, const uint16_t to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const uint32_t from, const uint32_t to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const uint64_t from, const uint64_t to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const int8_t from, const int8_t to, const Nd4jLong numElements);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::linspace(const bool from, const bool to, const Nd4jLong numElements);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
NDArray* NDArrayFactory::vector(Nd4jLong length, const T value, nd4j::LaunchContext * context) {
|
|
|
|
|
|
|
|
std::shared_ptr<DataBuffer> buffer = std::make_shared<DataBuffer>(length * sizeof(T), DataTypeUtils::fromT<T>(), context->getWorkspace(), true);
|
|
|
|
|
|
|
|
auto res = new NDArray(buffer, ShapeDescriptor::vectorDescriptor(length, DataTypeUtils::fromT<T>()), context);
|
|
|
|
|
|
|
|
if (value == (T)0.0f)
|
|
|
|
res->nullify();
|
|
|
|
else
|
|
|
|
res->assign(value);
|
|
|
|
|
|
|
|
return res;
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const double startingValue, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const float startingValue, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const float16 startingValue, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const bfloat16 startingValue, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const Nd4jLong startingValue, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const int startingValue, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint8_t startingValue, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint16_t startingValue, nd4j::LaunchContext *workspace);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint32_t startingValue, nd4j::LaunchContext *workspace);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const uint64_t startingValue, nd4j::LaunchContext *workspace);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const int8_t startingValue, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const int16_t startingValue, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray* NDArrayFactory::vector(Nd4jLong length, const bool startingValue, nd4j::LaunchContext * context);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
NDArray NDArrayFactory::create(const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context) {
|
|
|
|
std::vector<Nd4jLong> vec(shape);
|
|
|
|
return create<T>(order, vec, context);
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::create, (const char, const std::initializer_list<Nd4jLong>&, nd4j::LaunchContext * context), LIBND4J_TYPES);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, nd4j::LaunchContext * context) {
|
|
|
|
return create(order, shape, DataTypeUtils::fromT<T>(), context);
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::create, (const char order, const std::vector<Nd4jLong> &shape, nd4j::LaunchContext * context), LIBND4J_TYPES);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
NDArray NDArrayFactory::create(const char order, const std::vector<Nd4jLong> &shape, nd4j::DataType dtype, nd4j::LaunchContext* context) {
|
|
|
|
|
|
|
|
if ((int) shape.size() > MAX_RANK)
|
|
|
|
throw std::invalid_argument("NDArrayFactory::create: rank of NDArray can't exceed 32");
|
|
|
|
|
|
|
|
ShapeDescriptor descriptor(dtype, order, shape);
|
|
|
|
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|
|
std::shared_ptr<DataBuffer> buffer = std::make_shared<DataBuffer>(descriptor.arrLength() * DataTypeUtils::sizeOfElement(dtype), dtype, context->getWorkspace());
|
|
|
|
|
|
|
|
NDArray result(buffer, descriptor, context);
|
|
|
|
|
|
|
|
result.nullify();
|
|
|
|
|
|
|
|
return result;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
NDArray NDArrayFactory::create(nd4j::DataType dtype, nd4j::LaunchContext * context) {
|
|
|
|
|
|
|
|
std::shared_ptr<DataBuffer> buffer = std::make_shared<DataBuffer>(DataTypeUtils::sizeOfElement(dtype), dtype, context->getWorkspace(), true);
|
|
|
|
|
|
|
|
NDArray res(buffer, ShapeDescriptor::scalarDescriptor(dtype), context);
|
|
|
|
|
|
|
|
res.nullify();
|
|
|
|
|
|
|
|
return res;
|
|
|
|
}
|
|
|
|
|
2019-11-08 06:49:41 +01:00
|
|
|
NDArray* NDArrayFactory::create_(nd4j::DataType dtype, nd4j::LaunchContext * context) {
|
|
|
|
auto result = new NDArray();
|
|
|
|
*result = NDArrayFactory::create(dtype, context);
|
|
|
|
return result;
|
|
|
|
}
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
NDArray NDArrayFactory::create(const std::vector<T> &values, nd4j::LaunchContext * context) {
|
|
|
|
|
|
|
|
std::shared_ptr<DataBuffer> buffer = std::make_shared<DataBuffer>(values.size() * sizeof(T), DataTypeUtils::fromT<T>(), context->getWorkspace(), true);
|
|
|
|
|
|
|
|
NDArray res(buffer, ShapeDescriptor::vectorDescriptor(values.size(), DataTypeUtils::fromT<T>()), context);
|
|
|
|
|
|
|
|
memcpyFromVector<T>(res.getBuffer(), values);
|
|
|
|
|
|
|
|
res.tickWriteHost();
|
|
|
|
res.syncToDevice();
|
|
|
|
|
|
|
|
return res;
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector<double> &values, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector<float> &values, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector<float16> &values, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector<bfloat16> &values, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector<Nd4jLong> &values, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector<int> &values, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector<int16_t> &values, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector<uint16_t> &values, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector<int8_t> &values, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector<uint8_t> &values, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(const std::vector<bool> &values, nd4j::LaunchContext * context);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
NDArray* NDArrayFactory::empty_(nd4j::LaunchContext * context) {
|
|
|
|
auto shapeInfo = ShapeBuilders::createScalarShapeInfo(DataTypeUtils::fromT<T>(), context->getWorkspace());
|
|
|
|
ArrayOptions::setPropertyBit(shapeInfo, ARRAY_EMPTY);
|
|
|
|
auto result = new NDArray(nullptr, shapeInfo, context, false);
|
|
|
|
|
|
|
|
RELEASE(shapeInfo, context->getWorkspace());
|
|
|
|
|
|
|
|
return result;
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray* NDArrayFactory::empty_, (nd4j::LaunchContext * context), LIBND4J_TYPES);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
NDArray* NDArrayFactory::empty_(nd4j::DataType dataType, nd4j::LaunchContext * context) {
|
|
|
|
if (context == nullptr)
|
|
|
|
context = nd4j::LaunchContext ::defaultContext();
|
|
|
|
|
|
|
|
auto shapeInfo = ShapeBuilders::createScalarShapeInfo(dataType, context->getWorkspace());
|
|
|
|
ArrayOptions::setPropertyBit(shapeInfo, ARRAY_EMPTY);
|
|
|
|
auto result = new NDArray(nullptr, shapeInfo, context, false);
|
|
|
|
|
|
|
|
RELEASE(shapeInfo, context->getWorkspace());
|
|
|
|
|
|
|
|
return result;
|
|
|
|
}
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
NDArray NDArrayFactory::empty(nd4j::LaunchContext * context) {
|
|
|
|
return empty(DataTypeUtils::fromT<T>(), context);
|
|
|
|
}
|
2019-12-02 19:37:21 +01:00
|
|
|
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT NDArray NDArrayFactory::empty, (nd4j::LaunchContext * context), LIBND4J_TYPES);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
NDArray NDArrayFactory::empty(nd4j::DataType dataType, nd4j::LaunchContext * context) {
|
|
|
|
auto shapeInfo = ShapeBuilders::createScalarShapeInfo(dataType, context->getWorkspace());
|
|
|
|
ArrayOptions::setPropertyBit(shapeInfo, ARRAY_EMPTY);
|
|
|
|
NDArray result(nullptr, shapeInfo, context, false);
|
|
|
|
|
|
|
|
RELEASE(shapeInfo, context->getWorkspace());
|
|
|
|
|
|
|
|
return result;
|
|
|
|
}
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
NDArray* NDArrayFactory::valueOf(const std::vector<Nd4jLong>& shape, const NDArray& value, const char order, nd4j::LaunchContext * context) {
|
|
|
|
auto res = NDArrayFactory::create_(order, shape, value.dataType(), context);
|
|
|
|
res->assign(const_cast<NDArray&>(value));
|
|
|
|
return res;
|
|
|
|
}
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
NDArray* NDArrayFactory::create_( const char order, const std::vector<Nd4jLong> &shape, nd4j::DataType dataType, nd4j::LaunchContext * context) {
|
|
|
|
|
|
|
|
return new NDArray(order, shape, dataType, context);
|
|
|
|
}
|
|
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename T>
|
|
|
|
NDArray NDArrayFactory::create(T* buffer, const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context) {
|
|
|
|
|
|
|
|
if ((int) shape.size() > MAX_RANK)
|
|
|
|
throw std::invalid_argument("NDArrayFactory::create: Rank of NDArray can't exceed 32");
|
|
|
|
|
|
|
|
std::vector<Nd4jLong> shp(shape);
|
|
|
|
ShapeDescriptor descriptor(DataTypeUtils::fromT<T>(), order, shp);
|
|
|
|
|
|
|
|
std::shared_ptr<DataBuffer> pBuffer = std::make_shared<DataBuffer>(buffer, descriptor.arrLength() * sizeof(T), descriptor.dataType(), false, context->getWorkspace());
|
|
|
|
|
|
|
|
NDArray result(pBuffer, descriptor, context);
|
|
|
|
|
|
|
|
return result;
|
|
|
|
}
|
|
|
|
|
2019-12-02 19:37:21 +01:00
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(double* buffer, const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(float* buffer, const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(float16* buffer, const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(bfloat16* buffer, const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(Nd4jLong * buffer, const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(int* buffer, const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(bool* buffer, const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(uint8_t * buffer, const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(int8_t* buffer, const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context);
|
|
|
|
template ND4J_EXPORT NDArray NDArrayFactory::create(int16_t* buffer, const char order, const std::initializer_list<Nd4jLong>& shape, nd4j::LaunchContext * context);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
|
|
|
|
|
|
|
NDArray NDArrayFactory::string(char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<const char *> &strings, nd4j::LaunchContext * context) {
|
|
|
|
std::vector<const char*> vec(strings);
|
|
|
|
return NDArrayFactory::string(order, shape, vec, context);
|
|
|
|
}
|
|
|
|
|
|
|
|
NDArray NDArrayFactory::string(char order, const std::vector<Nd4jLong> &shape, const std::vector<const char *> &strings, nd4j::LaunchContext * context) {
|
|
|
|
std::vector<std::string> vec(strings.size());
|
|
|
|
int cnt = 0;
|
|
|
|
for (auto s:strings)
|
|
|
|
vec[cnt++] = std::string(s);
|
|
|
|
|
|
|
|
return NDArrayFactory::string(order, shape, vec, context);
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
NDArray NDArrayFactory::string(char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<std::string> &string, nd4j::LaunchContext * context) {
|
|
|
|
std::vector<std::string> vec(string);
|
|
|
|
return NDArrayFactory::string(order, shape, vec, context);
|
|
|
|
}
|
|
|
|
|
|
|
|
NDArray* NDArrayFactory::string_(char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<const char *> &strings, nd4j::LaunchContext * context) {
|
|
|
|
std::vector<const char*> vec(strings);
|
|
|
|
return NDArrayFactory::string_(order, shape, vec, context);
|
|
|
|
}
|
|
|
|
|
|
|
|
NDArray* NDArrayFactory::string_(char order, const std::vector<Nd4jLong> &shape, const std::vector<const char *> &strings, nd4j::LaunchContext * context) {
|
|
|
|
std::vector<std::string> vec(strings.size());
|
|
|
|
int cnt = 0;
|
|
|
|
for (auto s:strings)
|
|
|
|
vec[cnt++] = std::string(s);
|
|
|
|
|
|
|
|
return NDArrayFactory::string_(order, shape, vec, context);
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
NDArray* NDArrayFactory::string_(char order, const std::vector<Nd4jLong> &shape, const std::initializer_list<std::string> &string, nd4j::LaunchContext * context) {
|
|
|
|
std::vector<std::string> vec(string);
|
|
|
|
return NDArrayFactory::string_(order, shape, vec, context);
|
|
|
|
}
|
|
|
|
|
|
|
|
NDArray NDArrayFactory::string(char order, const std::vector<Nd4jLong> &shape, const std::vector<std::string> &string, nd4j::LaunchContext * context) {
|
|
|
|
|
|
|
|
if (context == nullptr)
|
|
|
|
context = nd4j::LaunchContext ::defaultContext();
|
|
|
|
|
|
|
|
auto headerLength = ShapeUtils::stringBufferHeaderRequirements(string.size());
|
|
|
|
|
|
|
|
std::vector<Nd4jLong> offsets(string.size() + 1);
|
|
|
|
Nd4jLong dataLength = 0;
|
|
|
|
for (int e = 0; e < string.size(); e++) {
|
|
|
|
offsets[e] = dataLength;
|
|
|
|
dataLength += string[e].length();
|
|
|
|
}
|
|
|
|
offsets[string.size()] = dataLength;
|
|
|
|
|
|
|
|
std::shared_ptr<DataBuffer> pBuffer = std::make_shared<DataBuffer>(headerLength + dataLength, DataType::UTF8, context->getWorkspace(), true);
|
|
|
|
|
|
|
|
NDArray res(pBuffer, ShapeDescriptor(DataType::UTF8, order, shape), context);
|
|
|
|
res.setAttached(context->getWorkspace() != nullptr);
|
|
|
|
|
|
|
|
if (res.lengthOf() != string.size())
|
|
|
|
throw std::invalid_argument("Number of strings should match length of array");
|
|
|
|
|
|
|
|
memcpy(res.buffer(), offsets.data(), offsets.size() * sizeof(Nd4jLong));
|
|
|
|
|
|
|
|
auto data = static_cast<int8_t*>(res.buffer()) + headerLength;
|
|
|
|
int resLen = res.lengthOf();
|
|
|
|
for (int e = 0; e < resLen; e++) {
|
|
|
|
auto length = offsets[e+1] - offsets[e];
|
|
|
|
auto cdata = data + offsets[e];
|
|
|
|
memcpy(cdata, string[e].c_str(), string[e].length());
|
|
|
|
}
|
|
|
|
|
|
|
|
res.tickWriteHost();
|
|
|
|
res.syncToDevice();
|
|
|
|
|
|
|
|
return res;
|
|
|
|
}
|
|
|
|
|
|
|
|
NDArray* NDArrayFactory::string_(char order, const std::vector<Nd4jLong> &shape, const std::vector<std::string> &string, nd4j::LaunchContext * context) {
|
|
|
|
auto res = new NDArray();
|
|
|
|
*res = NDArrayFactory::string(order, shape, string, context);
|
|
|
|
return res;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
}
|