192 lines
7.4 KiB
Plaintext
192 lines
7.4 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 AbdelRauf
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//
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#include <system/op_boilerplate.h>
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#include <ops/declarable/helpers/imagesHelpers.h>
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#include <helpers/ConstantTadHelper.h>
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#include <ops/declarable/helpers/adjust_hue.h>
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#include <helpers/PointersManager.h>
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#include <ops/declarable/helpers/transforms.h>
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#include <helpers/LoopsCoordsHelper.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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template<typename X>
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_CUDA_HD uint8_t pack(const X* buff, const X& threshold){
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uint8_t res;
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res = (buff[0] > threshold) << 7;
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res = res | ((buff[1] > threshold) << 6);
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res = res | ((buff[2] > threshold) << 5);
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res = res | ((buff[3] > threshold) << 4);
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res = res | ((buff[4] > threshold) << 3);
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res = res | ((buff[5] > threshold) << 2);
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res = res | ((buff[6] > threshold) << 1);
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res = res | (buff[7] > threshold);
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return res;
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}
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template<>
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_CUDA_HD uint8_t pack<bool>(const bool* buff, const bool &threshold){
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//ignore threshold
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uint8_t res;
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res = buff[0] << 7;
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res = res | (buff[1] << 6);
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res = res | (buff[2] << 5);
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res = res | (buff[3] << 4);
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res = res | (buff[4] << 3);
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res = res | (buff[5] << 2);
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res = res | (buff[6] << 1);
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res = res | buff[7] ;
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return res;
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}
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template<typename X>
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_CUDA_HD uint8_t pack(const X* buff, int stride, const X& threshold){
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uint8_t res;
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res = (buff[0] > threshold) << 7;
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res = res | ((buff[1*stride] > threshold) << 6);
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res = res | ((buff[2*stride] > threshold) << 5);
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res = res | ((buff[3*stride] > threshold) << 4);
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res = res | ((buff[4*stride] > threshold) << 3);
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res = res | ((buff[5*stride] > threshold) << 2);
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res = res | ((buff[6*stride] > threshold) << 1);
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res = res | (buff[7*stride] > threshold);
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return res;
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}
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template<>
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_CUDA_HD uint8_t pack<bool>(const bool* buff, int stride, const bool &threshold){
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//ignore threshold
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uint8_t res;
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res = buff[0] << 7;
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res = res | (buff[1*stride] << 6);
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res = res | (buff[2*stride] << 5);
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res = res | (buff[3*stride] << 4);
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res = res | (buff[4*stride] << 3);
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res = res | (buff[5*stride] << 2);
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res = res | (buff[6*stride] << 1);
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res = res | buff[7*stride] ;
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return res;
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}
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///////////////////////////////////////////////////////////////////
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template <typename T>
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static void _CUDA_G cmpBitpack(const void* vx, void* vz, int rank, int len, const Nd4jLong *xStridesExtended, const Nd4jLong *outPutShapeInfo, T threshold) {
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const T* x = reinterpret_cast<const T*>(vx);
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uint8_t* z = reinterpret_cast<uint8_t*>(vz);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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auto shapes = shape::shapeOf(outPutShapeInfo);
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auto zStrides = shape::stride(outPutShapeInfo);
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Nd4jLong coords[MAX_RANK] = {};
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Nd4jLong* ptr_coords = (Nd4jLong*)&coords;
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// its extended as {rank+1} so xStridesExtended[rank] is valid
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auto inLastStride = xStridesExtended[rank];
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for(auto k=tid; k < len; k+=gridDim.x * blockDim.x){
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sd::index2coords_C(k, rank, shapes, ptr_coords);
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auto offset = sd::offset_from_coords(xStridesExtended, zStrides, ptr_coords, rank);
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auto buffPart = &(x[offset.first]);
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auto outBuffPart = &(z[offset.second]);
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*outBuffPart = pack<T>(buffPart, inLastStride, threshold);
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}
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}
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template <typename T>
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static void _CUDA_G cmpBitpackEws(const void* vx, void* vz, int len, const Nd4jLong xStride, const Nd4jLong yStride, T threshold) {
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const T* x = reinterpret_cast<const T*>(vx);
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uint8_t* z = reinterpret_cast<uint8_t*>(vz);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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if(xStride==1){
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for(auto k=tid; k < len; k+=gridDim.x * blockDim.x){
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auto buffPart = &(x[k*8]);
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auto outBuffPart = &(z[k*yStride]);
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*outBuffPart = pack<T>(buffPart, threshold);
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}
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}else{
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for(auto k=tid; k < len; k+=gridDim.x * blockDim.x){
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auto buffPart = &(x[k*8*xStride]);
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auto outBuffPart = &(z[k*yStride]);
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*outBuffPart = pack<T>(buffPart, xStride, threshold);
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}
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}
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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static _CUDA_H void cmpBitpackCudaLauncher(sd::graph::Context& block, const NDArray& input, const NDArray& thresholdScalar, NDArray& output) {
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T threshold = thresholdScalar.e<T>(0);
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auto inStrides = input.stridesOf();
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auto rank = output.rankOf();
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//threadblock size
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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//grid size
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const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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auto stream = block.launchContext()->getCudaStream();
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//nd4j_printf("n %i g %i th %i \n", output.lengthOf(), blocksPerGrid, threadsPerBlock);
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PointersManager manager(block.launchContext(), "compare_and_bitpack");
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NDArray::prepareSpecialUse({&output}, {&input});
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if(input.ews()>0 && output.ews()>0 && input.ordering()=='c' && output.ordering()=='c'){
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cmpBitpackEws<T><<<blocksPerGrid, threadsPerBlock >>>(input.specialBuffer(), output.specialBuffer(), output.lengthOf(), inStrides[rank-1], output.stridesOf()[rank-1] , threshold);
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}else{
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//if output shape is {n1, n2, n3} then input shape is { n1. n2, n3 * 8}
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//therefore we can split input shape {n1, n2, n3 , 8} and correct its stride
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//as we do not need last shape info. lets just extend and correct its stride
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Nd4jLong extendedStrides[MAX_RANK];
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for(int i=0;i<rank; i++){
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extendedStrides[i] = inStrides[i];
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}
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//lets correct new stride
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extendedStrides[rank-1] = 8*inStrides[rank-1];
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extendedStrides[rank] = inStrides[rank-1];
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auto strideSize = (rank+1)*sizeof(Nd4jLong);
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Nd4jLong* extendedStridesDevPtr = reinterpret_cast<Nd4jLong*>(manager.replicatePointer(extendedStrides, strideSize));
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cmpBitpack<T><<<blocksPerGrid, threadsPerBlock >>>(input.specialBuffer(), output.specialBuffer(), rank, output.lengthOf(), extendedStridesDevPtr, output.specialShapeInfo(), threshold);
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}
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NDArray::registerSpecialUse({&output}, {&input});
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manager.synchronize();
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}
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void compareAndBitpack(sd::graph::Context& block, const NDArray& input, const NDArray& threshold, NDArray& output) {
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BUILD_SINGLE_SELECTOR(input.dataType(), cmpBitpackCudaLauncher, (block, input, threshold, output), LIBND4J_TYPES);
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}
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}
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}
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}
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