589 lines
23 KiB
Plaintext
589 lines
23 KiB
Plaintext
/*******************************************************************************
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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 Yurii Shyrma (iuriish@yahoo.com), created on 19.04.2018
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// @author raver119@gmail.com
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//
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#include <op_boilerplate.h>
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#include <ops/declarable/helpers/activations.h>
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#include <ShapeUtils.h>
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#include <numeric>
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#include <PointersManager.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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///////////////////////////////////////////////////////////////////
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template<typename X, typename Y>
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__global__ void preluCuda(const void *vx, const Nd4jLong *xShapeInfo,
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const void *vy, const Nd4jLong *yShapeInfo,
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void *vz) {
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const auto x = reinterpret_cast<const X*>(vx);
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const auto y = reinterpret_cast<const Y*>(vy);
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auto z = reinterpret_cast<X*>(vz);
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__shared__ Nd4jLong xzLen, totalThreads, *sharedMem;
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__shared__ int xzRank, yRank;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
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xzLen = shape::length(xShapeInfo);
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totalThreads = gridDim.x * blockDim.x;
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xzRank = shape::rank(xShapeInfo);
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yRank = shape::rank(yShapeInfo);
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}
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__syncthreads();
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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Nd4jLong* coords = sharedMem + threadIdx.x * xzRank;
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for (int i = tid; i < xzLen; i += totalThreads) {
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shape::index2coords(xzRank, xShapeInfo + 1, i, xzLen, coords);
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const auto xzOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + xzRank + 1, coords, xzRank);
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const auto xVal = x[xzOffset];
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if(xVal < 0) {
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for (uint j = 0; j < yRank; ++j)
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if(yShapeInfo[j + 1] == 1)
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coords[j + 1] = 0;
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z[xzOffset] = xVal * y[shape::getOffset(0, yShapeInfo + 1, yShapeInfo + yRank + 1, coords + 1, yRank)];
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}
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else
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z[xzOffset] = xVal;
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}
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}
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///////////////////////////////////////////////////////////////////
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template<typename X, typename Y>
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linkage void preluCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz) {
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preluCuda<X, Y><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz);
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}
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///////////////////////////////////////////////////////////////////
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void prelu(nd4j::LaunchContext * context, const NDArray& input, const NDArray& alpha, NDArray& output) {
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PointersManager manager(context, "prelu");
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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const int sharedMem = input.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128;
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const auto xType = input.dataType();
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const auto yType = alpha.dataType();
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NDArray::prepareSpecialUse({&output}, {&input, &alpha});
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BUILD_DOUBLE_SELECTOR(xType, yType, preluCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), alpha.getSpecialBuffer(), alpha.getSpecialShapeInfo(), output.getSpecialBuffer()), LIBND4J_TYPES, FLOAT_TYPES);
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NDArray::registerSpecialUse({&output}, {&input, &alpha});
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manager.synchronize();
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}
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///////////////////////////////////////////////////////////////////
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template<typename X, typename Y>
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__global__ linkage void preluBPCuda(const void *vIn, const Nd4jLong *inShapeInfo,
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const void *vAlpha, const Nd4jLong *alphaShapeInfo,
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const void *vdLdO, const Nd4jLong *dLdOShapeInfo,
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void *vdLdI, const Nd4jLong *dLdIShapeInfo,
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void *vdLdA, const Nd4jLong *dLdAShapeInfo) {
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const auto in = reinterpret_cast<const X*>(vIn);
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const auto alpha = reinterpret_cast<const Y*>(vAlpha);
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const auto dLdO = reinterpret_cast<const Y*>(vdLdO);
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auto dLdI = reinterpret_cast<Y*>(vdLdI);
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auto dLdA = reinterpret_cast<Y*>(vdLdA);
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__shared__ Nd4jLong inLen, totalThreads, *sharedMem;
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__shared__ int inRank, alphaRank;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
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inLen = shape::length(inShapeInfo);
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totalThreads = gridDim.x * blockDim.x;
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inRank = shape::rank(inShapeInfo);
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alphaRank = shape::rank(alphaShapeInfo);
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}
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__syncthreads();
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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Nd4jLong* coords = sharedMem + threadIdx.x * inRank;
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for (int i = tid; i < inLen; i += totalThreads) {
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shape::index2coords(inRank, inShapeInfo + 1, i, inLen, coords);
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const auto inOffset = shape::getOffset(0, inShapeInfo + 1, inShapeInfo + inRank + 1, coords, inRank);
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const auto dLdOOffset = shape::getOffset(0, dLdOShapeInfo + 1, dLdOShapeInfo + inRank + 1, coords, inRank);
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const auto dLdIOffset = shape::getOffset(0, dLdIShapeInfo + 1, dLdIShapeInfo + inRank + 1, coords, inRank);
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const auto xVal = in[inOffset];
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const auto grO = dLdO[dLdOOffset];
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if(xVal < 0) {
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for (uint j = 0; j < alphaRank; ++j)
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if(alphaShapeInfo[j + 1] == 1)
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coords[j + 1] = 0;
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const auto alphaOffset = shape::getOffset(0, alphaShapeInfo + 1, alphaShapeInfo + alphaRank + 1, coords + 1, alphaRank);
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const auto dLdAOffset = shape::getOffset(0, dLdAShapeInfo + 1, dLdAShapeInfo + alphaRank + 1, coords + 1, alphaRank);
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dLdI[dLdIOffset] = grO * alpha[alphaOffset];
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nd4j::math::atomics::nd4j_atomicAdd<Y>(&dLdA[dLdAOffset], static_cast<Y>(grO * xVal));
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}
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else
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dLdI[dLdIOffset] = grO;
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Y>
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__host__ linkage void preluBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void *vIn, const Nd4jLong *inShapeInfo, const void *vAlpha, const Nd4jLong *alphaShapeInfo, const void *vdLdO, const Nd4jLong *dLdOShapeInfo, void *vdLdI, const Nd4jLong *dLdIShapeInfo, void *vdLdA, const Nd4jLong *dLdAShapeInfo) {
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preluBPCuda<X, Y><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vIn, inShapeInfo, vAlpha, alphaShapeInfo, vdLdO, dLdOShapeInfo, vdLdI, dLdIShapeInfo, vdLdA, dLdAShapeInfo);
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}
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//////////////////////////////////////////////////////////////////////////
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void preluBP(nd4j::LaunchContext* context, const NDArray& input, const NDArray& alpha, const NDArray& dLdO, NDArray& dLdI, NDArray& dLdA) {
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dLdA.nullify();
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PointersManager manager(context, "preluBP");
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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const int sharedMem = input.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128;
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const auto xType = input.dataType();
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const auto zType = alpha.dataType();
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NDArray::prepareSpecialUse({&dLdI, &dLdA}, {&input, &alpha, &dLdO});
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BUILD_DOUBLE_SELECTOR(xType, zType, preluBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), alpha.getSpecialBuffer(), alpha.getSpecialShapeInfo(), dLdO.getSpecialBuffer(), dLdO.getSpecialShapeInfo(), dLdI.getSpecialBuffer(), dLdI.getSpecialShapeInfo(), dLdA.getSpecialBuffer(), dLdA.getSpecialShapeInfo()), LIBND4J_TYPES, FLOAT_TYPES);
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NDArray::registerSpecialUse({&dLdI, &dLdA}, {&input, &alpha, &dLdO});
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manager.synchronize();
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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__global__ void softMaxForVectorCuda(const void *vx, const Nd4jLong *xzShapeInfo, void *vz) {
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// logic of this kernel is based on assumption gridDim = 1
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const auto x = reinterpret_cast<const T*>(vx);
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auto z = reinterpret_cast<T*>(vz);
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__shared__ Nd4jLong len;
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__shared__ int numOfIters;
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__shared__ T* shmem;
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if (threadIdx.x == 0) {
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extern __shared__ char shared[];
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shmem = reinterpret_cast<T*>(shared);
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len = shape::length(xzShapeInfo);
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numOfIters = (len + blockDim.x - 1) / blockDim.x; // ceil (len / blockDim.x)
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}
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__syncthreads();
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T temp = -DataTypeUtils::max<T>(); // set start value to compare with at first iteration, FIXME: what if T is unsigned ??
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// ************ evaluate max element in input array x ************ //
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for (int i = 0; i < numOfIters; ++i) {
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const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
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if(elemIdx < len) {
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const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
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shmem[threadIdx.x] = (threadIdx.x != 0) ? x[offset] : nd4j::math::nd4j_max<T>(x[offset], temp); // take into account max element evaluated on previous iteration and stored in temp
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}
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else
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shmem[threadIdx.x] = -DataTypeUtils::max<T>(); // FIXME: what if T is unsigned ??
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__syncthreads();
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for (int s = blockDim.x / 2; s > 0; s /= 2) {
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if(threadIdx.x < s)
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shmem[threadIdx.x] = nd4j::math::nd4j_max<T>(shmem[threadIdx.x], shmem[threadIdx.x + s]);
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__syncthreads();
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}
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temp = shmem[0]; // save max value calculated at current iteration
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}
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const T max = temp;
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temp = 0;
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// ************ evaluate value of exp(x[offset] - max) per each element, store it to shared memory shmem ************ //
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// at the same evaluate sum of exponents, sum will be stored in shmem[0]
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for (int i = 0; i < numOfIters; ++i) {
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const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
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if(elemIdx < len) {
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const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
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z[offset] = nd4j::math::nd4j_exp<T, T>(x[offset] - max);
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shmem[threadIdx.x] = (threadIdx.x != 0) ? z[offset] : (z[offset] + temp); // take into account sum element evaluated on previous iteration and stored in temp
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}
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else
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shmem[threadIdx.x] = 0;
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__syncthreads();
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for (int s = blockDim.x / 2; s > 0; s /= 2) {
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if(threadIdx.x < s)
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shmem[threadIdx.x] += shmem[threadIdx.x + s];
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__syncthreads();
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}
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temp = shmem[0]; // save sum calculated at current iteration
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}
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// ************ evaluate z[offset] / sum ************ //
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for (int i = 0; i < numOfIters; ++i) {
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const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
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if(elemIdx >= len) continue;
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const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
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z[offset] /= shmem[0];
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}
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}
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///////////////////////////////////////////////////////////////////
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template <typename T>
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linkage void softMaxForVectorCudaLauncher(const cudaStream_t* stream, const void *vx, const Nd4jLong *xzShapeInfo, void *vz) {
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softMaxForVectorCuda<T><<<1, MAX_NUM_THREADS, MAX_NUM_THREADS * sizeof(T) + 512, *stream>>>(vx, xzShapeInfo, vz);
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}
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//////////////////////////////////////////////////////////////////////////
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void softmax(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
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if(!input.isActualOnDeviceSide()) input.syncToDevice();
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const int rank = input.rankOf();
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if(input.isVector()) {
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if(rank == 1 || input.sizeAt(dimension) != 1) {
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BUILD_SINGLE_SELECTOR(input.dataType(), softMaxForVectorCudaLauncher, (context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.getSpecialBuffer()), FLOAT_TYPES);
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input.tickReadDevice();
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}
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else
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output = 1.;
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}
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else {
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auto maxAlongDim = const_cast<NDArray&>(input).reduceAlongDims(reduce::Max, {dimension}, true);
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(input - maxAlongDim).applyTransform(transform::Exp, &output); // output contains exponents temporarily
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auto sumAlongDim = output.reduceAlongDims(reduce::Sum, {dimension}, true);
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output /= sumAlongDim;
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input.tickReadDevice();
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}
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PointersManager manager(context, "helpers::softmax");
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manager.synchronize();
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output.tickWriteDevice();
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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__global__ void logSoftMaxForVectorCuda(const void *vx, const Nd4jLong *xzShapeInfo, void *vz) {
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// logic of this kernel is based on assumption gridDim = 1
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const auto x = reinterpret_cast<const T*>(vx);
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auto z = reinterpret_cast<T*>(vz);
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__shared__ Nd4jLong len;
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__shared__ int numOfIters;
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__shared__ T* shmem;
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if (threadIdx.x == 0) {
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extern __shared__ char shared[];
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shmem = reinterpret_cast<T*>(shared);
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len = shape::length(xzShapeInfo);
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numOfIters = (len + blockDim.x - 1) / blockDim.x; // ceil (len / blockDim.x)
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}
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__syncthreads();
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T temp = -DataTypeUtils::max<T>(); // set start value to compare with at first iteration, FIXME: what if T is unsigned ??
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// ************ evaluate max element in input array x ************ //
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for (int i = 0; i < numOfIters; ++i) {
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const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
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if(elemIdx < len) {
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const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
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shmem[threadIdx.x] = (threadIdx.x != 0) ? x[offset] : nd4j::math::nd4j_max<T>(x[offset], temp); // take into account max element evaluated on previous iteration and stored in temp
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}
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else
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shmem[threadIdx.x] = -DataTypeUtils::max<T>(); // FIXME: what if T is unsigned ??
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__syncthreads();
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for (int s = blockDim.x / 2; s > 0; s /= 2) {
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if(threadIdx.x < s)
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shmem[threadIdx.x] = nd4j::math::nd4j_max<T>(shmem[threadIdx.x], shmem[threadIdx.x + s]);
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__syncthreads();
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}
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temp = shmem[0]; // save max value calculated at current iteration
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}
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const T max = temp;
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temp = 0;
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// ************ evaluate value of exp(x[offset] - max) per each element, store it to shared memory shmem ************ //
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// at the same time evaluate sum of exponents, sum will be stored in shmem[0]
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for (int i = 0; i < numOfIters; ++i) {
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const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
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if(elemIdx < len) {
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const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
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z[offset] = nd4j::math::nd4j_exp<T, T>(x[offset] - max);
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shmem[threadIdx.x] = (threadIdx.x != 0) ? z[offset] : (z[offset] + temp); // take into account sum element evaluated on previous iteration and stored in temp
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}
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else
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shmem[threadIdx.x] = 0;
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__syncthreads();
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for (int s = blockDim.x / 2; s > 0; s /= 2) {
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if(threadIdx.x < s)
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shmem[threadIdx.x] += shmem[threadIdx.x + s];
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__syncthreads();
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}
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temp = shmem[0]; // save sum calculated at current iteration
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}
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// ************ evaluate log(z[offset] / sum) ************ //
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for (int i = 0; i < numOfIters; ++i) {
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const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
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if(elemIdx >= len) continue;
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const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
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z[offset] = nd4j::math::nd4j_log<T,T>(z[offset] / shmem[0]);
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}
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}
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///////////////////////////////////////////////////////////////////
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template <typename T>
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linkage void logSoftMaxForVectorCudaLauncher(const cudaStream_t* stream, const void *vx, const Nd4jLong *xzShapeInfo, void *vz) {
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logSoftMaxForVectorCuda<T><<<1, MAX_NUM_THREADS, MAX_NUM_THREADS * sizeof(T) + 512, *stream>>>(vx, xzShapeInfo, vz);
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}
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//////////////////////////////////////////////////////////////////////////
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void logSoftmax(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
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if(!input.isActualOnDeviceSide()) input.syncToDevice();
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const int rank = input.rankOf();
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if(input.isVector()) {
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if(rank == 1 || input.sizeAt(dimension) != 1) {
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BUILD_SINGLE_SELECTOR(input.dataType(), logSoftMaxForVectorCudaLauncher, (context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.getSpecialBuffer()), FLOAT_TYPES);
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input.tickReadDevice();
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}
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else
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output = 0.;
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}
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else {
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auto maxAlongDim = const_cast<NDArray&>(input).reduceAlongDims(reduce::Max, {dimension}, true);
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(input - maxAlongDim).applyTransform(transform::Exp, &output); // output contains exponents temporarily
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auto sumAlongDim = output.reduceAlongDims(reduce::Sum, {dimension}, true);
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output /= sumAlongDim;
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output.applyTransform(transform::Log);
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input.tickReadDevice();
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}
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PointersManager manager(context, "helpers::logSoftmax");
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manager.synchronize();
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|
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output.tickWriteDevice();
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}
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|
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|
///////////////////////////////////////////////////////////////////
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template<typename T>
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__global__ linkage void softMaxDerivForVectorCuda(const void *vx, const Nd4jLong *xzShapeInfo, void *vz) {
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|
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// logic of this kernel is based on assumption gridDim = 1
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|
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const auto x = reinterpret_cast<const T*>(vx);
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auto z = reinterpret_cast<T*>(vz);
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__shared__ Nd4jLong len;
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__shared__ int numOfIters;
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__shared__ T* shmem;
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|
|
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if (threadIdx.x == 0) {
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extern __shared__ char shared[];
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shmem = reinterpret_cast<T*>(shared);
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len = shape::length(xzShapeInfo);
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numOfIters = (len + blockDim.x - 1) / blockDim.x; // ceil (len / blockDim.x)
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}
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__syncthreads();
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|
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T temp = -DataTypeUtils::max<T>(); // set start value to compare with at first iteration, FIXME: what if T is unsigned ??
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|
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// ************ evaluate max element in input array x ************ //
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for (int i = 0; i < numOfIters; ++i) {
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|
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const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
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if(elemIdx < len) {
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const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
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shmem[threadIdx.x] = (threadIdx.x != 0) ? x[offset] : nd4j::math::nd4j_max<T>(x[offset], temp); // take into account max element evaluated on previous iteration and stored in temp
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}
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else
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shmem[threadIdx.x] = -DataTypeUtils::max<T>(); // FIXME: what if T is unsigned ??
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|
|
|
__syncthreads();
|
|
|
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for (int s = blockDim.x / 2; s > 0; s /= 2) {
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if(threadIdx.x < s)
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|
shmem[threadIdx.x] = nd4j::math::nd4j_max<T>(shmem[threadIdx.x], shmem[threadIdx.x + s]);
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|
__syncthreads();
|
|
}
|
|
|
|
temp = shmem[0]; // save max value calculated at current iteration
|
|
}
|
|
|
|
const T max = temp;
|
|
temp = 0;
|
|
|
|
// ************ evaluate value of exp(x[offset] - max) per each element, store it to shared memory shmem ************ //
|
|
// at the same evaluate sum of exponents, sum will be stored in shmem[0]
|
|
for (int i = 0; i < numOfIters; ++i) {
|
|
|
|
const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
|
|
if(elemIdx < len) {
|
|
const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
|
|
z[offset] = nd4j::math::nd4j_exp<T, T>(x[offset] - max);
|
|
shmem[threadIdx.x] = (threadIdx.x != 0) ? z[offset] : (z[offset] + temp); // take into account sum element evaluated on previous iteration and stored in temp
|
|
}
|
|
else
|
|
shmem[threadIdx.x] = 0;
|
|
|
|
__syncthreads();
|
|
|
|
for (int s = blockDim.x / 2; s > 0; s /= 2) {
|
|
if(threadIdx.x < s)
|
|
shmem[threadIdx.x] += shmem[threadIdx.x + s];
|
|
__syncthreads();
|
|
}
|
|
|
|
temp = shmem[0]; // save sum calculated at current iteration
|
|
}
|
|
|
|
// ************ evaluate (z[offset] / sum) and derivative z[offset] = z[offset] * (1 - z[offset]) ************ //
|
|
for (int i = 0; i < numOfIters; ++i) {
|
|
const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
|
|
if(elemIdx >= len) continue;
|
|
const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
|
|
z[offset] /= shmem[0];
|
|
z[offset] *= (1.f - z[offset]); // derivative
|
|
}
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
linkage void softMaxDerivForVectorCudaLauncher(const cudaStream_t* stream, const void *vx, const Nd4jLong *xzShapeInfo, void *vz) {
|
|
|
|
softMaxDerivForVectorCuda<T><<<1, MAX_NUM_THREADS, MAX_NUM_THREADS * sizeof(T) + 512, *stream>>>(vx, xzShapeInfo, vz);
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
void softmaxDerivative(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
|
|
|
|
if(!input.isActualOnDeviceSide()) input.syncToDevice();
|
|
const int rank = input.rankOf();
|
|
int temp;
|
|
|
|
if(shape::isCommonVector(input.getShapeInfo(), temp)) {
|
|
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), softMaxDerivForVectorCudaLauncher, (context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.getSpecialBuffer()), FLOAT_TYPES);
|
|
input.tickReadDevice();
|
|
}
|
|
else {
|
|
|
|
auto maxAlongDim = const_cast<NDArray&>(input).reduceAlongDims(reduce::Max, {dimension}, true);
|
|
(input - maxAlongDim).applyTransform(transform::Exp, &output); // output contains exponents temporarily
|
|
auto sumAlongDim = output.reduceAlongDims(reduce::Sum, {dimension}, true);
|
|
output /= sumAlongDim;
|
|
output *= (1.f - output); // derivative
|
|
input.tickReadDevice();
|
|
}
|
|
|
|
PointersManager manager(context, "helpers::softmaxDerivative");
|
|
manager.synchronize();
|
|
|
|
output.tickWriteDevice();
|
|
}
|
|
|
|
|
|
template <typename T>
|
|
linkage void thresholdRelu_(NDArray const& input, double threshold, NDArray& output) {
|
|
auto routine = LAMBDA_T(_x, threshold) {
|
|
return _x > (T)threshold ? _x: (T)0.f;
|
|
};
|
|
const_cast<NDArray&>(input).applyLambda(routine, &output);
|
|
}
|
|
|
|
void thresholdRelu(nd4j::LaunchContext * context, NDArray const& input, double threshold, NDArray& output) {
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), thresholdRelu_, (input, threshold, output), FLOAT_TYPES);
|
|
}
|
|
|
|
template <typename T>
|
|
linkage void thresholdReluDerivative_(NDArray* input, double theta, NDArray* dLdO, NDArray* output) {
|
|
auto derivative = LAMBDA_TT(_x, grO, theta) {if (_x > theta) return grO; else return static_cast<T>(0); };
|
|
|
|
input->applyPairwiseLambda(dLdO, derivative, output);
|
|
}
|
|
|
|
void thresholdReluDerivative(nd4j::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output) {
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), thresholdReluDerivative_, (input, threshold, dLdO, output), FLOAT_TYPES);
|
|
}
|
|
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void thresholdReluDerivative_, (NDArray* input, double threshold, NDArray* dLdO, NDArray* output), FLOAT_TYPES);
|
|
BUILD_DOUBLE_TEMPLATE(template void preluCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz), LIBND4J_TYPES, FLOAT_TYPES);
|
|
BUILD_DOUBLE_TEMPLATE(template void preluBPCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void *vIn, const Nd4jLong *inShapeInfo, const void *vAlpha, const Nd4jLong *alphaShapeInfo, const void *vdLdO, const Nd4jLong *dLdOShapeInfo, void *vdLdI, const Nd4jLong *dLdIShapeInfo, void *vdLdA, const Nd4jLong *dLdAShapeInfo), LIBND4J_TYPES, FLOAT_TYPES);
|
|
BUILD_SINGLE_TEMPLATE(template void softMaxForVectorCudaLauncher, (const cudaStream_t* stream, const void *vx, const Nd4jLong *xzShapeInfo, void *vz), FLOAT_TYPES);
|
|
BUILD_SINGLE_TEMPLATE(template void softMaxDerivForVectorCudaLauncher, (const cudaStream_t* stream, const void *vx, const Nd4jLong *xzShapeInfo, void *vz), FLOAT_TYPES);
|
|
|
|
|
|
}
|
|
}
|
|
}
|
|
|