2021-02-01 13:31:45 +01:00
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/* ******************************************************************************
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*
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2019-06-06 14:21:15 +02:00
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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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2021-02-01 13:31:45 +01:00
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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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2019-06-06 14:21:15 +02:00
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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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2020-03-02 10:49:41 +01:00
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#include <system/op_boilerplate.h>
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2019-06-06 14:21:15 +02:00
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#include <loops/broadcasting_bool.h>
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#include <loops/legacy_ops.h>
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#include <types/types.h>
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2020-03-02 10:49:41 +01:00
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#include <system/Environment.h>
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2019-06-06 14:21:15 +02:00
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#include <cuda.h>
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#include <cuda_runtime.h>
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#include <string>
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#include <stdexcept>
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2020-03-02 10:49:41 +01:00
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#include <helpers/StringUtils.h>
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2019-06-06 14:21:15 +02:00
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using namespace simdOps;
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Z, typename OpClass>
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static __global__ void broadcastBoolSimple(
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2020-05-09 07:06:14 +02:00
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void const* x,
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Nd4jLong const* xShapeInfo,
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void const* y,
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Nd4jLong const* yShapeInfo,
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2019-06-06 14:21:15 +02:00
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void *z,
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2020-05-09 07:06:14 +02:00
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Nd4jLong const* zShapeInfo,
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2019-11-21 13:43:03 +01:00
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void *extraParams,
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2019-06-06 14:21:15 +02:00
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int *dimension,
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2020-05-09 07:06:14 +02:00
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int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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2019-09-11 19:12:09 +02:00
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2019-11-21 13:43:03 +01:00
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functions::broadcast::BroadcastBool<X, Z>::template transformCuda<OpClass>(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo, extraParams, dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ);
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2019-06-06 14:21:15 +02:00
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}
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2020-03-10 14:29:09 +01:00
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Z, typename OpClass>
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2020-05-09 07:06:14 +02:00
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static __global__ void broadcastBoolSimple(const void const* x, const Nd4jLong const* xShapeInfo,
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const void const* y, const Nd4jLong const* yShapeInfo,
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void *z, const Nd4jLong const* zShapeInfo,
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2020-03-10 14:29:09 +01:00
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void *extraParams) {
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functions::broadcast::BroadcastBool<X, Z>::template transformCuda<OpClass>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams);
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}
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2019-06-06 14:21:15 +02:00
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Z, typename OpClass>
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static __global__ void broadcastBoolInverseSimple(
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void const* x,
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Nd4jLong const* xShapeInfo,
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void const* y,
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Nd4jLong const* yShapeInfo,
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void *z,
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Nd4jLong const* zShapeInfo,
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void *extraParams,
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int *dimension,
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2020-05-09 07:06:14 +02:00
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int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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2019-06-06 14:21:15 +02:00
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2019-11-21 13:43:03 +01:00
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functions::broadcast::BroadcastBool<X, Z>::template transformInverseCuda<OpClass>(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo,extraParams,dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ);
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2019-06-06 14:21:15 +02:00
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}
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namespace functions {
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2020-03-10 14:29:09 +01:00
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namespace broadcast {
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2019-06-06 14:21:15 +02:00
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Z>
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template <typename OpClass>
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2020-05-09 07:06:14 +02:00
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__host__ void BroadcastBool<X,Z>::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream, void const* x, Nd4jLong const* xShapeInfo, void const* y, Nd4jLong const* yShapeInfo, void* z, Nd4jLong const* zShapeInfo, void *extraParams, int *dimension, int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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2020-03-10 14:29:09 +01:00
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broadcastBoolSimple<X, Z, OpClass><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ);
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sd::DebugHelper::checkErrorCode(stream, "intermediateBroadcastBool(...) failed");
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}
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2019-06-06 14:21:15 +02:00
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//////////////////////////////////////////////////////////////////////////
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2020-03-10 14:29:09 +01:00
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template<typename X, typename Z>
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template <typename OpClass>
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__host__ void BroadcastBool<X,Z>::intermediateBroadcast(dim3 launchDims, cudaStream_t *stream,
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const void *x, const Nd4jLong *xShapeInfo,
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const void *y, const Nd4jLong *yShapeInfo,
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void *z, const Nd4jLong *zShapeInfo,
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void *extraParams) {
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broadcastBoolSimple<X, Z, OpClass><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams);
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sd::DebugHelper::checkErrorCode(stream, "intermediateBroadcastBool(...) failed");
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}
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2019-06-06 14:21:15 +02:00
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2020-03-10 14:29:09 +01:00
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y>
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2020-05-09 07:06:14 +02:00
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__host__ void BroadcastBool<X,Y>::execBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void const* x, Nd4jLong const* xShapeInfo, void const* y, Nd4jLong const* yShapeInfo, void *z, Nd4jLong const* zShapeInfo, void *extraParams, int *dimension, int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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2020-03-10 14:29:09 +01:00
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DISPATCH_BY_OPNUM_TT(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_BOOL_OPS))
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DEBUG_KERNEL(stream, opNum);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y>
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__host__ void BroadcastBool<X,Y>::execBroadcast(dim3 launchDims, cudaStream_t *stream, const int opNum,
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const void *x, const Nd4jLong *xShapeInfo,
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const void *y, const Nd4jLong *yShapeInfo,
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void *z, const Nd4jLong *zShapeInfo,
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void *extraParams) {
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DISPATCH_BY_OPNUM_TT(intermediateBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams), OPS_A(BROADCAST_BOOL_OPS))
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DEBUG_KERNEL(stream, opNum);
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}
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2019-06-06 14:21:15 +02:00
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Z>
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template <typename OpClass>
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2020-05-09 07:06:14 +02:00
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__host__ void BroadcastBool<X,Z>::intermediateInverseBroadcast(dim3 launchDims, cudaStream_t *stream, void const* x, Nd4jLong const* xShapeInfo, void const* y, Nd4jLong const* yShapeInfo, void *z, Nd4jLong const* zShapeInfo, void *extraParams, int *dimension, int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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2019-11-21 13:43:03 +01:00
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broadcastBoolInverseSimple<X, Z, OpClass><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ);
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2020-03-02 10:49:41 +01:00
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sd::DebugHelper::checkErrorCode(stream, "intermediateBroadcastBool(...) failed");
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2019-06-06 14:21:15 +02:00
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y>
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2020-05-09 07:06:14 +02:00
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__host__ void BroadcastBool<X,Y>::execInverseBroadcast(dim3 launchDims, cudaStream_t *stream, int opNum, void const* x, Nd4jLong const* xShapeInfo, void const* y, Nd4jLong const* yShapeInfo, void *z, Nd4jLong const* zShapeInfo, void *extraParams, int *dimension, int dimensionLength, Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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2019-11-21 13:43:03 +01:00
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DISPATCH_BY_OPNUM_TT(intermediateInverseBroadcast, PARAMS(launchDims, stream, x, xShapeInfo, y, yShapeInfo, z, zShapeInfo, extraParams, dimension, dimensionLength, tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ), OPS_A(BROADCAST_BOOL_OPS))
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2019-06-06 14:21:15 +02:00
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DEBUG_KERNEL(stream, opNum);
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Z>
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template <typename OpType>
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__device__ void BroadcastBool<X,Z>::transformInverseCuda(
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2020-05-09 07:06:14 +02:00
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void const* vx, Nd4jLong const* xShapeInfo,
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void const* vy, Nd4jLong const* yShapeInfo,
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void *vz, Nd4jLong const* zShapeInfo,
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2019-11-21 13:43:03 +01:00
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void *vextraParams,
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2019-06-06 14:21:15 +02:00
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int *dimension, int dimensionLength,
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2020-05-09 07:06:14 +02:00
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Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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2019-06-06 14:21:15 +02:00
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if (tadOnlyShapeInfoZ == nullptr) {
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tadOnlyShapeInfoZ = tadOnlyShapeInfo;
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tadOffsetsZ = tadOffsets;
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}
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2020-05-09 07:06:14 +02:00
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auto x = reinterpret_cast<X const*>(vx);
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auto y = reinterpret_cast<X const*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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2019-11-21 13:43:03 +01:00
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auto extraParams = reinterpret_cast<X*>(vextraParams);
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2019-06-06 14:21:15 +02:00
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//decompose in to several sub tads after
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//moving all dimensions (in sorted order)
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//to the back.
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//permuted version of the x shape info for setting up the tad problem
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__shared__ Nd4jLong tadLength;
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__shared__ Nd4jLong tadEWS;
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__shared__ int numTads;
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__shared__ Nd4jLong xEWS;
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__shared__ Nd4jLong zEWS;
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if (threadIdx.x == 0) {
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tadLength = shape::length(tadOnlyShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
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tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
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2019-08-02 19:01:03 +02:00
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numTads = shape::length(yShapeInfo) / tadLength;
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2019-06-06 14:21:15 +02:00
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xEWS = shape::elementWiseStride(xShapeInfo);
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zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ);
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}
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__syncthreads();
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for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
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auto rZ = z + tadOffsetsZ[r];
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auto rY = y + tadOffsets[r];
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if(tadEWS > 0 && zEWS > 0 && xEWS > 0 && dimensionLength == 1) {
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for (int i = threadIdx.x; i < tadLength; i+= blockDim.x)
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2019-11-21 13:43:03 +01:00
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rZ[i * zEWS] = OpType::op(x[i * xEWS], rY[i * tadEWS], extraParams);
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}
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else {
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// it is expected that x and z tads and y array all have the same length
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for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
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2019-09-11 19:12:09 +02:00
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auto xOffset = shape::getIndexOffset(i, xShapeInfo);
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auto yOffset = shape::getIndexOffset(i, tadOnlyShapeInfo);
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auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ);
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2019-11-21 13:43:03 +01:00
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rZ[zOffset] = OpType::op(x[xOffset], rY[yOffset], extraParams);
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2019-06-06 14:21:15 +02:00
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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, typename Z>
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template <typename OpType>
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__device__ void BroadcastBool<X,Z>::transformCuda(
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2020-05-09 07:06:14 +02:00
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void const* vx, Nd4jLong const* xShapeInfo,
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void const* vy, Nd4jLong const* yShapeInfo,
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void *vz, Nd4jLong const* zShapeInfo,
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2019-11-21 13:43:03 +01:00
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void *vextraParams,
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2019-06-06 14:21:15 +02:00
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int *dimension, int dimensionLength,
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2020-05-09 07:06:14 +02:00
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Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets, Nd4jLong const* tadOnlyShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
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2019-06-06 14:21:15 +02:00
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if (tadOnlyShapeInfoZ == nullptr) {
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tadOnlyShapeInfoZ = tadOnlyShapeInfo;
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tadOffsetsZ = tadOffsets;
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}
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2020-05-09 07:06:14 +02:00
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auto x = reinterpret_cast<X const*>(vx);
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auto y = reinterpret_cast<X const*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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2019-11-21 13:43:03 +01:00
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auto extraParams = reinterpret_cast<X*>(vextraParams);
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2019-06-06 14:21:15 +02:00
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//decompose in to several sub tads after
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//moving all dimensions (in sorted order)
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//to the back.
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//permuted version of the x shape info for setting up the tad problem
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__shared__ Nd4jLong tadLength;
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__shared__ Nd4jLong tadEWS;
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__shared__ int numTads;
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__shared__ Nd4jLong yEWS;
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__shared__ Nd4jLong zEWS;
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2019-06-06 14:21:15 +02:00
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if (threadIdx.x == 0) {
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tadLength = shape::length(tadOnlyShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
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tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
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numTads = shape::length(xShapeInfo) / tadLength;
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yEWS = shape::elementWiseStride(yShapeInfo);
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2019-09-11 19:12:09 +02:00
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zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ);
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2019-06-06 14:21:15 +02:00
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}
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__syncthreads();
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__shared__ Z *rZ;
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__shared__ X const* rX;
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for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
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if (threadIdx.x == 0) {
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rZ = z + tadOffsetsZ[r];
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rX = x + tadOffsets[r];
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}
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__syncthreads();
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if(tadEWS > 0 && zEWS > 0 && yEWS > 0 && dimensionLength == 1) {
|
|
|
|
|
|
|
|
for (int i = threadIdx.x; i < tadLength; i+= blockDim.x)
|
2019-11-21 13:43:03 +01:00
|
|
|
rZ[i * zEWS] = OpType::op(rX[i * tadEWS], y[i * yEWS], extraParams);
|
2019-06-06 14:21:15 +02:00
|
|
|
}
|
|
|
|
else {
|
|
|
|
// it is expected that x and z tads and y array all have the same length
|
|
|
|
for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
|
2019-09-11 19:12:09 +02:00
|
|
|
auto xOffset = shape::getIndexOffset(i, tadOnlyShapeInfo);
|
|
|
|
auto yOffset = shape::getIndexOffset(i, yShapeInfo);
|
|
|
|
auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ);
|
2019-06-06 14:21:15 +02:00
|
|
|
|
2019-11-21 13:43:03 +01:00
|
|
|
rZ[zOffset] = OpType::op(rX[xOffset], y[yOffset], extraParams);
|
2019-06-06 14:21:15 +02:00
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2020-03-10 14:29:09 +01:00
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
|
|
template<typename X, typename Z>
|
|
|
|
template <typename OpType>
|
|
|
|
__device__ void BroadcastBool<X,Z>::transformCuda(const void *vx, const Nd4jLong *xShapeInfo,
|
|
|
|
const void *vy, const Nd4jLong *yShapeInfo,
|
|
|
|
void *vz, const Nd4jLong *zShapeInfo,
|
|
|
|
void *vextraParams) {
|
|
|
|
|
|
|
|
const X* x = reinterpret_cast<const X*>(vx);
|
|
|
|
const X* y = reinterpret_cast<const X*>(vy);
|
|
|
|
Z* z = reinterpret_cast<Z*>(vz);
|
|
|
|
|
|
|
|
auto extraParams = reinterpret_cast<X*>(vextraParams);
|
|
|
|
|
|
|
|
__shared__ Nd4jLong zLen;
|
|
|
|
__shared__ int rank;
|
2020-03-11 15:58:53 +01:00
|
|
|
__shared__ bool xzSameOffsets, yzSameOffsets;
|
2020-03-10 14:29:09 +01:00
|
|
|
|
|
|
|
if (threadIdx.x == 0) {
|
[WIP] multi-device support (#80)
* fix pad javadoc and @see links. (#72)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* [WIP] More fixes (#73)
* special tests for ConstantTadHelper/ConstantShapeHelper
Signed-off-by: raver119 <raver119@gmail.com>
* release methods for data buffers
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary TadPack C++/Java side (#74)
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: raver119 <raver119@gmail.com>
* upsampling2d fix CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* DL4J trace logging (#79)
* MLN/CG trace logging for debugging
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tiny tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* strided_slice_bp shape fn leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: raver119 <raver119@gmail.com>
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* fix javadoc. (#76)
* fix javadoc.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace most @see with @link s.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* 4 additional tests
Signed-off-by: raver119 <raver119@gmail.com>
* launch context reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* per-device LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* Various DL4J/ND4J fixes (#81)
* #7954 Force refresh of UI when switching tabs on overview page
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8017 Concurrent modification exception (synchronize) fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8033 Don't initialize updater in middle of writing memory crash dump
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8208 Fix shape checks for ND4J int[] creator methods
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6385 #7992 Keras import naming fixes + cleanup
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8016 Upsampling3D - add NDHWC format support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: raver119 <raver119@gmail.com>
* remove duplicate code in createBufferDetached. (#83)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Keras model import - updater lr fix (#84)
* Keras model import - updater lr fix
Signed-off-by: eraly <susan.eraly@gmail.com>
* Keras model import - updater lr fix, cleanup
Signed-off-by: eraly <susan.eraly@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* Fix functions of OpaqueVariablesSet
* thread-local buffers/affinity
Signed-off-by: raver119 <raver119@gmail.com>
* thread safety for LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* more of thread safety
Signed-off-by: raver119 <raver119@gmail.com>
* one more multi threaded test
Signed-off-by: raver119 <raver119@gmail.com>
* SameDiff Convolution Config validation, better output methods (#82)
* Conv Config validation & tests
Signed-off-by: Ryan Nett <rnett@skymind.io>
* stackOutputs utility method
Signed-off-by: Ryan Nett <rnett@skymind.io>
* use constructor for validation, support negative kernel sizes (infered from weights)
Signed-off-by: Ryan Nett <rnett@skymind.io>
* better output methods
Signed-off-by: Ryan Nett <rnett@skymind.io>
* move output to be with fit and evaluate
Signed-off-by: Ryan Nett <rnett@skymind.io>
* fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* more fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* refactor duplicate code from pad methods. (#86)
* refactor duplicate code from pad methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace switch with if.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes and improvements (#87)
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6488 ElementWiseVertex broadcast support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Constructors and broadcast supported it Transforms.max/min
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8054 ElementWiseVertex now supports broadcast inputs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8057 Nd4j.create overload dtype fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7551 ND4J Shape validation fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Numpy boolean import (#91)
* numpy bool type
Signed-off-by: raver119 <raver119@gmail.com>
* numpy bool java side
Signed-off-by: raver119 <raver119@gmail.com>
* remove create method with unused parameter. (#89)
* remove create method with unused parameter.
* removed more unused methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* removing more unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* last removal of unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* remove createSparse methods. (#92)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes (#90)
* Deprecate Old*Op instances
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8063 #8054 Broadcast exceptions + cleanup inplace ops
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Remove bad test condition
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7993 Fix shape function issue in crop_and_resize op
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* DL4J SameDiff lambda layer fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8029 Fix for pnorm backprop math
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8038 Fix Op profiler NaN/Inf triggering + add tests (#93)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* createUninitializedDetached refactoring. (#94)
* wip
* update interface, add null implementations.
* Breaking one test in a weird way.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* createUninitializedDetached refactored.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* cuda build fix for issues introduced by recent refactoring
Signed-off-by: raver119 <raver119@gmail.com>
* [WIP] More of CUDA (#95)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: Yurii <yurii@skymind.io>
* - remove old test for batch_to_space (had wrong format and numbers were not checked)
Signed-off-by: Yurii <yurii@skymind.io>
* 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
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for concat.
* comment unnecessary stuff in s_t_b
Signed-off-by: Yurii <yurii@skymind.io>
* 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...)
Signed-off-by: Yurii <yurii@skymind.io>
* - debugging and fixing cuda tests in JavaInteropTests file
Signed-off-by: Yurii <yurii@skymind.io>
* - correct some tests
Signed-off-by: Yurii <yurii@skymind.io>
* 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
Signed-off-by: Yurii <yurii@skymind.io>
* Added tests for calculateOutputShapes.
* Addded Benchmarks test.
* Commented benchmark tests.
* change assertion
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed axpy op.
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* - fix tests for nativeOps::concat
Signed-off-by: Yurii <yurii@skymind.io>
* sequential transform/scalar
Signed-off-by: raver119 <raver119@gmail.com>
* allow nested parallelism
Signed-off-by: raver119 <raver119@gmail.com>
* assign_bp leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* block setRNG fix
Signed-off-by: raver119 <raver119@gmail.com>
* enable parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* enable nested parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: Yurii <yurii@skymind.io>
* 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
Signed-off-by: raver119 <raver119@gmail.com>
* - some tests fixes
Signed-off-by: Yurii <yurii@skymind.io>
* - correct the rest of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* - further correction of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for Cbow op. Also added cuda implementation for cbow helpers.
* - improve code of stack operation for scalar case
Signed-off-by: Yurii <yurii@skymind.io>
* - provide cuda kernel for gatherND operation
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of cbow helpers with cuda kernels.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* - further correction of cuda stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Implementatation of cbow op helper with cuda kernels. Working edition.
* Skip random testing for cudablas case.
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: Yurii <yurii@skymind.io>
* - get rid of concat op call, use instead direct concat helper call
Signed-off-by: Yurii <yurii@skymind.io>
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for lrelu and lrelu_bp.
* Added tests for selu and selu_bp.
* Fixed lrelu derivative helpers.
* - some corrections in lstm
Signed-off-by: Yurii <yurii@skymind.io>
* operator * result shape fix
Signed-off-by: raver119 <raver119@gmail.com>
* - correct typo in lstmCell
Signed-off-by: Yurii <yurii@skymind.io>
* few tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA inverse broadcast bool fix
Signed-off-by: raver119 <raver119@gmail.com>
* disable MMAP test for CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* BooleanOp syncToDevice
Signed-off-by: raver119 <raver119@gmail.com>
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* additional data types for im2col/col2im
Signed-off-by: raver119 <raver119@gmail.com>
* Added test for firas_sparse op.
* one more RandomBuffer test excluded
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for flatten op.
* Added test for Floor op.
* bunch of tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* mmulDot tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* 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
Signed-off-by: Yurii <yurii@skymind.io>
* Eliminate cbow crach.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminated abortion with batched nlp test.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed shared flag initializing.
* disabled bunch of cpu workspaces tests
Signed-off-by: raver119 <raver119@gmail.com>
* scalar operators fix: missing registerSpecialUse call
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed logdet for cuda and tests.
* - correct clipBynorm_bp
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed crop_and_resize shape datatype.
* - correct some mmul tests
Signed-off-by: Yurii <yurii@skymind.io>
* build fix
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI (#97)
Signed-off-by: raver119 <raver119@gmail.com>
* temporary stack fix
Signed-off-by: raver119 <raver119@gmail.com>
* round robin affinity test
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy CudaContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy ContextPool classes/methods
Signed-off-by: raver119 <raver119@gmail.com>
* one legacy test removed
Signed-off-by: raver119 <raver119@gmail.com>
* few more fields rearranged
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext++
Signed-off-by: raver119 <raver119@gmail.com>
* more of OpaqueLaunchContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext -> CudaContext
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handles
Signed-off-by: raver119 <raver119@gmail.com>
* typo
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver method
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handle propagated
Signed-off-by: raver119 <raver119@gmail.com>
* blas/solver handles
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* legacy concat implementations replaced with new CustomOp
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* concat now uses way more blocks
Signed-off-by: raver119 <raver119@gmail.com>
* print
Signed-off-by: raver119 <raver119@gmail.com>
* no more triple template mmul
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bitonic sort reorganized
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* type conversions moved to generic impl
Signed-off-by: raver119 <raver119@gmail.com>
* cpu data types pass
Signed-off-by: raver119 <raver119@gmail.com>
* non_max_suppression
Signed-off-by: raver119 <raver119@gmail.com>
* sortByValue fix
Signed-off-by: raver119 <raver119@gmail.com>
* ignore all mixed datatype tests for mmul
Signed-off-by: raver119 <raver119@gmail.com>
* special handling of OpProfiler exceptions
Signed-off-by: raver119 <raver119@gmail.com>
* - one failing concat test in cpp
- Nd4j.tile now uses op internally
Signed-off-by: raver119 <raver119@gmail.com>
* get back dtype exception for legacy arrays deserialization
Signed-off-by: raver119 <raver119@gmail.com>
2019-08-14 15:52:34 +02:00
|
|
|
|
2020-03-10 14:29:09 +01:00
|
|
|
zLen = shape::length(zShapeInfo);
|
|
|
|
rank = shape::rank(zShapeInfo);
|
2020-03-11 15:58:53 +01:00
|
|
|
|
|
|
|
xzSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo);
|
|
|
|
yzSameOffsets = shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo);
|
2019-06-06 14:21:15 +02:00
|
|
|
}
|
2020-03-10 14:29:09 +01:00
|
|
|
__syncthreads();
|
|
|
|
|
|
|
|
|
|
|
|
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
|
|
|
|
2020-07-26 14:59:27 +02:00
|
|
|
int coords[MAX_RANK];
|
2020-03-10 14:29:09 +01:00
|
|
|
|
|
|
|
for (int i = tid; i < zLen; i += blockDim.x * gridDim.x) {
|
|
|
|
|
2020-07-26 14:59:27 +02:00
|
|
|
shape::index2coords(i, zShapeInfo, coords);
|
2020-03-10 14:29:09 +01:00
|
|
|
|
2020-07-26 14:59:27 +02:00
|
|
|
const auto zOffset = shape::getOffset(zShapeInfo, coords);
|
|
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const auto xOffset = xzSameOffsets ? zOffset : shape::getOffset(xShapeInfo, coords);
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const auto yOffset = yzSameOffsets ? zOffset : shape::getOffset(yShapeInfo, coords);
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2020-03-10 14:29:09 +01:00
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z[zOffset] = OpType::op(x[xOffset], y[yOffset], extraParams);
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}
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}
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BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT BroadcastBool, , LIBND4J_TYPES, BOOL_TYPES);
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}
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2019-06-06 14:21:15 +02:00
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}
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