wehub-resource-sync
222 lines
9.0 KiB
Plaintext
222 lines
9.0 KiB
Plaintext
/*
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* ******************************************************************************
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* *
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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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* * See the NOTICE file distributed with this work for additional
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* * information regarding copyright ownership.
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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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//
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <execution/cuda/LaunchDims.h>
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#include <helpers/PointersManager.h>
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#include <math/templatemath.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include "helpers/DebugHelper.h"
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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SD_KERNEL static void pooling3dBPCuda(const void* vx, const LongType* xShapeInfo, const void* vy,
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const LongType* yShapeInfo, void* vz, const LongType* zShapeInfo,
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const int kD, const int kH, const int kW, const int sD, const int sH,
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const int sW, const int pD, const int pH, const int pW, const int dD,
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const int dH, const int dW, const int poolingMode, const int extraParam0) {
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const T* x = reinterpret_cast<const T*>(vx);
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const T* y = reinterpret_cast<const T*>(vy);
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T* z = reinterpret_cast<T*>(vz);
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LongType coord2, coord3, coord4;
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__shared__ int rank, kDeff, kHeff, kWeff, iD, iH, iW, kProd;
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__shared__ LongType yLen, *sharedMem;
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__shared__ LongType* xShape;
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__shared__ LongType* yShape;
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__shared__ LongType* zShape;
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__shared__ LongType* xStride;
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__shared__ LongType* yStride;
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__shared__ LongType* zStride;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<LongType*>(shmem);
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yLen = shape::length(yShapeInfo);
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rank = 5;
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kDeff = kD + (kD - 1) * (dD - 1);
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kHeff = kH + (kH - 1) * (dH - 1);
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kWeff = kW + (kW - 1) * (dW - 1);
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iD = xShapeInfo[3];
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iH = xShapeInfo[4];
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iW = xShapeInfo[5];
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kProd = kD * kH * kW;
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// Cache shape information
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xShape = shape::shapeOf(xShapeInfo);
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yShape = shape::shapeOf(yShapeInfo);
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zShape = shape::shapeOf(zShapeInfo);
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xStride = shape::stride(xShapeInfo);
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yStride = shape::stride(yShapeInfo);
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zStride = shape::stride(zShapeInfo);
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}
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__syncthreads();
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const auto yInd = threadIdx.x + blockIdx.x * blockDim.x;
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if (yInd >= yLen) return;
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auto coords = sharedMem + threadIdx.x * rank;
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INDEX2COORDS(yInd, rank, yShape, coords);
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LongType yOffset;
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COORDS2INDEX(rank, yStride, coords, yOffset);
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int dstart = coords[2] * sD - pD;
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int hstart = coords[3] * sH - pH;
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int wstart = coords[4] * sW - pW;
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int dend = dstart + kDeff;
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int hend = hstart + kHeff;
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int wend = wstart + kWeff;
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if (dstart < 0) dstart += dD * ((-dstart + dD - 1) / dD);
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if (hstart < 0) hstart += dH * ((-hstart + dH - 1) / dH);
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if (wstart < 0) wstart += dW * ((-wstart + dW - 1) / dW);
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if (dend > iD) dend -= dD * ((dend - iD + dD - 1) / dD);
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if (hend > iH) hend -= dH * ((hend - iH + dH - 1) / dH);
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if (wend > iW) wend -= dW * ((wend - iW + dW - 1) / dW);
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switch (poolingMode) {
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/*** max ***/
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case 0: {
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T max = -DataTypeUtils::max<T>();
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for (coords[2] = dstart; coords[2] < dend; coords[2] += dD) {
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for (coords[3] = hstart; coords[3] < hend; coords[3] += dH) {
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for (coords[4] = wstart; coords[4] < wend; coords[4] += dW) {
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LongType xOffset;
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COORDS2INDEX(rank, xStride, coords, xOffset);
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T val = x[xOffset];
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if (val > max) {
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max = val;
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coord2 = coords[2];
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coord3 = coords[3];
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coord4 = coords[4];
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}
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}
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}
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}
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coords[2] = coord2;
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coords[3] = coord3;
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coords[4] = coord4;
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LongType zOffset;
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COORDS2INDEX(rank, zStride, coords, zOffset);
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math::atomics::sd_atomicAdd<T>(&z[zOffset], y[yOffset]);
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} break;
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/*** avg ***/
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case 1: {
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T val = y[yOffset];
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if (extraParam0 == 0) // Exclude padding
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val /= static_cast<T>(math::sd_ceil<double, int>(static_cast<double>(dend - dstart) / static_cast<double>(dD)) *
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math::sd_ceil<double, int>(static_cast<double>(hend - hstart) / static_cast<double>(dH)) *
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math::sd_ceil<double, int>(static_cast<double>(wend - wstart) / static_cast<double>(dW)));
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else if (extraParam0 == 1) // Include padding
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val /= static_cast<T>(kProd);
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for (coords[2] = dstart; coords[2] < dend; coords[2] += dD)
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for (coords[3] = hstart; coords[3] < hend; coords[3] += dH)
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for (coords[4] = wstart; coords[4] < wend; coords[4] += dW) {
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LongType zOffset;
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COORDS2INDEX(rank, zStride, coords, zOffset);
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math::atomics::sd_atomicAdd<T>(&z[zOffset], val);
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}
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} break;
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/*** pnorm ***/
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case 2: {
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T sum = static_cast<T>(0.);
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T val = y[yOffset];
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for (coords[2] = dstart; coords[2] < dend; coords[2] += dD)
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for (coords[3] = hstart; coords[3] < hend; coords[3] += dH)
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for (coords[4] = wstart; coords[4] < wend; coords[4] += dW) {
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LongType xOffset;
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COORDS2INDEX(rank, xStride, coords, xOffset);
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sum += math::sd_pow<T, T, T>(math::sd_abs<T,T>(x[xOffset]), static_cast<T>(extraParam0));
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}
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val *= math::sd_pow<T, T, T>(sum, (static_cast<T>(1.0f) - static_cast<T>(extraParam0)) / static_cast<T>(extraParam0));
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for (coords[2] = dstart; coords[2] < dend; coords[2] += dD)
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for (coords[3] = hstart; coords[3] < hend; coords[3] += dH)
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for (coords[4] = wstart; coords[4] < wend; coords[4] += dW) {
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LongType xOffset, zOffset;
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COORDS2INDEX(rank, xStride, coords, xOffset);
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COORDS2INDEX(rank, zStride, coords, zOffset);
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math::atomics::sd_atomicAdd<T>(
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&z[zOffset], val * math::sd_pow<T, T, T>(math::sd_abs<T,T>(x[xOffset]), static_cast<T>(extraParam0) - static_cast<T>(1.0f)) *
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math::sd_sgn<T, T>(x[xOffset]));
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}
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} break;
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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 pooling3dBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem,
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const cudaStream_t* stream, const void* vx, const LongType* xShapeInfo,
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const void* vy, const LongType* yShapeInfo, void* vz,
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const LongType* zShapeInfo, const int kD, const int kH, const int kW,
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const int sD, const int sH, const int sW, const int pD, const int pH, const int pW,
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const int dD, const int dH, const int dW, const int poolingMode,
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const int extraParam0) {
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pooling3dBPCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz,
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zShapeInfo, kD, kH, kW, sD, sH, sW, pD, pH,
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pW, dD, dH, dW, poolingMode, extraParam0);
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DebugHelper::checkErrorCode(const_cast<cudaStream_t*>(stream),"pooling3dBPCudaLauncher failed");
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}
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//////////////////////////////////////////////////////////////////////////
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void ConvolutionUtils::pooling3dBP(graph::Context& block, NDArray& input, NDArray& gradO,
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NDArray& gradI, const LongType kD, const LongType kH, const LongType kW, const LongType sD, const LongType sH,
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const LongType sW, const LongType pD, const LongType pH, const LongType pW, const LongType dD, const LongType dH,
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const LongType dW, const int poolingMode, const int extraParam0) {
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// initial zeroing of gradI
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gradI.nullify();
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PointersManager manager(block.launchContext(), "pooling3dBP");
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dim3 poolingDims = getPoolingDims(gradO.lengthOf(),gradO.rankOf());
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NDArray::prepareSpecialUse({&gradI}, {&input, &gradO});
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BUILD_SINGLE_SELECTOR(
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input.dataType(), pooling3dBPCudaLauncher,
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(poolingDims.y, poolingDims.x, poolingDims.z, block.launchContext()->getCudaStream(), input.specialBuffer(),
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input.specialShapeInfo(), gradO.specialBuffer(), gradO.specialShapeInfo(), gradI.specialBuffer(),
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gradI.specialShapeInfo(), kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, poolingMode, extraParam0),
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SD_FLOAT_TYPES);
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NDArray::registerSpecialUse({&gradI}, {&input, &gradO});
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manager.synchronize();
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}
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} // namespace ops
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} // namespace s |