2019-06-06 15:21:15 +03:00
										 
									 
								 
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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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								// @author raver119@gmail.com
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								//
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								#include <Environment.h>
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								#include <loops/transform_same.h>
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								#include <types/types.h>
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								#include <op_boilerplate.h>
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								#include <loops/legacy_ops.h>
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								#include <helpers/DebugHelper.h>
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								using namespace simdOps;
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								template <typename X, typename OpType>
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								__global__ void transformSameSimple(void *x, Nd4jLong *xShapeInfo, int xRank,
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																void *params,
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																void *z, Nd4jLong *zShapeInfo, int zRank,
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																int *allocationPointer,
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																void *reductionPointer,
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																Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
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									functions::transform::TransformSame<X>::template transformCuda<OpType>(x,xShapeInfo,params,z,zShapeInfo,allocationPointer,reductionPointer, tadShapeInfo, tadOffsets);
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								}
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								namespace functions {
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								    namespace transform {
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								        template<typename X>
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								        _CUDA_H void TransformSame<X>::executeTransformShaped(dim3 launchDims, cudaStream_t *stream, int opNum, void *x, Nd4jLong *xShape, int xRank, void *extraParams, void *z, Nd4jLong *zShape, int zRank, int *allocationPointer, void *reductionPointer,  Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
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											DISPATCH_BY_OPNUM_T(intermediateShaped, PARAMS(launchDims, stream, x, xShape, xRank, extraParams, z, zShape, zRank, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets), TRANSFORM_SAME_OPS);
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								            DEBUG_KERNEL(stream, opNum);
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								        }
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								        template<typename X>
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								        template <typename OpType>
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								        __device__ void TransformSame<X>::transformCuda(void *vx, Nd4jLong *xShapeInfo,
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								        												void *vparams,
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								        												void *vz, Nd4jLong *zShapeInfo,
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								        												int *allocationPointer, void *vreductionPointer,
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								        												Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
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								        	auto x = static_cast<X*>(vx);
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										    auto z = static_cast<X*>(vz);
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										    auto params = static_cast<X*>(vparams);
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										    auto reductionPointer = static_cast<X*>(vreductionPointer);
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										    if(OpType::requiresSpecial) {
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											    OpType::execSpecialCuda(x,xShapeInfo,z,zShapeInfo,params, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets);
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											    return;
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										    } else {
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										    	__shared__ Nd4jLong xEws;
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								    	        __shared__ Nd4jLong zEws;
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								        	    __shared__ char xOrder;
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								            	__shared__ char zOrder;
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								            	__shared__ Nd4jLong length;
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									            if (threadIdx.x == 0) {
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								        	        xEws = shape::elementWiseStride(xShapeInfo);
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								            	    zEws = shape::elementWiseStride(zShapeInfo);
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								                	xOrder = shape::order(xShapeInfo);
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													zOrder = shape::order(zShapeInfo);
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													length = shape::length(xShapeInfo);
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								            	}
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								            	__syncthreads();
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									    	    auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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												int totalThreads = gridDim.x * blockDim.x;
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												Oleh tenzor mmul (#231)
* Libnd4j: TensorMMul backprop op #8174, raw implementation
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* Libnd4j: TensorMMul backprop op #8174 merge master and some corrections
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* Libnd4j: TensorMMul backprop op #8174 algorithm update, need testing, sync with  master
* Libnd4j: TensorMMul backprop op #8174 fixed incorrect B axes calculation
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* Libnd4j: TensorMMul backprop op #8174 optimize axes identification and fix bug of indeces overlapping, added first test. need testing with different shapes
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* Libnd4j: TensorMMul backprop op #8174 some fixes and improvements need more testing
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* Libnd4j: TensorMMul backprop op #8174 fixed order of matrix multiply
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* Libnd4j: TensorMMul backprop op #8174 fixed issue of incorrect axes definition, add tests based on TF, need additional testing for case dLdC not equal 1
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* Libnd4j: TensorMMul backprop op #8174 fixed scalar case add test
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* Libnd4j: TensorMMul backprop op #8174 fixed bp algorithm, axes definition, need some mode testing with different orders combination f,c; c,f f,f and add some checks for inputs
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* Libnd4j: TensorMMul backprop op #8174 some checks and corrections added tests, exists the problem with different input orders support A-f B-c and A-f B-f
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* Libnd4j: TensorMMul backprop op #8174 sync master
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* - correct bug in MmulHelper::tensorDot(a, b, c, axes_a, axes_b,permutForC)
Signed-off-by: Yurii <iuriish@yahoo.com>
* Libnd4j: TensorMMul backprop op #8174 code clean up and refactoring
Signed-off-by: Oleg <oleg.semeniv@gmail.com>
* - add check for linspase ordered permutations in ShapeUtils::evalShapeForTensorDot
Signed-off-by: Yurii <iuriish@yahoo.com>
* - provide additional code in shape::reshape stuff in order to reduce amount of allocation/copy operations during reshaping procedure
Signed-off-by: Yurii <iuriish@yahoo.com>
* - further work on problem of wrong shape evaluation during permute/reshape procedures
Signed-off-by: Yurii <iuriish@yahoo.com>
* - still looking for bug reason in reshape/permute stuff
Signed-off-by: Yurii <iuriish@yahoo.com>
* - correct bug in transform cuda native ops
Signed-off-by: Yurii <iuriish@yahoo.com>
* - correct bug in NDArray::assign
Signed-off-by: Yurii <iuriish@yahoo.com>
* - remove old shape::reshape stuff
Signed-off-by: Yurii <iuriish@yahoo.com>
* - add possibility to disable copy of old buffer to new buffer during reshape operation in NDArray class
Signed-off-by: Yurii <iuriish@yahoo.com>
* - correct bug in tensorDot which had to do with wrong pointers assigments
Signed-off-by: Yurii <iuriish@yahoo.com>
Co-authored-by: Oleh <oleg.semeniv@gmail.com>
											
										 
										
											2020-02-13 19:33:54 +02:00
										 
									 
								 
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										        if(xEws > 0 && zEws > 0 && xOrder == zOrder && xOrder == 'c') {
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											2019-06-06 15:21:15 +03:00
										 
									 
								 
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													for (int i = tid; i < length; i += totalThreads)
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														z[i * zEws] = OpType::op(x[i * xEws], params);
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										        }
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										        else {
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													if(vx == vz) {
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											2019-09-02 11:25:48 +03:00
										 
									 
								 
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														for (Nd4jLong i = tid; i < length; i+= totalThreads) {
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											2019-09-11 20:12:09 +03:00
										 
									 
								 
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															auto xOffset = shape::getIndexOffset(i, xShapeInfo);
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											2019-06-06 15:21:15 +03:00
										 
									 
								 
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									    			    	z[xOffset] = OpType::op(x[xOffset], params);
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										    	    	}
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													}
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													else {
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											2019-09-02 11:25:48 +03:00
										 
									 
								 
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										    	    	for (Nd4jLong i = tid; i < length; i+= totalThreads) {
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											2019-09-11 20:12:09 +03:00
										 
									 
								 
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															auto xOffset = shape::getIndexOffset(i, xShapeInfo);
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															auto zOffset = shape::getIndexOffset(i, zShapeInfo);
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											2019-06-06 15:21:15 +03:00
										 
									 
								 
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									    			    	z[zOffset] = OpType::op(x[xOffset], params);
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										    	    	}
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										    		}
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										        }
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									        }
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									    };
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										template<typename X>
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										template <typename OpType>
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										_CUDA_H void TransformSame<X>::intermediateShaped(dim3 launchDims, cudaStream_t *stream, void *x, Nd4jLong *xShape, int xRank, void *extraParams, void *z, Nd4jLong *zShape, int zRank, int *allocationPointer, void *reductionPointer,  Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
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											transformSameSimple<X, OpType><<<launchDims.x, launchDims.x, launchDims.z, *stream>>>(x, xShape, xRank, extraParams, z, zShape, zRank, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets);
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								            nd4j::DebugHelper::checkErrorCode(stream, "transformSame(...) failed");
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										}
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								        BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT TransformSame, , LIBND4J_TYPES);
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								    }
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								}
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