945 lines
42 KiB
Plaintext
945 lines
42 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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* Copyright (c) 2019 Konduit K.K.
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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 20.04.2018
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//
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#include<ops/declarable/helpers/transforms.h>
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#include <array/ResultSet.h>
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#include <helpers/ShapeUtils.h>
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#include <numeric>
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#include <NDArrayFactory.h>
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#include <helpers/TAD.h>
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#include <exceptions/cuda_exception.h>
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#include <PointersManager.h>
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#include <ConstantTadHelper.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 T>
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__global__ static void invertPermutationCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo) {
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const T* x = reinterpret_cast<const T*>(vx);
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T* z = reinterpret_cast<T*>(vz);
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__shared__ Nd4jLong len, totalThreads;
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if (threadIdx.x == 0) {
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len = shape::length(xShapeInfo);
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totalThreads = gridDim.x * blockDim.x;
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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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for (Nd4jLong i = tid; i < len; i += totalThreads) {
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const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
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const Nd4jLong index = x[xOffset];
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const auto zOffset = shape::getIndexOffset(index, zShapeInfo);
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z[zOffset] = i;
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}
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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__host__ static void invertPermutationCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
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const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo) {
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invertPermutationCuda<T><<<blocksPerGrid, threadsPerBlock, 1024, *stream>>>(vx, xShapeInfo, vz, zShapeInfo);
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}
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////////////////////////////////////////////////////////////////////////
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void invertPermutation(nd4j::LaunchContext* context, const NDArray& input, NDArray& output) {
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const int threadsPerBlock = MAX_NUM_THREADS;
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const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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PointersManager manager(context, "invertPermutation");
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NDArray::prepareSpecialUse({&output}, {&input});
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BUILD_SINGLE_SELECTOR(input.dataType(), invertPermutationCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.getSpecialBuffer(), output.getSpecialShapeInfo()), LIBND4J_TYPES);
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NDArray::registerSpecialUse({&output}, {&input});
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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__ static void traceCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const uint diagLen) {
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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__ T* sharedMem;
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__shared__ int xRank, zRank; // xRank = zRank + 2
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__shared__ Nd4jLong xLen, zLen, *coordsMem;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<T*>(shmem);
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coordsMem = reinterpret_cast<Nd4jLong*>(shmem + blockDim.x * sizeof(T));
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xRank = shape::rank(xShapeInfo);
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zRank = shape::rank(zShapeInfo);
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xLen = shape::length(xShapeInfo);
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zLen = shape::length(zShapeInfo); // corresponds to number of matrices
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}
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__syncthreads();
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Nd4jLong* coords = coordsMem + threadIdx.x * xRank;
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for (uint m = blockIdx.x; m < zLen; m += gridDim.x) { // one block per each element of z, that is per each matrix
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shape::index2coords(m, zShapeInfo, coords);
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const auto zOffset = shape::getOffset(zShapeInfo, coords);
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sharedMem[threadIdx.x] = 0;
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for (uint i = threadIdx.x; i < diagLen; i += blockDim.x) {
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coords[zRank] = coords[zRank + 1] = i;
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const auto xOffset = shape::getOffset(xShapeInfo, coords);
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sharedMem[threadIdx.x] += x[xOffset];
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}
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__syncthreads();
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// aggregate sum
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for (Nd4jLong activeThreads = blockDim.x / 2; activeThreads > 0; activeThreads /= 2) {
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if (threadIdx.x < activeThreads)
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sharedMem[threadIdx.x] += sharedMem[threadIdx.x + activeThreads];
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__syncthreads();
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}
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if (threadIdx.x == 0)
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z[zOffset] = *sharedMem;
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__syncthreads();
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}
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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static void traceCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
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const void *vx, const Nd4jLong *xShapeInfo,
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void *vz, const Nd4jLong *zShapeInfo,
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const uint diagLen) {
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traceCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, diagLen);
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}
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///////////////////////////////////////////////////////////////////
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void trace(nd4j::LaunchContext* context, const NDArray& input, NDArray& output) {
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PointersManager manager(context, "trace");
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const uint diagLen = input.sizeAt(-1) < input.sizeAt(-2) ? input.sizeAt(-1) : input.sizeAt(-2);
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const int threadsPerBlock = MAX_NUM_THREADS / 4;
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const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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const int sharedMem = threadsPerBlock * (sizeof(Nd4jLong) * input.rankOf() + input.sizeOfT()) + 128;
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NDArray::prepareSpecialUse({&output}, {&input});
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BUILD_SINGLE_SELECTOR(input.dataType(), traceCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), diagLen), LIBND4J_TYPES);
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NDArray::registerSpecialUse({&output}, {&input});
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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__ static void triuBPCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int diag) {
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// x and z have same shapes
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const auto x = reinterpret_cast<const T*>(vx); // gradO
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auto z = reinterpret_cast<T*>(vz); // gradI
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__shared__ int rank, areSameOffsets; // xRank = zRank
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__shared__ Nd4jLong len, totalThreads, *sharedMem; // xLen = zLen
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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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areSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo);
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rank = shape::rank(xShapeInfo);
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len = shape::length(zShapeInfo);
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totalThreads = gridDim.x * blockDim.x;
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}
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__syncthreads();
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auto coords = sharedMem + threadIdx.x * rank;
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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for (Nd4jLong i = tid; i < len; i += totalThreads) {
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shape::index2coords(i, zShapeInfo, coords);
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const auto zOffset = shape::getOffset(zShapeInfo, coords);
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if((coords[rank - 2] + diag > coords[rank - 1])) // row + diag > col
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z[zOffset] = 0;
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else
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z[zOffset] = x[areSameOffsets ? zOffset : shape::getOffset(xShapeInfo, coords)];
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}
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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static void triuBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int diag) {
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triuBPCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, diag);
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}
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///////////////////////////////////////////////////////////////////
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void triuBP(nd4j::LaunchContext* context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) {
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const int threadsPerBlock = MAX_NUM_THREADS / 4;
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const int blocksPerGrid = (gradO.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * gradO.rankOf() + 128;
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PointersManager manager(context, "triuBP");
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NDArray::prepareSpecialUse({&gradI}, {&gradO});
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BUILD_SINGLE_SELECTOR(gradI.dataType(), triuBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), gradO.getSpecialBuffer(), gradO.getSpecialShapeInfo(), gradI.specialBuffer(), gradI.specialShapeInfo(), diagonal), LIBND4J_TYPES);
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NDArray::registerSpecialUse({&gradI}, {&gradO});
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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__ static void tileBPCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, Nd4jLong* globMem) {
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// x and z have same shapes
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const auto x = reinterpret_cast<const T*>(vx); // gradO
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auto z = reinterpret_cast<T*>(vz); // gradI
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__shared__ int xRank, zRank; // xRank >= zRank
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__shared__ Nd4jLong numOfXOffsets, zLen, totalThreads, *sharedMem; // xLen >= zLen
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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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xRank = shape::rank(zShapeInfo);
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zLen = shape::length(zShapeInfo);
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numOfXOffsets = shape::length(xShapeInfo) / zLen;
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totalThreads = gridDim.x * blockDim.x;
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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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auto memBuff = sharedMem + threadIdx.x * 2 * xRank;
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auto xOffsets = globMem + tid * numOfXOffsets;
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for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
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const auto zOffset = shape::getIndexOffset(i, zShapeInfo);
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shape::outerArrayOffsets(xOffsets, i, xShapeInfo, zShapeInfo, memBuff);
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z[zOffset] = x[xOffsets[0]]; // first offset
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for (Nd4jLong j = 1; j < numOfXOffsets; ++j) // rest offsets
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z[zOffset] += x[xOffsets[j]];
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}
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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static void tileBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, Nd4jLong* globMem) {
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tileBPCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, globMem);
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}
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//////////////////////////////////////////////////////////////////////////
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void tileBP(nd4j::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {
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NDArray memBuff('c', gradO.getShapeAsVector(), nd4j::DataType::INT64, context); // empty auxiliary array for storing device memory which will be used in kernel calculations
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const int threadsPerBlock = MAX_NUM_THREADS / 4;
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const int blocksPerGrid = (gradI.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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const int sharedMem = threadsPerBlock * sizeof(Nd4jLong) * 2 * gradO.rankOf() + 128;
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PointersManager manager(context, "tileBP");
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NDArray::prepareSpecialUse({&gradI}, {&gradO, &memBuff});
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BUILD_SINGLE_SELECTOR(gradI.dataType(), tileBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), gradO.getSpecialBuffer(), gradO.getSpecialShapeInfo(), gradI.specialBuffer(), gradI.specialShapeInfo(), reinterpret_cast<Nd4jLong*>(memBuff.specialBuffer())), FLOAT_TYPES);
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NDArray::registerSpecialUse({&gradI}, {&gradO, &memBuff});
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manager.synchronize();
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}
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//////////////////////////////////////////////////////////////////////////
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// x - input, y - gradO, z - gradI
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template<typename X, typename Z>
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__global__ static void clipByNormBPWholeArrCuda(const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo, void* vz, const Nd4jLong* zShapeInfo, void* vreducBuff, const Z clipNormVal) {
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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if(tid >= shape::length(zShapeInfo))
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return;
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const auto x = reinterpret_cast<const X*>(vx);
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const auto y = reinterpret_cast<const Z*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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auto reducBuff = reinterpret_cast<Z*>(vreducBuff);
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uint* count = reinterpret_cast<uint*>(vreducBuff) + 16384;
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__shared__ Z* shMem;
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__shared__ Nd4jLong len;
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__shared__ bool amIinLastBlock;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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shMem = reinterpret_cast<Z*>(shmem);
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len = shape::length(zShapeInfo); // xLen = yLen = zLen
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}
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__syncthreads();
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// fill shared memory with array elements
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const auto xVal = x[shape::getIndexOffset(tid, xShapeInfo)];
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const auto yVal = y[shape::getIndexOffset(tid, yShapeInfo)];
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shMem[2*threadIdx.x] = static_cast<Z>(xVal * xVal); // for norm
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shMem[2*threadIdx.x + 1] = static_cast<Z>(xVal * yVal); // for input * gradO
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__syncthreads();
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// accumulate sum per block
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for (int activeThreads = blockDim.x / 2; activeThreads > 0; activeThreads /= 2) {
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if (threadIdx.x < activeThreads && tid + activeThreads < len) {
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shMem[2*threadIdx.x] += shMem[2*(threadIdx.x + activeThreads)];
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shMem[2*threadIdx.x + 1] += shMem[2*(threadIdx.x + activeThreads) + 1];
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}
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__syncthreads();
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}
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// store accumulated sums in reduction buffer (reducBuff)
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if (threadIdx.x == 0) {
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reducBuff[2*blockIdx.x] = shMem[0];
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reducBuff[2*blockIdx.x + 1] = shMem[1];
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__threadfence();
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amIinLastBlock = gridDim.x == 1 || (atomicInc(count, gridDim.x) == gridDim.x - 1);
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}
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__syncthreads();
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// shared memory of last block is used for final summation of values stored in reduction buffer
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if (amIinLastBlock) {
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for (int i = threadIdx.x; i < gridDim.x; i += blockDim.x) {
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shMem[2*threadIdx.x] = (i == threadIdx.x ) ? reducBuff[2*i] : reducBuff[2*i] + shMem[2*threadIdx.x];
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shMem[2*threadIdx.x + 1] = (i == threadIdx.x ) ? reducBuff[2*i + 1] : reducBuff[2*i + 1] + shMem[2*threadIdx.x + 1];
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}
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__syncthreads();
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// accumulate sum
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for (int activeThreads = blockDim.x / 2; activeThreads > 0; activeThreads /= 2) {
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if (threadIdx.x < activeThreads && threadIdx.x + activeThreads < gridDim.x) {
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shMem[2*threadIdx.x] += shMem[2*(threadIdx.x + activeThreads)];
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shMem[2*threadIdx.x + 1] += shMem[2*(threadIdx.x + activeThreads) + 1];
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}
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__syncthreads();
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}
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if (threadIdx.x == 0) {
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reducBuff[0] = math::nd4j_sqrt<Z,Z>(shMem[0]);
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reducBuff[1] = shMem[1];
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count = 0;
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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// x - input, y - gradO, z - gradI
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template<typename X, typename Z>
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__global__ static void clipByNormBPCalcGradCuda(const void* vx, const Nd4jLong* xShapeInfo, const void* vy, const Nd4jLong* yShapeInfo, void* vz, const Nd4jLong* zShapeInfo, void* vreducBuff, const Z clipNormVal) {
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const Nd4jLong len = shape::length(zShapeInfo); // xLen = yLen = zLen
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if(tid >= len)
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return;
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const auto x = reinterpret_cast<const X*>(vx);
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const auto y = reinterpret_cast<const Z*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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__shared__ Z norm, sumOfProd;
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if (threadIdx.x == 0) {
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norm = reinterpret_cast<Z*>(vreducBuff)[0];
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sumOfProd = reinterpret_cast<Z*>(vreducBuff)[1];
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}
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__syncthreads();
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const auto yOffset = shape::getIndexOffset(tid, yShapeInfo);
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const auto zOffset = shape::getIndexOffset(tid, zShapeInfo);
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if(norm > clipNormVal) {
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const auto xOffset = shape::getIndexOffset(tid, xShapeInfo);
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const Z factor1 = static_cast<Z>(1) / norm; // 1 / norm
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const Z factor2 = factor1 / (norm * norm); // 1 / (norm * norm * norm)
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z[zOffset] = clipNormVal * (factor1 * y[yOffset] - factor2 * sumOfProd * x[xOffset]);
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}
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else {
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z[zOffset] = y[yOffset];
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}
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}
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//////////////////////////////////////////////////////////////////////////
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// x - input, y - gradO, z - gradI
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template<typename X, typename Z>
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__global__ static void clipByNormBPTadsCuda(const void* vx, const Nd4jLong* xTadShapeInfo, const Nd4jLong* xTadOffsets, const void* vy, const Nd4jLong* yTadShapeInfo, const Nd4jLong* yTadOffsets, void* vz, const Nd4jLong* zTadShapeInfo, const Nd4jLong* zTadOffsets, const Z clipNormVal) {
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const auto x = reinterpret_cast<const X*>(vx);
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const auto y = reinterpret_cast<const Z*>(vy);
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auto z = reinterpret_cast<Z*>(vz);
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__shared__ Z* shMem;
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__shared__ Nd4jLong tadLen;
|
|
|
|
if (threadIdx.x == 0) {
|
|
|
|
extern __shared__ unsigned char shmem[];
|
|
shMem = reinterpret_cast<Z*>(shmem);
|
|
tadLen = shape::length(zTadShapeInfo); // xTadLen = yTadLen = zTadLen
|
|
}
|
|
__syncthreads();
|
|
|
|
const auto* xTad = x + xTadOffsets[blockIdx.x];
|
|
const auto* yTad = y + yTadOffsets[blockIdx.x];
|
|
auto* zTad = z + zTadOffsets[blockIdx.x];
|
|
|
|
// *** FIRST STAGE - ACCUMULATE REQUIRED SUMS *** //
|
|
|
|
Z norm = 0;
|
|
Z sumOfProd = 0;
|
|
|
|
for (uint i = threadIdx.x; i < tadLen; i += blockDim.x) {
|
|
|
|
const auto xOffset = shape::getIndexOffset(i, xTadShapeInfo);
|
|
const auto yOffset = shape::getIndexOffset(i, yTadShapeInfo);
|
|
|
|
shMem[2*threadIdx.x] = static_cast<Z>(xTad[xOffset] * xTad[xOffset]); // for norm
|
|
shMem[2*threadIdx.x + 1] = static_cast<Z>(xTad[xOffset] * yTad[yOffset]); // for input * gradO
|
|
|
|
__syncthreads();
|
|
|
|
// accumulate sum per block
|
|
for (uint activeThreads = blockDim.x / 2; activeThreads > 0; activeThreads /= 2) {
|
|
|
|
if (threadIdx.x < activeThreads && i + activeThreads < tadLen) {
|
|
|
|
shMem[2*threadIdx.x] += shMem[2*(threadIdx.x + activeThreads)];
|
|
shMem[2*threadIdx.x + 1] += shMem[2*(threadIdx.x + activeThreads) + 1];
|
|
}
|
|
__syncthreads();
|
|
}
|
|
|
|
norm += shMem[0];
|
|
sumOfProd += shMem[1];
|
|
}
|
|
|
|
// *** SECOND STAGE - GRADIENT CALCULATION *** //
|
|
|
|
norm = math::nd4j_sqrt<Z,Z>(norm);
|
|
|
|
for (uint i = threadIdx.x; i < tadLen; i += blockDim.x) {
|
|
|
|
const auto yOffset = shape::getIndexOffset(i, yTadShapeInfo);
|
|
const auto zOffset = shape::getIndexOffset(i, zTadShapeInfo);
|
|
|
|
if(norm > clipNormVal) {
|
|
|
|
const auto xOffset = shape::getIndexOffset(i, xTadShapeInfo);
|
|
|
|
const Z factor1 = static_cast<Z>(1) / norm; // 1 / norm
|
|
const Z factor2 = factor1 / (norm * norm); // 1 / (norm * norm * norm)
|
|
|
|
zTad[zOffset] = clipNormVal * (factor1 * yTad[yOffset] - factor2 * sumOfProd * xTad[xOffset]);
|
|
}
|
|
else {
|
|
zTad[zOffset] = yTad[yOffset];
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename X, typename Z>
|
|
static void clipByNormBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
|
|
const void* vx, const Nd4jLong* xShapeInfo, const Nd4jLong* xTadOffsets,
|
|
const void* vy, const Nd4jLong* yShapeInfo, const Nd4jLong* yTadOffsets,
|
|
void* vz, const Nd4jLong* zShapeInfo, const Nd4jLong* zTadOffsets,
|
|
void* vreducBuff, const double clipNormVal) {
|
|
|
|
if(xTadOffsets == nullptr) { // means whole array
|
|
clipByNormBPWholeArrCuda<X,Z><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, vreducBuff, static_cast<Z>(clipNormVal));
|
|
clipByNormBPCalcGradCuda<X,Z><<<blocksPerGrid, threadsPerBlock, 256, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, vreducBuff, static_cast<Z>(clipNormVal));
|
|
}
|
|
else // means tads using
|
|
clipByNormBPTadsCuda<X,Z><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, xTadOffsets, vy, yShapeInfo, yTadOffsets, vz, zShapeInfo, zTadOffsets, static_cast<Z>(clipNormVal));
|
|
}
|
|
BUILD_DOUBLE_TEMPLATE(template void clipByNormBPCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, const Nd4jLong* xTadOffsets, const void *vy, const Nd4jLong *yShapeInfo, const Nd4jLong* yTadOffsets, void *vz, const Nd4jLong *zShapeInfo, const Nd4jLong* zTadOffsets, void* vreducBuff, const double clipNormVal), FLOAT_TYPES, FLOAT_TYPES);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm) {
|
|
|
|
PointersManager manager(context, "clipByNormBP");
|
|
|
|
const double clipNormVal = clipNorm.e<double>(0);
|
|
|
|
const auto xType = input.dataType();
|
|
const auto zType = gradI.dataType();
|
|
|
|
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
|
const int sharedMem = threadsPerBlock * 2 * input.sizeOfT() + 128;
|
|
|
|
NDArray::prepareSpecialUse({&gradI}, {&input, &gradO});
|
|
|
|
|
|
if(dimensions.empty() || dimensions.size() == input.rankOf()) { // means whole array
|
|
|
|
const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
|
|
BUILD_DOUBLE_SELECTOR(xType, zType, clipByNormBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), nullptr, gradO.getSpecialBuffer(), gradO.getSpecialShapeInfo(), nullptr, gradI.getSpecialBuffer(), gradI.getSpecialShapeInfo(), nullptr, context->getReductionPointer(), clipNormVal), FLOAT_TYPES, FLOAT_TYPES);
|
|
}
|
|
else { // means tads using
|
|
|
|
auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions);
|
|
auto packY = ConstantTadHelper::getInstance()->tadForDimensions(gradO.getShapeInfo(), dimensions);
|
|
auto packZ = ConstantTadHelper::getInstance()->tadForDimensions(gradI.getShapeInfo(), dimensions);
|
|
|
|
const int blocksPerGrid = packX.numberOfTads();
|
|
BUILD_DOUBLE_SELECTOR(xType, zType, clipByNormBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.getSpecialBuffer(), packX.platformShapeInfo(), packX.platformOffsets(), gradO.getSpecialBuffer(), packY.platformShapeInfo(), packY.platformOffsets(), gradI.getSpecialBuffer(), packZ.platformShapeInfo(), packZ.platformOffsets(), nullptr, clipNormVal), FLOAT_TYPES, FLOAT_TYPES);
|
|
}
|
|
|
|
NDArray::registerSpecialUse({&gradI}, {&input, &gradO});
|
|
|
|
manager.synchronize();
|
|
}
|
|
|
|
template <typename T>
|
|
static __global__ void swapShuffleKernel(T* input, Nd4jLong* shape, Nd4jLong firstDim, nd4j::graph::RandomGenerator* rng) {
|
|
auto tid = blockIdx.x * blockDim.x;
|
|
auto step = blockDim.x * gridDim.x;
|
|
|
|
for (int i = firstDim - 1 - tid - threadIdx.x; i > 0; i -= step) {
|
|
int r = rng->relativeInt(i) % i;
|
|
if (i != r) {
|
|
const auto iOffset = shape::getIndexOffset(i, shape);
|
|
const auto rOffset = shape::getIndexOffset(r, shape);
|
|
T e0 = input[iOffset];
|
|
T e1 = input[rOffset];
|
|
//math::nd4j_swap<T>(input(i), input(r));
|
|
input[iOffset] = e1;
|
|
input[rOffset] = e0;
|
|
}
|
|
}
|
|
}
|
|
template <typename T>
|
|
static __global__ void fillShuffleKernel(T* input, Nd4jLong* inputShape, T* output, Nd4jLong* outputShape, Nd4jLong firstDim, int* indices, nd4j::graph::RandomGenerator* rng) {
|
|
|
|
// PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold())
|
|
auto tid = blockIdx.x * blockDim.x;
|
|
auto step = blockDim.x * gridDim.x;
|
|
|
|
for(int i = firstDim - 1 - tid - threadIdx.x; i > 0; i -= step) {
|
|
int r = rng->relativeInt(i) % i;
|
|
output[shape::getIndexOffset(i, outputShape)] = input[shape::getIndexOffset(indices[r], inputShape)];
|
|
if(i != r) {
|
|
output[shape::getIndexOffset(r, outputShape)] = input[shape::getIndexOffset(indices[i], inputShape)];
|
|
// output.p(r, input.e<T>(indices[i]));
|
|
// math::nd4j_swap<int>(indices[i], indices[r]);
|
|
atomicExch(&indices[i], indices[r]);
|
|
}
|
|
}
|
|
|
|
}
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
void randomShuffle_(nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace) {
|
|
|
|
// check edge cases first
|
|
int temp;
|
|
const int firstDim = input.sizeAt(0);
|
|
auto stream = context->getCudaStream();
|
|
NDArray::prepareSpecialUse({&output}, {&input});
|
|
if(input.lengthOf() == 1 || firstDim == 1) {
|
|
if(!isInplace)
|
|
output.assign(input);
|
|
}
|
|
else if (input.isVector() || shape::isLikeVector(input.getShapeInfo(), temp)) {
|
|
|
|
// apply Fisher-Yates shuffle
|
|
nd4j::graph::RandomGenerator* dRandom = nullptr;
|
|
cudaMalloc(&dRandom, sizeof(nd4j::graph::RandomGenerator));
|
|
cudaMemcpy(dRandom, &rng, sizeof(nd4j::graph::RandomGenerator), cudaMemcpyHostToDevice);
|
|
T* inputBuf = reinterpret_cast<T*>(input.specialBuffer());
|
|
if(isInplace) {
|
|
swapShuffleKernel<T><<<128, 256, 1024, *stream>>>(inputBuf, input.specialShapeInfo(), firstDim, dRandom);
|
|
}
|
|
else {
|
|
std::vector<int> indices(firstDim);
|
|
std::iota(indices.begin(), indices.end(), 0);
|
|
cudaMemcpy(output.specialBuffer(), input.specialBuffer(), sizeof(T), cudaMemcpyDeviceToDevice);
|
|
//output.p<T>(Nd4jLong(0), input.e<T>(0));
|
|
PointersManager pointersManager(context, "helper::randomShuffle_");
|
|
int* indicesDev = reinterpret_cast<int*>(pointersManager.replicatePointer(indices.data(), indices.size() * sizeof(int)));
|
|
T* outputBuf = reinterpret_cast<T*>(output.specialBuffer());
|
|
fillShuffleKernel<T><<<128, 256, 1024, *stream>>>(inputBuf, input.specialShapeInfo(), outputBuf, output.specialShapeInfo(), firstDim, indicesDev, dRandom);
|
|
pointersManager.synchronize();
|
|
}
|
|
// rng.rewindH(firstDim - 1);
|
|
cudaFree(dRandom);
|
|
}
|
|
else {
|
|
|
|
// evaluate sub-arrays list of input array through all dimensions excluding first one
|
|
std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input.rankOf(), {0});
|
|
auto subArrsListIn = input.allTensorsAlongDimension(dimensions);
|
|
|
|
// apply Fisher-Yates shuffle
|
|
if(isInplace) {
|
|
for(int i = firstDim - 1; i > 0; --i) {
|
|
int r = rng.relativeInt(i) % i;
|
|
|
|
if(i != r)
|
|
subArrsListIn.at(i)->swapUnsafe(*subArrsListIn.at(r));
|
|
}
|
|
}
|
|
else {
|
|
// evaluate sub-arrays list of output array through all dimensions excluding first one
|
|
auto subArrsListOut = output.allTensorsAlongDimension(dimensions);
|
|
std::vector<int> indices(firstDim);
|
|
std::iota(indices.begin(), indices.end(), 0);
|
|
bool isZeroShuffled = false;
|
|
|
|
for(int i = firstDim - 1; i > 0; --i) {
|
|
int r = rng.relativeInt(i) % i;
|
|
subArrsListOut.at(i)->assign(subArrsListIn.at(indices[r]));
|
|
if(r == 0)
|
|
isZeroShuffled = true;
|
|
|
|
if(i != r) {
|
|
subArrsListOut.at(r)->assign(subArrsListIn.at(indices[i]));
|
|
math::nd4j_swap<int>(indices[i], indices[r]);
|
|
}
|
|
}
|
|
if(!isZeroShuffled)
|
|
subArrsListOut.at(0)->assign(subArrsListIn.at(0));
|
|
}
|
|
rng.rewindH(firstDim-1);
|
|
}
|
|
NDArray::registerSpecialUse({&output}, {&input});
|
|
|
|
}
|
|
|
|
void randomShuffle(nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace) {
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), randomShuffle_, (context, input, output, rng, isInplace), LIBND4J_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void randomShuffle_, (nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::graph::RandomGenerator& rng, const bool isInplace), LIBND4J_TYPES);
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void eye(nd4j::LaunchContext * context, NDArray& output) {
|
|
|
|
output.setIdentity();
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static __global__ void clipByNormInplaceKernel(Nd4jLong numOfSubArrs, T* inputBuffer, Nd4jLong* shape, Nd4jLong* inputOffsets, T* norm2Buf, Nd4jLong* norm2shape, T clipNorm) {
|
|
for (int arr = blockIdx.x; arr < numOfSubArrs; arr += gridDim.x) {
|
|
__shared__ T* z;
|
|
__shared__ Nd4jLong len;
|
|
if (threadIdx.x == 0) {
|
|
len = shape::length(shape);
|
|
z = inputBuffer + inputOffsets[arr];
|
|
}
|
|
__syncthreads();
|
|
for (int j = threadIdx.x; j < len; j+= blockDim.x) {
|
|
auto xIndex = shape::getIndexOffset(j, shape);
|
|
|
|
if(norm2Buf[arr] > clipNorm)
|
|
z[xIndex] *= clipNorm / norm2Buf[arr]; // case with ews = 1 and ordering is 'c'
|
|
}
|
|
}
|
|
}
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static __global__ void clipByNormKernel(Nd4jLong numOfSubArrs, T* inputBuffer, Nd4jLong* shape, Nd4jLong* inputOffsets, T* outputBuffer, Nd4jLong* outputShape, Nd4jLong* outputOffsets, T* norm2Buf, Nd4jLong* norm2shape, T clipNorm) {
|
|
|
|
for (Nd4jLong arr = blockIdx.x; arr < numOfSubArrs; arr += gridDim.x) {
|
|
__shared__ T* x, *z;
|
|
__shared__ Nd4jLong lenZ;
|
|
__shared__ T norm2;
|
|
|
|
if (threadIdx.x == 0) {
|
|
x = inputBuffer + inputOffsets[arr];
|
|
z = outputBuffer + outputOffsets[arr];
|
|
lenZ = shape::length(outputShape);
|
|
norm2 = norm2Buf[shape::getIndexOffset(arr, norm2shape)];
|
|
}
|
|
__syncthreads();
|
|
for (Nd4jLong j = threadIdx.x; j < lenZ; j+= blockDim.x) {
|
|
auto xIndex = shape::getIndexOffset(j, shape);
|
|
auto zIndex = shape::getIndexOffset(j, outputShape);
|
|
if(norm2 > clipNorm) {
|
|
z[zIndex] = x[xIndex] * clipNorm / norm2; // case with ews = 1 and ordering is 'c'
|
|
} else {
|
|
z[zIndex] = x[xIndex];
|
|
}
|
|
//printf("%lld: %lf %lf\n", j, z[zIndex], x[xIndex]);
|
|
}
|
|
__syncthreads();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void clipByNorm_(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, NDArray const& clipNormA, const bool isInplace) {
|
|
const int rank = input.rankOf();
|
|
auto norm2 = input.reduceAlongDimension(reduce::Norm2, dimensions);
|
|
clipNormA.syncToHost();
|
|
//norm2.printBuffer("Norm2");
|
|
T const clipNorm = clipNormA.e<T>(0);
|
|
//clipNormA.printBuffer("ClipNorm");
|
|
auto stream = context->getCudaStream();
|
|
if (isInplace) {
|
|
if(norm2.lengthOf() == 1) {
|
|
norm2.syncToHost();
|
|
T norm2Val = norm2.e<T>(0);
|
|
if(norm2Val > clipNorm)
|
|
input *= clipNorm / norm2Val;
|
|
}
|
|
else {
|
|
|
|
std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(rank, dimensions);
|
|
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(input.getShapeInfo(), dimsToExclude);
|
|
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions);
|
|
//auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), dimsToExclude);
|
|
T* inputBuffer = reinterpret_cast<T*>(input.specialBuffer());
|
|
T* norm2buf = reinterpret_cast<T*>(norm2.specialBuffer());
|
|
|
|
clipByNormInplaceKernel<T><<<256, 512, 1024, *stream>>>(numOfSubArrs, inputBuffer, packX.specialShapeInfo(), packX.specialOffsets(), norm2buf, norm2.specialShapeInfo(), clipNorm);
|
|
}
|
|
}
|
|
else {
|
|
|
|
if(norm2.lengthOf() == 1) {
|
|
norm2.syncToHost();
|
|
T norm2Val = norm2.e<T>(0);
|
|
|
|
if(norm2Val > clipNorm)
|
|
output.assign( input * (clipNorm / norm2Val));
|
|
else
|
|
output.assign( input );
|
|
}
|
|
else {
|
|
|
|
std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(rank, dimensions);
|
|
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(input.getShapeInfo(), dimsToExclude);
|
|
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimensions);
|
|
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output.getShapeInfo(), dimensions);
|
|
T* inputBuffer = reinterpret_cast<T*>(input.specialBuffer());
|
|
T* norm2buf = reinterpret_cast<T*>(norm2.specialBuffer());
|
|
T* outputBuffer = reinterpret_cast<T*>(output.specialBuffer());
|
|
|
|
clipByNormKernel<T><<<256, 512, 1024, *stream>>>(numOfSubArrs, inputBuffer, packX.specialShapeInfo(), packX.specialOffsets(), outputBuffer, packZ.specialShapeInfo(), packZ.specialOffsets(), norm2buf, norm2.specialShapeInfo(), clipNorm);
|
|
}
|
|
}
|
|
}
|
|
|
|
void clipByNorm(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
|
|
BUILD_SINGLE_SELECTOR(output.dataType(), clipByNorm_, (context, input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void clipByNorm_, (nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace), FLOAT_TYPES);
|
|
|
|
template <typename T>
|
|
void clipByGlobalNorm_(nd4j::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
|
|
NDArray globalNorm = NDArrayFactory::create<T>(0, inputs[0]->getContext()); //sqrt(sum([l2norm(t)**2 for t in t_list]))
|
|
|
|
for (auto i = 0; i < inputs.size(); i++) {
|
|
auto input = inputs[i];
|
|
auto l2norm = input->reduceNumber(reduce::Norm2);
|
|
globalNorm += l2norm * l2norm;
|
|
}
|
|
|
|
globalNorm.applyTransform(transform::Sqrt, globalNorm); // = nd4j::math::nd4j_sqrt(globalNorm);
|
|
outputs[inputs.size()]->p(0, globalNorm);
|
|
globalNorm.syncToHost();
|
|
const T factor = static_cast<T>(clipNorm) / globalNorm.e<T>(0);
|
|
|
|
for (size_t e = 0; e < inputs.size(); e++) {
|
|
// all-reduce
|
|
auto input = inputs[e];
|
|
auto output = outputs[e];
|
|
|
|
if (globalNorm.e<double>(0) <= clipNorm) {
|
|
output->assign(input);
|
|
}
|
|
else {
|
|
|
|
auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
|
|
input->applyLambda(lambda, *output);
|
|
}
|
|
}
|
|
}
|
|
|
|
void clipByGlobalNorm(nd4j::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
|
|
BUILD_SINGLE_SELECTOR(outputs[0]->dataType(), clipByGlobalNorm_, (context, inputs, clipNorm, workspace, outputs, isInplace), FLOAT_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void clipByGlobalNorm_, (nd4j::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace), FLOAT_TYPES);
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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static void clipByAveraged_(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
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auto cn = clipNorm.e<T>(0);
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if (dimensions.size() == 0) {
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// all-reduce
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T n2 = input.reduceNumber(reduce::Norm2).e<T>(0) / static_cast<T>(input.lengthOf());
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if (n2 <= cn) {
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if (!isInplace)
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output.assign(input);
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}
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else {
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const T factor = cn / n2;
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//auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
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//input.applyLambda<T>(lambda, output);
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output.assign(input * factor);
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}
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}
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else {
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// along dimension
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auto norm2 = input.reduceAlongDimension(reduce::Norm2, dimensions, false);
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if (!isInplace)
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output.assign(input);
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auto tads = output.allTensorsAlongDimension(dimensions);
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auto outTads = output.allTensorsAlongDimension(dimensions);
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// TODO: make this CUDA-compliant somehow
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for (int e = 0; e < tads.size(); e++) {
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T n2 = norm2.e<T>(e) / static_cast<T>(tads.at(e)->lengthOf());
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const T factor = cn / n2;
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if (n2 > cn) {
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//auto lambda = LAMBDA_T(_x, factor) {return _x * factor;};
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tads.at(e)->applyScalar(scalar::Multiply, factor, *outTads.at(e));//applyLambda<T>(lambda, &output);
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}
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}
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}
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}
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void clipByAveraged(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
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BUILD_SINGLE_SELECTOR(input.dataType(), clipByAveraged_, (context, input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void clipByAveraged_, (nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace), FLOAT_TYPES);
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/*
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if (d1 > params[1])
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return params[1];
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else if (d1 < params[0])
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return params[0];
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else return d1;
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*/
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template <typename T>
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static void __global__ clipByValueKernel(void* input, Nd4jLong* inputShape, void* output, Nd4jLong* outputShape, double leftBound, double rightBound) {
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__shared__ T* outputBuf;
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|
__shared__ T* inputBuf;
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|
__shared__ Nd4jLong length;
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__shared__ bool linearBuffers;
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|
if (threadIdx.x == 0) {
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outputBuf = reinterpret_cast<T *>(output);
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inputBuf = reinterpret_cast<T *>(input);
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length = shape::length(inputShape);
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linearBuffers = shape::elementWiseStride(inputShape) == shape::elementWiseStride(outputShape) && shape::elementWiseStride(inputShape) == 1;
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}
|
|
__syncthreads();
|
|
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
|
const auto step = gridDim.x * blockDim.x;
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|
|
|
for (Nd4jLong e = tid; e < length; e += step) {
|
|
if (linearBuffers) {
|
|
if (inputBuf[e] > rightBound) outputBuf[e] = (T) rightBound;
|
|
else if (inputBuf[e] < leftBound) outputBuf[e] = (T) leftBound;
|
|
else outputBuf[e] = inputBuf[e];
|
|
}
|
|
else {
|
|
auto inputOffset = shape::getIndexOffset(e, inputShape);
|
|
auto outputOffset = shape::getIndexOffset(e, outputShape);
|
|
if (inputBuf[inputOffset] > rightBound) outputBuf[outputOffset] = (T) rightBound;
|
|
else if (inputBuf[inputOffset] < leftBound) outputBuf[outputOffset] = (T) leftBound;
|
|
else outputBuf[outputOffset] = inputBuf[outputOffset];
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T>
|
|
static void clipByValue_(nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) {
|
|
auto stream = context->getCudaStream();
|
|
if (!input.isActualOnDeviceSide())
|
|
input.syncToDevice();
|
|
NDArray::prepareSpecialUse({&output}, {&input});
|
|
clipByValueKernel<T><<<256, 512, 8192, *stream>>>(input.specialBuffer(), input.specialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), leftBound, rightBound);
|
|
NDArray::registerSpecialUse({&output}, {&input});
|
|
}
|
|
|
|
void clipByValue(nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) {
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), clipByValue_, (context, input, leftBound, rightBound, output), FLOAT_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void clipByValue_, (nd4j::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output);, FLOAT_TYPES);
|
|
|
|
}
|
|
}
|
|
}
|
|
|