919 lines
43 KiB
Plaintext
919 lines
43 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 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 concatCuda(const int numOfArrs, void* pVx, void* pxShapeInfo, void* pVz, void* pzShapeInfo) {
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__shared__ int arrIdx, blocksPerArr;
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__shared__ T *x, *z;
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__shared__ Nd4jLong *zShapeInfo, *xShapeInfo, arrLen, arrLenPerBlock, start, end;
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if (threadIdx.x == 0) {
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blocksPerArr = (gridDim.x + numOfArrs - 1) / numOfArrs; // ceil
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arrIdx = blockIdx.x / blocksPerArr;
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x = reinterpret_cast<T*>(reinterpret_cast<void**>(pVx)[arrIdx]);
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z = reinterpret_cast<T*>(reinterpret_cast<void**>(pVz)[arrIdx]);
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xShapeInfo = reinterpret_cast<Nd4jLong**>(pxShapeInfo)[arrIdx];
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zShapeInfo = reinterpret_cast<Nd4jLong**>(pzShapeInfo)[arrIdx];
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arrLen = shape::length(xShapeInfo);
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arrLenPerBlock = (arrLen + blocksPerArr - 1) / blocksPerArr; // ceil
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start = (blockIdx.x % blocksPerArr) * arrLenPerBlock;
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end = (start + arrLenPerBlock) > arrLen ? arrLen : (start + arrLenPerBlock);
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}
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__syncthreads();
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for (Nd4jLong i = start + threadIdx.x; i < end; i += blockDim.x)
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z[shape::getIndexOffset(i, zShapeInfo, arrLen)] = x[shape::getIndexOffset(i, xShapeInfo, arrLen)];
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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__host__ static void concatCudaLauncher(const int numOfArrs, const cudaStream_t *stream, void* pVx, void* pxShapeInfo, void* pVz, void* pzShapeInfo) {
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concatCuda<T><<<512, 256, 1024, *stream>>>(numOfArrs, pVx, pxShapeInfo, pVz, pzShapeInfo);
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}
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///////////////////////////////////////////////////////////////////
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// x - input, y - paddings, z - output
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template<typename X, typename Y>
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__global__ static void padCuda(const int mode,
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const void *vx, const Nd4jLong *xShapeInfo,
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const void *vy, const Nd4jLong *yShapeInfo,
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void *vz, const Nd4jLong *zShapeInfo,
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const void *vPadVal) {
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const X padVal = *reinterpret_cast<const X*>(vPadVal);
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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__ int rank, rankMinusOne;
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__shared__ Nd4jLong zLen, yLen, totalThreads, *coords, *xShape, *zShape, *xStride, *zStride, shift1, shift2, yStride0;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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coords = reinterpret_cast<Nd4jLong*>(shmem);
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zLen = shape::length(zShapeInfo);
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xShape = shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo));
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zShape = shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo));
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xStride = shape::stride(const_cast<Nd4jLong*>(xShapeInfo));
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zStride = shape::stride(const_cast<Nd4jLong*>(zShapeInfo));
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yStride0 = shape::stride(const_cast<Nd4jLong*>(yShapeInfo))[0];
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rank = shape::rank(xShapeInfo);
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zLen = shape::length(zShapeInfo);
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yLen = 2 * rank;
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rankMinusOne = rank - 1;
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totalThreads = gridDim.x * blockDim.x;
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shift1 = mode == 1 ? 0 : 1; // REFLECT : SYMMETRIC
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shift2 = mode == 1 ? 2 : 1; // REFLECT : SYMMETRIC
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}
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__syncthreads();
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auto xzCoord = coords + threadIdx.x * rank; // we use xzCoord storage both for x and z arrays
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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if(mode == 0) { // CONSTANT case
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for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
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shape::index2coords(rank, zShape, i, zLen, xzCoord);
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const auto zOffset = shape::getOffset(0, zShape, zStride, xzCoord, rank);
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bool within = true;
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for(int j = rankMinusOne; j >= 0; --j) {
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if(xShape[j] == zShape[j]) continue;
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const auto left = y[shape::getIndexOffset(yStride0 * j, yShapeInfo, yLen)];
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if(xzCoord[j] < left || xzCoord[j] >= left + xShape[j]) {within = false; break;}
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else {xzCoord[j] = xzCoord[j] - left;}
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}
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if(within)
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z[zOffset] = x[shape::getOffset(0, xShape, xStride, xzCoord, rank)];
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else
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z[zOffset] = padVal;
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}
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}
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else { // REFLECT and SYMMETRIC cases
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for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
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shape::index2coords(rank, zShape, i, zLen, xzCoord);
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const auto zOffset = shape::getOffset(0, zShape, zStride, xzCoord, rank);
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for(int j = rankMinusOne; j >= 0; --j) {
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if(xShape[j] == zShape[j]) continue;
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xzCoord[j] = xzCoord[j] - y[shape::getIndexOffset(yStride0 * j, yShapeInfo, yLen)]; // are ready to fill middle (within input dimension range)
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if(xzCoord[j] < 0) xzCoord[j] = -xzCoord[j] - shift1; // means fill from left
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else if(xzCoord[j] >= xShape[j]) xzCoord[j] = 2 * xShape[j] - xzCoord[j] - shift2; // means fill from right
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}
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const auto xOffset = shape::getOffset(0, xShape, xStride, xzCoord, rank);
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z[zOffset] = x[xOffset];
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}
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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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static void padCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
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const int mode,
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const void *vx, const Nd4jLong *xShapeInfo,
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const void *vy, const Nd4jLong *yShapeInfo,
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void *vz, const Nd4jLong *zShapeInfo,
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const void* padVal) {
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padCuda<X,Y><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(mode, vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, padVal);
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}
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///////////////////////////////////////////////////////////////////
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void pad(nd4j::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, const NDArray& padValue) {
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PointersManager manager(context, "pad");
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NDArray::prepareSpecialUse({&output}, {&input, &paddings, &padValue});
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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 = 8 * threadsPerBlock * output.rankOf() + 128;
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const auto xType = input.dataType();
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const auto yType = paddings.dataType();
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BUILD_DOUBLE_SELECTOR(xType, yType, padCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), mode, input.getSpecialBuffer(), input.getSpecialShapeInfo(), paddings.getSpecialBuffer(), paddings.getSpecialShapeInfo(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), padValue.getSpecialBuffer()), LIBND4J_TYPES, INTEGER_TYPES);
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NDArray::registerSpecialUse({&output}, {&input, &paddings, &padValue});
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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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static void triuBP_(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) {
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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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BUILD_SINGLE_SELECTOR(gradO.dataType(), triuBP_, (context, input, gradO, gradI, diagonal), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void triuBP_, (nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal), LIBND4J_TYPES);
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void trace_(nd4j::LaunchContext * context, const NDArray& input, NDArray& output) {
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}
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void trace(nd4j::LaunchContext * context, const NDArray& input, NDArray& output) {
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BUILD_SINGLE_SELECTOR(input.dataType(), trace_, (context, input, output), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void trace_, (nd4j::LaunchContext * context, const NDArray& input, NDArray& output), LIBND4J_TYPES);
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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void randomShuffle_(nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::random::RandomBuffer& rng, const bool isInplace) {
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}
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void randomShuffle(nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::random::RandomBuffer& rng, const bool isInplace) {
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BUILD_SINGLE_SELECTOR(input.dataType(), randomShuffle_, (context, input, output, rng, isInplace), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void randomShuffle_, (nd4j::LaunchContext * context, NDArray& input, NDArray& output, nd4j::random::RandomBuffer& rng, const bool isInplace), LIBND4J_TYPES);
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////////////////////////////////////////////////////////////////////////
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void invertPermutation(nd4j::LaunchContext * context, const NDArray& input, NDArray& output) {
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}
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////////////////////////////////////////////////////////////////////////
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template<typename T>
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static void gatherND_(nd4j::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) {
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}
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void gatherND(nd4j::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) {
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BUILD_SINGLE_SELECTOR(input.dataType(), gatherND_, (context, input, indices, output), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void gatherND_, (nd4j::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output), LIBND4J_TYPES);
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//////////////////////////////////////////////////////////////////////////
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void eye(nd4j::LaunchContext * context, NDArray& output) {
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}
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//////////////////////////////////////////////////////////////////////////
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void scatterUpdate(nd4j::LaunchContext * context, NDArray& operand, NDArray& updates, const std::vector<int>* intArgs) {
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T, typename Z>
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static __global__ void global_mergeMaxIndex_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
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auto output = reinterpret_cast<Z*>(voutput);
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const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += step) {
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T mVal = -DataTypeUtils::max<T>();
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Z mIdx(0);
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<T*>(inArrs[i]);
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auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
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auto val = x[shape::getIndexOffset(e, xShape, length)];;
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if (mVal < val)
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mIdx = static_cast<Z>(e);
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}
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__syncthreads();
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output[shape::getIndexOffset(e, outputShape, length)] = mIdx;
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}
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}
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template <typename T, typename Z>
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static void mergeMaxIndex_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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std::vector<void *> inBuffers(inArrs.size());
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std::vector<void *> inShapes(inArrs.size());
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for (int e = 0; e < inArrs.size(); e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeMaxIndex");
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auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
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auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
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auto length = output.lengthOf();
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global_mergeMaxIndex_<T,Z><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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manager.synchronize();
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}
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void mergeMaxIndex(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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BUILD_DOUBLE_SELECTOR(inArrs[0]->dataType(), output.dataType(), mergeMaxIndex_, (context, inArrs, output), LIBND4J_TYPES, INTEGER_TYPES);
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}
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BUILD_DOUBLE_TEMPLATE(template void mergeMaxIndex_, (nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output), LIBND4J_TYPES, INTEGER_TYPES);
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static __global__ void global_mergeMax_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
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auto output = reinterpret_cast<T*>(voutput);
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const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += step) {
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T mVal = -DataTypeUtils::max<T>();
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<T*>(inArrs[i]);
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auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
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auto val = x[shape::getIndexOffset(e, xShape, length)];;
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if (mVal < val)
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mVal = val;
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}
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__syncthreads();
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output[shape::getIndexOffset(e, outputShape, length)] = mVal;
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}
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}
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template<typename T>
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static void mergeMax_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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std::vector<void *> inBuffers(inArrs.size());
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std::vector<void *> inShapes(inArrs.size());
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for (int e = 0; e < inArrs.size(); e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeMax");
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auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
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auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
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auto length = output.lengthOf();
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global_mergeMax_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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manager.synchronize();
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}
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BUILD_SINGLE_TEMPLATE(template void mergeMax_, (nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output), LIBND4J_TYPES);
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void mergeMax(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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BUILD_SINGLE_SELECTOR(output.dataType(), mergeMax_, (context, inArrs, output), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static __global__ void global_mergeAvg_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
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auto output = reinterpret_cast<T*>(voutput);
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const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += step) {
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T sum(0.0f);
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<T*>(inArrs[i]);
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auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
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sum += x[shape::getIndexOffset(e, xShape, length)];
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}
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output[shape::getIndexOffset(e, outputShape, length)] = sum / numArrays;
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}
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}
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template<typename T>
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static void mergeAvg_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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std::vector<void *> inBuffers(inArrs.size());
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std::vector<void *> inShapes(inArrs.size());
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for (int e = 0; e < inArrs.size(); e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeAvg");
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auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
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auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
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auto length = output.lengthOf();
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global_mergeAvg_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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manager.synchronize();
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}
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BUILD_SINGLE_TEMPLATE(template void mergeAvg_, (nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output), LIBND4J_TYPES);
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void mergeAvg(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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BUILD_SINGLE_SELECTOR(output.dataType(), mergeAvg_, (context, inArrs, output), LIBND4J_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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|
template <typename T>
|
|
static __global__ void global_mergeAdd_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
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|
auto output = reinterpret_cast<T*>(voutput);
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|
|
|
const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
|
|
const auto step = gridDim.x * blockDim.x;
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|
|
|
for (Nd4jLong e = tid; e < length; e += step) {
|
|
T sum(0.0f);
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|
|
|
for (int i = 0; i < numArrays; i++) {
|
|
auto x = reinterpret_cast<T*>(inArrs[i]);
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|
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
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|
|
|
sum += x[shape::getIndexOffset(e, xShape, length)];
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|
}
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|
|
|
output[shape::getIndexOffset(e, outputShape, length)] = sum;
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|
}
|
|
}
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|
|
|
template<typename T>
|
|
static void mergeAdd_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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std::vector<void *> inBuffers(inArrs.size());
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std::vector<void *> inShapes(inArrs.size());
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|
|
|
for (int e = 0; e < inArrs.size(); e++) {
|
|
inBuffers[e] = inArrs[e]->getSpecialBuffer();
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|
inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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|
}
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|
|
|
PointersManager manager(context, "mergeAdd");
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|
|
|
auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
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|
auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
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auto length = output.lengthOf();
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|
|
|
global_mergeAdd_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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|
|
|
manager.synchronize();
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|
}
|
|
BUILD_SINGLE_TEMPLATE(template void mergeAdd_, (nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output), LIBND4J_TYPES);
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|
|
void mergeAdd(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
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|
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAdd_, (context, inArrs, output), LIBND4J_TYPES);
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|
}
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|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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|
template <typename T>
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|
static __global__ void clipByNormInplaceKernel(Nd4jLong numOfSubArrs, T* inputBuffer, Nd4jLong* shape, Nd4jLong* inputOffsets, T* norm2Buf, Nd4jLong* norm2shape, T clipNorm) {
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|
for (int arr = blockIdx.x; arr < numOfSubArrs; arr += gridDim.x) {
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|
__shared__ T* z;
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|
__shared__ Nd4jLong len;
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|
if (threadIdx.x == 0) {
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|
len = shape::length(shape);
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|
z = inputBuffer + inputOffsets[arr];
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|
}
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|
__syncthreads();
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|
for (int j = threadIdx.x; j < len; j+= blockDim.x) {
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|
auto xIndex = shape::getIndexOffset(j, shape, len);
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|
|
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if(norm2Buf[arr] > clipNorm)
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|
z[xIndex] *= clipNorm / norm2Buf[arr]; // case with ews = 1 and ordering is 'c'
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|
}
|
|
}
|
|
}
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
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 lenX, lenZ;
|
|
__shared__ T norm2;
|
|
|
|
if (threadIdx.x == 0) {
|
|
lenX = shape::length(shape);
|
|
x = inputBuffer + inputOffsets[arr];
|
|
z = outputBuffer + outputOffsets[arr];
|
|
lenZ = shape::length(outputShape);
|
|
norm2 = norm2Buf[shape::getIndexOffset(arr, norm2shape, numOfSubArrs)];
|
|
//printf("%d: %lf (vs %lf) %lld %lld\n", arr, norm2, clipNorm, lenX, lenZ);
|
|
}
|
|
__syncthreads();
|
|
for (Nd4jLong j = threadIdx.x; j < lenZ; j+= blockDim.x) {
|
|
auto xIndex = shape::getIndexOffset(j, shape, lenX);
|
|
auto zIndex = shape::getIndexOffset(j, outputShape, lenZ);
|
|
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.reduceAlongDims(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>
|
|
static void clipByGlobalNorm_(nd4j::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
|
|
|
|
}
|
|
|
|
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);
|
|
}
|
|
|
|
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);
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void clipByNormBP_(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm) {
|
|
|
|
}
|
|
|
|
void clipByNormBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm) {
|
|
BUILD_SINGLE_SELECTOR(gradI.dataType(), clipByNormBP_, (context, input, gradO, gradI, dimensions, clipNorm), FLOAT_TYPES);
|
|
}
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void clipByNormBP_, (nd4j::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm), FLOAT_TYPES);
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
static void clipByAveraged_(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
|
|
auto cn = clipNorm.e<T>(0);
|
|
if (dimensions.size() == 0) {
|
|
// all-reduce
|
|
T n2 = input.reduceNumber(reduce::Norm2).e<T>(0) / input.lengthOf();
|
|
if (n2 <= cn) {
|
|
if (!isInplace)
|
|
output.assign(input);
|
|
}
|
|
else {
|
|
const T factor = cn / n2;
|
|
//auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
|
|
//input.applyLambda<T>(lambda, &output);
|
|
output.assign(input * factor);
|
|
}
|
|
}
|
|
else {
|
|
// along dimension
|
|
auto norm2 = input.reduceAlongDims(reduce::Norm2, dimensions, false);
|
|
if (!isInplace)
|
|
output.assign(input);
|
|
auto tads = output.allTensorsAlongDimension(dimensions);
|
|
auto outTads = output.allTensorsAlongDimension(dimensions);
|
|
// TODO: make this CUDA-compliant somehow
|
|
for (int e = 0; e < tads->size(); e++) {
|
|
T n2 = norm2.e<T>(e) / tads->at(e)->lengthOf();
|
|
const T factor = cn / n2;
|
|
if (n2 > cn) {
|
|
//auto lambda = LAMBDA_T(_x, factor) {return _x * factor;};
|
|
tads->at(e)->applyScalar(scalar::Multiply, factor, outTads->at(e));//applyLambda<T>(lambda, &output);
|
|
}
|
|
}
|
|
delete tads;
|
|
delete outTads;
|
|
}
|
|
}
|
|
|
|
void clipByAveraged(nd4j::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), clipByAveraged_, (context, input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES);
|
|
}
|
|
|
|
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);
|
|
|
|
/*
|
|
if (d1 > params[1])
|
|
return params[1];
|
|
else if (d1 < params[0])
|
|
return params[0];
|
|
else return d1;
|
|
*/
|
|
template <typename T>
|
|
static void __global__ clipByValueKernel(void* input, Nd4jLong* inputShape, void* output, Nd4jLong* outputShape, double leftBound, double rightBound) {
|
|
__shared__ T* outputBuf;
|
|
__shared__ T* inputBuf;
|
|
__shared__ Nd4jLong length;
|
|
__shared__ bool linearBuffers;
|
|
if (threadIdx.x == 0) {
|
|
outputBuf = reinterpret_cast<T *>(output);
|
|
inputBuf = reinterpret_cast<T *>(input);
|
|
length = shape::length(inputShape);
|
|
linearBuffers = shape::elementWiseStride(inputShape) == shape::elementWiseStride(outputShape) && shape::elementWiseStride(inputShape) == 1;
|
|
}
|
|
__syncthreads();
|
|
const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
|
|
const auto step = gridDim.x * blockDim.x;
|
|
|
|
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, length);
|
|
auto outputOffset = shape::getIndexOffset(e, outputShape, length);
|
|
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);
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static __global__ void mirrorPadLinearKernel(void const* vx, Nd4jLong* xShape, void* vz, Nd4jLong* zShape, Nd4jLong leftSide, Nd4jLong leftSideCorrected, Nd4jLong xLen, Nd4jLong len, Nd4jLong zLen) {
|
|
|
|
__shared__ T const* x;
|
|
__shared__ T* z;
|
|
if (threadIdx.x == 0) {
|
|
x = reinterpret_cast<T const*>(vx);
|
|
z = reinterpret_cast<T*>(vz);
|
|
}
|
|
__syncthreads();
|
|
auto start = blockIdx.x * blockDim.x + threadIdx.x;
|
|
auto step = blockDim.x * gridDim.x;
|
|
|
|
for(int i = start; i < zLen; i+= step) {
|
|
auto zIndex = shape::getIndexOffset(i, zShape, zLen);
|
|
auto xIndex = shape::getIndexOffset(len - i, xShape, xLen);
|
|
|
|
if (i < leftSide) // left side
|
|
xIndex = shape::getIndexOffset(leftSideCorrected - i, xShape, xLen);
|
|
|
|
else if(i >= leftSide && i < leftSide + xLen) // middle
|
|
xIndex = shape::getIndexOffset(i - leftSide, xShape, xLen);
|
|
|
|
// else // right side
|
|
// z[i] = x[len - i];
|
|
z[zIndex] = x[xIndex];
|
|
}
|
|
|
|
}
|
|
|
|
template <typename F, typename I>
|
|
static __global__ void mirrorPadKernel(void const* vx, Nd4jLong* xShape, void* vz, Nd4jLong* zShape, Nd4jLong outLen, void const* paddings, Nd4jLong* paddingShape, int reflBorder) {
|
|
|
|
__shared__ F const* x;
|
|
__shared__ I const* pads;
|
|
__shared__ F* z;
|
|
__shared__ Nd4jLong zRank, rank;
|
|
__shared__ Nd4jLong* xShapeOf, *xStrideOf, *padsShapeOf, *padsStrideOf;
|
|
__shared__ Nd4jLong* zShapeOf, *zStrideOf;
|
|
__shared__ Nd4jLong* xIdx;
|
|
if (threadIdx.x == 0) {
|
|
extern __shared__ unsigned char shmem[];
|
|
xIdx = reinterpret_cast<Nd4jLong*>(shmem);
|
|
rank = shape::rank(xShape);
|
|
|
|
x = reinterpret_cast<F const*>(vx);//
|
|
pads = reinterpret_cast<I const*>(paddings);
|
|
z = reinterpret_cast<F*>(vz);
|
|
xShapeOf = shape::shapeOf(xShape);
|
|
xStrideOf = shape::stride(xShape);
|
|
zShapeOf = shape::shapeOf(zShape);
|
|
zRank = shape::rank(zShape);
|
|
zStrideOf = shape::stride(zShape);
|
|
padsShapeOf = shape::shapeOf(paddingShape);
|
|
padsStrideOf = shape::stride(paddingShape);
|
|
}
|
|
__syncthreads();
|
|
auto start = threadIdx.x + blockIdx.x * blockDim.x;
|
|
auto step = blockDim.x * gridDim.x;
|
|
|
|
for(Nd4jLong i = start; i < outLen; i+= step) {
|
|
auto xzCoord = xIdx + threadIdx.x * rank;
|
|
//auto zxCoord = xIdx + (threadIdx.x + threadIdx.x % 2 + 1) * rank;
|
|
|
|
shape::index2coords(rank, zShapeOf, i, xzCoord);
|
|
auto outOffset = shape::getOffset(0, zShapeOf, zStrideOf, xzCoord, rank);
|
|
// auto intStep = blockDim.y * gridDim.y;
|
|
for(int j = 0; j < rank; j++) {
|
|
|
|
const Nd4jLong inLen = shape::sizeAt(xShape, j);
|
|
Nd4jLong coords[2] = {j, 0};
|
|
auto padOffset = shape::getOffset(0, padsShapeOf, padsStrideOf, coords, 2); // padding already has rank 2
|
|
const auto leftSide = pads[padOffset];
|
|
const auto leftSideCorrected = leftSide - reflBorder;
|
|
const Nd4jLong len = 2 * (inLen - 1) + leftSide + reflBorder;
|
|
|
|
if(xzCoord[j] < leftSide) // left side
|
|
xzCoord[j] = leftSideCorrected - xzCoord[j];
|
|
|
|
else if(xzCoord[j] >= leftSide && xzCoord[j] < leftSide + inLen) // middle
|
|
xzCoord[j] = xzCoord[j] - leftSide;
|
|
|
|
else if (len > xzCoord[j]) // right side
|
|
xzCoord[j] = len - xzCoord[j];
|
|
else
|
|
xzCoord[j] = xzCoord[j] - len;
|
|
}
|
|
|
|
auto inOffset = shape::getOffset(0, xShapeOf, xStrideOf, xzCoord, rank);
|
|
z[outOffset] = x[inOffset];
|
|
}
|
|
}
|
|
|
|
template<typename F, typename I>
|
|
static void mirrorPad_(nd4j::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
|
|
// mode: 0 - REFLECT, else - SYMMETRIC
|
|
const int reflBorder = (bool)mode ? 1 : 0;
|
|
const int rank = input.rankOf();
|
|
const Nd4jLong outLen = output.lengthOf();
|
|
auto stream = context->getCudaStream();
|
|
NDArray::prepareSpecialUse({&output}, {&input, &paddings});
|
|
|
|
if(rank <= 1) {
|
|
|
|
const Nd4jLong inLen = input.lengthOf();
|
|
const auto leftSide = paddings.e<Nd4jLong>(0);
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const auto leftSideCorrected = leftSide - reflBorder;
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const Nd4jLong len = 2*(inLen-1) + leftSide + reflBorder;
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|
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mirrorPadLinearKernel<F><<<256, 512, 256, *stream>>>(input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), leftSide, leftSideCorrected, inLen, len, outLen);
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nd4j::DebugHelper::checkErrorCode(stream, "helpers::mirrorPadLinearKernel(...) failed");
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|
}
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|
else {
|
|
mirrorPadKernel<F, I><<<256, 256, 8192, *stream>>>(input.getSpecialBuffer(), input.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), outLen, paddings.getSpecialBuffer(), paddings.getSpecialShapeInfo(), reflBorder);
|
|
nd4j::DebugHelper::checkErrorCode(stream, "helpers::mirrorPadKernel(...) failed");
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|
}
|
|
NDArray::registerSpecialUse({&output}, {&input, &paddings});
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|
}
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|
|
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void mirrorPad(nd4j::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
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BUILD_DOUBLE_SELECTOR(input.dataType(), paddings.dataType(), mirrorPad_, (context, input, paddings, output, mode), LIBND4J_TYPES, INTEGER_TYPES);
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|
}
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|
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BUILD_DOUBLE_TEMPLATE(template void mirrorPad_, (nd4j::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode), LIBND4J_TYPES, INTEGER_TYPES);
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|
|
|
//////////////////////////////////////////////////////////////////////////
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|
void concat(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output, const int axis) {
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|
|
|
const int numOfArrs = inArrs.size();
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|
for(int i = 0; i < numOfArrs; ++i)
|
|
if(!inArrs[i]->isActualOnDeviceSide()) inArrs[i]->syncToDevice();
|
|
|
|
const int rank = inArrs[0]->rankOf();
|
|
const int rank2 = 2*rank;
|
|
std::vector<std::vector<Nd4jLong>> indices(numOfArrs, std::vector<Nd4jLong>(rank2,0));
|
|
|
|
// take into account indices for first array
|
|
indices[0][2 * axis + 1] = inArrs[0]->sizeAt(axis);
|
|
|
|
// loop through the rest of input arrays
|
|
for(int i = 1; i < numOfArrs; ++i) {
|
|
indices[i][2 * axis] = indices[i-1][2 * axis + 1]; // index start from
|
|
indices[i][2 * axis + 1] = indices[i-1][2 * axis + 1] + inArrs[i]->sizeAt(axis); // index end with (excluding)
|
|
}
|
|
|
|
std::vector<NDArray*> outSubArrs(numOfArrs);
|
|
for(int i = 0; i < numOfArrs; ++i)
|
|
outSubArrs[i] = new NDArray(output(indices[i], true));
|
|
|
|
// prepare arrays of pointers on buffers and shapes
|
|
std::vector<void*> hOutBuffers(numOfArrs), hInBuffers(numOfArrs);
|
|
std::vector<Nd4jLong*> hOutShapeInfo(numOfArrs), hInShapeInfo(numOfArrs);
|
|
for(int i = 0; i < numOfArrs; ++i) {
|
|
hOutBuffers[i] = outSubArrs[i]->getSpecialBuffer();
|
|
hInBuffers[i] = inArrs[i]->getSpecialBuffer();
|
|
hOutShapeInfo[i] = outSubArrs[i]->getSpecialShapeInfo();
|
|
hInShapeInfo[i] = inArrs[i]->getSpecialShapeInfo();
|
|
}
|
|
|
|
// allocate and copy all buffers and shapes arrays to global memory
|
|
PointersManager manager(context, "helpers::concat");
|
|
void* dOutBuffers = manager.replicatePointer(hOutBuffers.data(), hOutBuffers.size() * sizeof(void*));
|
|
void* dInBuffers = manager.replicatePointer(hInBuffers.data(), hInBuffers.size() * sizeof(void*));
|
|
void* dInShapeInfo = manager.replicatePointer(hInShapeInfo.data(), hInShapeInfo.size() * sizeof(Nd4jLong*));
|
|
void* dOutShapeInfo = manager.replicatePointer(hOutShapeInfo.data(), hOutShapeInfo.size() * sizeof(Nd4jLong*));
|
|
|
|
BUILD_SINGLE_SELECTOR(inArrs[0]->dataType(), concatCudaLauncher, (numOfArrs, context->getCudaStream(), dInBuffers, dInShapeInfo, dOutBuffers, dOutShapeInfo), LIBND4J_TYPES);
|
|
|
|
manager.synchronize();
|
|
|
|
for(int i = 0; i < numOfArrs; ++i)
|
|
delete outSubArrs[i];
|
|
|
|
for(int i = 0; i < numOfArrs; ++i)
|
|
inArrs[i]->tickReadHost();
|
|
|
|
output.tickWriteDevice();
|
|
}
|
|
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void tileBP_(nd4j::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {
|
|
|
|
}
|
|
|
|
void tileBP(nd4j::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {
|
|
BUILD_SINGLE_SELECTOR(gradI.dataType(), tileBP_, (context, gradO, gradI, reps), FLOAT_TYPES);
|
|
}
|
|
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void tileBP_, (nd4j::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps), FLOAT_TYPES);
|
|
|
|
void scatterSimple(const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector<int>& dimensions) {
|
|
|
|
}
|
|
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void concatCudaLauncher, (const int numOfArrs, const cudaStream_t *stream, void* pVx, void* pxShapeInfo, void* pVz, void* pzShapeInfo), LIBND4J_TYPES);
|
|
BUILD_DOUBLE_TEMPLATE(template void padCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const int mode, const void *vx, const Nd4jLong *xShapeInfo, const void *vy, const Nd4jLong *yShapeInfo, void *vz, const Nd4jLong *zShapeInfo, const void* vPadVal), LIBND4J_TYPES, INTEGER_TYPES);
|
|
|
|
}
|
|
}
|
|
}
|