cavis/libnd4j/include/helpers/cpu/MmulHelper.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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* 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

348 lines
15 KiB
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
******************************************************************************/
//
// @author raver119@gmail.com
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include "../MmulHelper.h"
#include <NDArrayFactory.h>
#include <helpers/BlasHelper.h>
#include <exceptions/datatype_exception.h>
namespace nd4j {
//////////////////////////////////////////////////////////////////////////////
// MXK x KxN = MxN
template <typename T1, typename T2, typename T3>
static void usualGemm(const char cOrder, const bool transA, const bool transB, const int M, const int N, const int K, const double alpha, const void* vA, const int lda, const void* vB, const int ldb, const double beta, void* vC, const int ldc) {
T1* A = reinterpret_cast<T1*>(const_cast<void*>(vA));
T2* B = reinterpret_cast<T2*>(const_cast<void*>(vB));
T3* C = reinterpret_cast<T3*>(vC);
T3 alphaZ(alpha), betaZ(beta);
const bool flagC = cOrder == 'f';
const bool flagA = (flagC && transA) || (!flagC && !transA);
const bool flagB = (flagC && transB) || (!flagC && !transB);
// PRAGMA_OMP_PARALLEL_FOR_ARGS(if(M*N > Environment::getInstance()->elementwiseThreshold()) schedule(guided))
// for(uint row = 0; row < M; ++row) {
// T3* c = flagC ? (C + row) : (C + row * ldc);
// for(uint col = 0; col < N; ++col)
// c[flagC ? col * ldc : col] = 0;
// for(uint i = 0; i < K; ++i) {
// T3* b = flagB ? (B + i * ldb) : (B + i);
// T3* a = flagA ? (A + row * lda + i) : (A + row + i * lda);
// if(flagC) {
// PRAGMA_OMP_SIMD
// for(uint col = 0; col < N; ++col) {
// if(betaZ)
// c[col * ldc] += a * b[flagB ? col : col * ldb] + betaZ * c[col * ldc];
// else
// c[col * ldc] += a * b[flagB ? col : col * ldb];
// }
// }
// else {
// PRAGMA_OMP_SIMD
// for(uint col = 0; col < N; ++col) {
// if(betaZ)
// c[col] += a * b[flagB ? col : col * ldb] + betaZ * c[col];
// else
// c[col] += a * b[flagB ? col : col * ldb];
// }
// }
// }
// }
PRAGMA_OMP_PARALLEL_FOR_ARGS(if(M*N > Environment::getInstance()->elementwiseThreshold()) schedule(guided) collapse(2))
for(uint row = 0; row < M; ++row) {
for(uint col = 0; col < N; ++col) {
T3* c = flagC ? (C + row + col * ldc) : (C + row * ldc + col);
T3 val = 0;
PRAGMA_OMP_SIMD
for(uint i = 0; i < K; ++i) {
T3 a = flagA ? *(A + row * lda + i) : *(A + row + i * lda);
T3 b = flagB ? *(B + col + i * ldb) : *(B + col * ldb + i);
val += alphaZ * a * b;
}
if(betaZ)
*c = val + betaZ * *c;
else
*c = val;
}
}
}
//////////////////////////////////////////////////////////////////////////////
// MXN x N = M
template <typename T1, typename T2, typename T3>
static void usualGemv(const char aOrder, const int M, const int N, const double alpha, const void* vA, const int lda, const void* vX, const int incx, const double beta, void* vY, const int incy) {
T1* A = reinterpret_cast<T1*>(const_cast<void*>(vA));
T2* X = reinterpret_cast<T2*>(const_cast<void*>(vX));
T3* Y = reinterpret_cast<T3*>(vY);
T3 alphaZ(alpha), betaZ(beta);
const bool flagA = aOrder == 'f';
PRAGMA_OMP_PARALLEL_FOR_ARGS(if(M > Environment::getInstance()->elementwiseThreshold()) schedule(guided))
for(int row = 0; row < M; ++row) {
T3* y = Y + row * incy;
T3 val = 0;
PRAGMA_OMP_SIMD
for(int i = 0; i < N; ++i) {
T3 a = flagA ? *(A + row + i * lda) : *(A + row * lda + i);
T3 x = *(X + i * incx);
val += alphaZ * a * x;
}
if(betaZ)
*y = val + betaZ * *y;
else
*y = val;
}
}
//////////////////////////////////////////////////////////////////////////////
// (X*Y) = Z[0]
template <typename T1, typename T2, typename T3>
static void usualDot(const Nd4jLong length, const double alpha, const void* vX, const Nd4jLong incx, const void* vY, const Nd4jLong incy, const double beta, void* vZ) {
T1* X = reinterpret_cast<T1*>(const_cast<void*>(vX));
T2* Y = reinterpret_cast<T2*>(const_cast<void*>(vY));
T3* Z = reinterpret_cast<T3*>(vZ);
T3 alphaZ(alpha), betaZ(beta);
T3 sum = 0;
PRAGMA_OMP_PARALLEL_FOR_ARGS(if(length > Environment::getInstance()->elementwiseThreshold()) schedule(guided) reduction(OMP_SUMT:sum))
for(int i = 0; i < length; ++i)
sum = sum + X[i * incx] * Y[i * incy];
*Z = alphaZ * sum + betaZ * *Z;
}
//////////////////////////////////////////////////////////////////////////////
// MXK x KxN = MxN
NDArray* MmulHelper::mmulMxM(const NDArray* A, const NDArray* B, NDArray* C, const double alpha, const double beta, const char outOrder) {
if (A->dataType() != B->dataType())
throw datatype_exception::build("mmulMxM expects all data types to be the same", A->dataType(), B->dataType());
if (C != nullptr && A->dataType() != C->dataType())
throw datatype_exception::build("mmulMxM expects all data types to be the same", A->dataType(), C->dataType());
if(A->rankOf() != 2)
throw std::runtime_error("MmulHelper::mmulMxM: rank of A array is not equal 2 !");
if(B->rankOf() != 2)
throw std::runtime_error("MmulHelper::mmulMxM: rank of B array is not equal 2 !");
const auto M = A->sizeAt(0);
const auto K = A->sizeAt(1);
const auto N = B->sizeAt(1);
const auto bRows = B->sizeAt(0);
if(C != nullptr && C->rankOf() != 2)
throw std::runtime_error("MmulHelper::mmulMxM: rank of C array is not equal 2 !");
if(bRows != K)
throw std::runtime_error("MmulHelper::mmulMxM: B array has wrong number of rows !");
if(C != nullptr && C->sizeAt(0) != M)
throw std::runtime_error("MmulHelper::mmulMxM: C array has wrong number of rows !");
if(C != nullptr && C->sizeAt(1) != N)
throw std::runtime_error("MmulHelper::mmulMxM: C array has wrong number of columns !");
if(C == nullptr)
C = new NDArray(outOrder, {M,N}, DataTypeUtils::pickPairwiseResultType(A->dataType(), B->dataType()), A->getContext());
NDArray *pA(const_cast<NDArray*>(A)), *pB(const_cast<NDArray*>(B)), *pC(const_cast<NDArray*>(C));
const auto cOrder = C->ordering();
if(A->ews() != 1)
pA = pA->dup(cOrder);
if(B->ews() != 1)
pB = pB->dup(cOrder);
if(C->ews() != 1)
pC = pC->dup(cOrder);
const auto aOrder = pA->ordering();
const auto bOrder = pB->ordering();
const bool transA = aOrder != cOrder;
const bool transB = bOrder != cOrder;
const CBLAS_ORDER blasOrder = cOrder == 'f' ? CblasColMajor : CblasRowMajor;
const CBLAS_TRANSPOSE transAblas = transA ? CblasTrans : CblasNoTrans;
const CBLAS_TRANSPOSE transBblas = transB ? CblasTrans : CblasNoTrans;
const int lda = aOrder == 'f' ? M : K;
const int ldb = bOrder == 'f' ? K : N;
const int ldc = cOrder == 'f' ? M : N;
const auto aType = pA->dataType();
const auto bType = pB->dataType();
const auto cType = pC->dataType();
const bool AB(aType == bType), AC(aType == cType), ABC(AB && AC);
const bool hasGemm = BlasHelper::getInstance()->hasGEMM(aType);
// we'll use platform-specific gemm here eventually. maybe tomorrow.
// TODO: put proper _gemm here
if (ABC && hasGemm && aType == DataType::FLOAT32) {
BlasHelper::getInstance()->sgemm()(blasOrder, transAblas, transBblas, M, N, K, (float) alpha, reinterpret_cast<float *>(pA->getBuffer()), lda, reinterpret_cast<float *>(pB->getBuffer()), ldb, (float) beta, reinterpret_cast<float *>(pC->getBuffer()), ldc);
}
else if (ABC && hasGemm && aType == DataType::DOUBLE) {
BlasHelper::getInstance()->dgemm()(blasOrder, transAblas, transBblas, M, N, K, (double) alpha, reinterpret_cast<double *>(pA->getBuffer()), lda, reinterpret_cast<double *>(pB->getBuffer()), ldb, (double) beta, reinterpret_cast<double *>(pC->getBuffer()), ldc);
}
else {
BUILD_SINGLE_SELECTOR_THRICE(aType, usualGemm, (cOrder, transA, transB, M, N, K, alpha, pA->getBuffer(), lda, pB->getBuffer(), ldb, beta, pC->getBuffer(), ldc), NUMERIC_TYPES);
//BUILD_TRIPLE_SELECTOR(aType, bType, cType, usualGemm, (cOrder, transA, transB, M, N, K, alpha, pA->getBuffer(), lda, pB->getBuffer(), ldb, beta, pC->getBuffer(), ldc), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
}
if(pC != C) {
C->assign(pC);
delete pC;
}
if(pA != A)
delete pA;
if(pB != B)
delete pB;
return C;
}
////////////////////////////////////////////////////////////////////////////
// MXN x N = M
NDArray* MmulHelper::mmulMxV(const NDArray* A, const NDArray* X, nd4j::NDArray* Y, const double alpha, const double beta, const char outOrder) {
if (X->dataType() != A->dataType())
throw datatype_exception::build("mmulMxV expects all data types to be the same", A->dataType(), X->dataType());
if (Y != nullptr && X->dataType() != Y->dataType())
throw datatype_exception::build("mmulMxV expects all data types to be the same", A->dataType(), Y->dataType());
int xLenDim, yLenDim(0);
if(A->rankOf() != 2)
throw std::runtime_error("MmulHelper::mmulMxV: rank of A array is not equal 2 !");
if(!shape::isCommonVector(X->getShapeInfo(), xLenDim))
throw std::runtime_error("MmulHelper::mmulMxV: X array must be vector !");
const auto M = A->sizeAt(0);
const auto N = A->sizeAt(1);
if(Y != nullptr && !shape::isCommonVector(Y->getShapeInfo(), yLenDim))
throw std::runtime_error("MmulHelper::mmulMxV: Y array must be vector !");
if(X->lengthOf() != N)
throw std::runtime_error("MmulHelper::mmulMxV: X vector has wrong length !");
if(Y != nullptr && Y->lengthOf() != M)
throw std::runtime_error("MmulHelper::mmulMxV: Y array has wrong length !");
if(Y == nullptr)
Y = new NDArray(outOrder, {M}, DataTypeUtils::pickPairwiseResultType(A->dataType(), X->dataType()), A->getContext());
NDArray *pA(const_cast<NDArray*>(A));
if(A->ews() != 1)
pA = pA->dup();
CBLAS_ORDER blasOrder;
int lda;
if (pA->ordering() == 'f') {blasOrder = CblasColMajor; lda = M; }
else {blasOrder = CblasRowMajor; lda = N; }
const int incx = X->stridesOf()[xLenDim];
const int incy = Y->stridesOf()[yLenDim];
const auto aType = pA->dataType();
const auto xType = X->dataType();
const auto yType = Y->dataType();
const bool AX(aType == xType), AY(aType == yType), AXY(AX && AY);
const bool hasGemv = BlasHelper::getInstance()->hasGEMV(aType);
// choose appropriate cuda gemm api depending on data types
if(AXY && hasGemv && aType == DataType::DOUBLE) {
BlasHelper::getInstance()->dgemv()(blasOrder, CblasNoTrans, M, N, alpha, (double*)pA->getBuffer(), lda, (double*)X->getBuffer(), incx, beta, (double*)Y->getBuffer(), incy);
}
else if(AXY && hasGemv && aType == DataType::FLOAT32) {
BlasHelper::getInstance()->sgemv()(blasOrder, CblasNoTrans, M, N, (float)alpha, (float*)pA->getBuffer(), lda, (float*)X->getBuffer(), incx, (float)beta, (float*)Y->getBuffer(), incy);
}
else {
BUILD_SINGLE_SELECTOR_THRICE(aType, usualGemv, (pA->ordering(), M, N, alpha, pA->getBuffer(), lda, X->getBuffer(), incx, beta, Y->getBuffer(), incy), NUMERIC_TYPES);
//BUILD_TRIPLE_SELECTOR(aType, xType, yType, usualGemv, (pA->ordering(), M, N, alpha, pA->getBuffer(), lda, X->getBuffer(), incx, beta, Y->getBuffer(), incy), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
}
if(pA != A)
delete pA;
return Y;
}
////////////////////////////////////////////////////////////////////////////
// (X * Y) = Z[0]
NDArray* MmulHelper::dot(const NDArray* X, const NDArray* Y, nd4j::NDArray* Z, const double alpha, const double beta) {
if (X->dataType() != Y->dataType())
throw datatype_exception::build("Dot expects all data types to be the same", X->dataType(), Y->dataType());
if (Z != nullptr && X->dataType() != Z->dataType())
throw datatype_exception::build("Dot expects all data types to be the same", X->dataType(), Z->dataType());
int xLenDim(0), yLenDim(0);
if(!shape::isCommonVector(X->getShapeInfo(), xLenDim))
throw std::runtime_error("MmulHelper::dot cuda: X array must be vector !");
if(!shape::isCommonVector(Y->getShapeInfo(), yLenDim))
throw std::runtime_error("MmulHelper::dot cuda: Y array must be vector !");
if(Z != nullptr && !Z->isScalar())
throw std::runtime_error("MmulHelper::dot cuda: Z array must be scalar !");
const auto length = X->lengthOf();
if(Y->lengthOf() != length)
throw std::runtime_error("MmulHelper::dot cuda: lengths of input vectors are different !");
if(Z == nullptr)
Z = new NDArray(DataTypeUtils::pickPairwiseResultType(X->dataType(), Y->dataType()), X->getContext());
const Nd4jLong incx = X->stridesOf()[xLenDim];
const Nd4jLong incy = Y->stridesOf()[yLenDim];
const auto xType = X->dataType();
const auto yType = Y->dataType();
const auto zType = Z->dataType();
BUILD_SINGLE_SELECTOR_THRICE(xType, usualDot, (length, alpha, X->getBuffer(), incx, Y->getBuffer(), incy, beta, Z->getBuffer()), NUMERIC_TYPES);
//BUILD_TRIPLE_SELECTOR(xType, yType, zType, usualDot, (length, alpha, X->getBuffer(), incx, Y->getBuffer(), incy, beta, Z->getBuffer()), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
return Z;
}
//BUILD_TRIPLE_TEMPLATE(template void usualGemm, (const char cOrder, const bool transA, const bool transB, const int M, const int N, const int K, const double alpha, const void* A, const int lda, const void* B, const int ldb, const double beta, void* C, const int ldc), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
//BUILD_TRIPLE_TEMPLATE(template void usualGemv, (const char aOrder, const int M, const int N, const double alpha, const void* A, const int lda, const void* B, const int incx, const double beta, void* C, const int incy), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
//BUILD_TRIPLE_TEMPLATE(template void usualDot, (const Nd4jLong length, const double alpha, const void* vX, const Nd4jLong incx, const void* vY, const Nd4jLong incy, const double beta, void* vZ), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
}