177 lines
6.8 KiB
Plaintext
177 lines
6.8 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 raver119@gmail.com
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <exceptions/cuda_exception.h>
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#include <cublas_v2.h>
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#include <ops/specials_cuda.h>
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#include <system/op_boilerplate.h>
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#include <types/float16.h>
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#include <ops/declarable/helpers/batched_gemm.h>
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#include <helpers/PointersManager.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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//////////////////////////////////////////////////////////////////////////////
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// bsxMXK x bSxKxN = bSxMxN
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void bgemm(const std::vector<NDArray*>& vA, const std::vector<NDArray*>& vB, std::vector<NDArray*>& vC, const NDArray* alphas, const NDArray* betas, int transA, int transB, int M, int N, int K, const int lda, const int ldb, const int ldc) {
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const auto bS = vA.size(); // batch size
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std::vector<NDArray*> pA(bS), pB(bS), pC(bS);
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std::vector<NDArray*> toDelete;
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for(int i = 0; i < bS; ++i) {
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if(vA[i]->ews() != 1) {
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pA[i] = new NDArray(vA[i]->dup('f'));
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toDelete.emplace_back(pA[i]);
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}
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else
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pA[i] = vA[i];
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if(vB[i]->ews() != 1) {
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pB[i] = new NDArray(vB[i]->dup('f'));
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toDelete.emplace_back(pB[i]);
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}
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else
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pB[i] = vB[i];
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if(vC[i]->ews() != 1) {
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pC[i] = new NDArray(vC[i]->dup('f'));
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toDelete.emplace_back(pC[i]);
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}
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else
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pC[i] = vC[i];
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if(pC[i]->ordering() != 'f') {
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auto temp = pA[i];
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pA[i] = new NDArray(pB[i]->permute({1,0}));
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pB[i] = new NDArray(temp ->permute({1,0}));
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pC[i] = new NDArray(pC[i]->permute({1,0}));
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toDelete.push_back(pA[i]);
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toDelete.push_back(pB[i]);
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toDelete.push_back(pC[i]);
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M = pA[i]->sizeAt(0);
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K = pA[i]->sizeAt(1);
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N = pB[i]->sizeAt(1);
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}
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NDArray::prepareSpecialUse ({pC[i]}, {pA[i], pB[i]});
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NDArray::registerSpecialUse({pC[i]}, {pA[i], pB[i]});
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}
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NDArray::prepareSpecialUse ({}, {alphas, betas});
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NDArray::registerSpecialUse({}, {alphas, betas});
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std::vector<void*> pAbuffs(bS), pBbuffs(bS), pCbuffs(bS);
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for(int i = 0; i < bS; ++i) {
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pAbuffs[i] = pA[i]->getSpecialBuffer();
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pBbuffs[i] = pB[i]->getSpecialBuffer();
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pCbuffs[i] = pC[i]->getSpecialBuffer();
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}
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sd::LaunchContext* context = vA[0]->getContext();
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PointersManager manager(context, "helpers::bgemm cuda");
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const void** aBuffers = reinterpret_cast<const void**>(manager.replicatePointer(pAbuffs.data(), bS * sizeof(void*)));
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const void** bBuffers = reinterpret_cast<const void**>(manager.replicatePointer(pBbuffs.data(), bS * sizeof(void*)));
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void** cBuffers = reinterpret_cast<void**>(manager.replicatePointer(pCbuffs.data(), bS * sizeof(void*)));
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// const auto aOrder = pA->ordering();
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// const auto bOrder = pB->ordering();
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// const bool transA = aOrder != 'f';
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// const bool transB = bOrder != 'f';
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const cublasOperation_t transAblas = transA == 112 ? CUBLAS_OP_T : CUBLAS_OP_N;
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const cublasOperation_t transBblas = transB == 112 ? CUBLAS_OP_T : CUBLAS_OP_N;
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// const int lda = aOrder == 'f' ? M : K;
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// const int ldb = bOrder == 'f' ? K : N;
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// const int ldc = M; // cOrder == 'f' ? M : N;
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const auto aType = pA[0]->dataType();
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const auto bType = pB[0]->dataType();
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const auto cType = pC[0]->dataType();
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auto handle = reinterpret_cast<cublasHandle_t*>(context->getCublasHandle());
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auto stream = context->getCudaStream();
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auto status = cublasSetStream_v2(*handle, *stream);
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if (status != CUBLAS_STATUS_SUCCESS)
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throw cuda_exception::build("MmulHelper::mmulMxM cuda failed !", status);
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const bool AB(aType == bType), AC(aType == cType), ABC(AB && AC);
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// choose appropriate cuda gemm api depending on data types
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if(ABC && aType == DataType::DOUBLE) {
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double alpha = alphas->e<double>(0);
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double beta = betas->e<double>(0);
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status = cublasDgemmBatched(*handle, transAblas, transBblas, M, N, K, &alpha, (const double**)aBuffers, lda, (const double**)bBuffers, ldb, &beta, (double**)cBuffers, ldc, bS);
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}
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else if(ABC && aType == DataType::FLOAT32) {
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float alpha = alphas->e<float>(0);
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float beta = betas->e<float>(0);
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status = cublasSgemmBatched(*handle, transAblas, transBblas, M, N, K, &alpha, (const float**)aBuffers, lda, (const float**)bBuffers, ldb, &beta, (float**)cBuffers, ldc, bS);
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}
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else if(ABC && aType == DataType::HALF) {
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__half alpha = alphas->e<float>(0);
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__half beta = betas->e<float>(0);
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status = cublasHgemmBatched(*handle, transAblas, transBblas, M, N, K, &alpha, (const __half**)aBuffers, lda, (const __half**)bBuffers, ldb, &beta, (__half**)cBuffers, ldc, bS);
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}
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else if(AB && aType == DataType::INT8 && cType == DataType::FLOAT32) {
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float alpha = alphas->e<float>(0);
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float beta = betas->e<float>(0);
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status = cublasGemmBatchedEx(*handle, transAblas, transBblas, M, N, K, &alpha, aBuffers, CUDA_R_8I, lda, bBuffers, CUDA_R_8I, ldb, &beta, cBuffers, CUDA_R_32F, ldc, bS, CUDA_R_32F, CUBLAS_GEMM_DEFAULT);
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}
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else if(AB && aType == DataType::HALF && cType == DataType::FLOAT32) {
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float alpha = alphas->e<float>(0);
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float beta = betas->e<float>(0);
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status = cublasGemmBatchedEx(*handle, transAblas, transBblas, M, N, K, &alpha, aBuffers, CUDA_R_16F, lda, bBuffers, CUDA_R_16F, ldb, &beta, cBuffers, CUDA_R_32F, ldc, bS, CUDA_R_32F, CUBLAS_GEMM_DEFAULT);
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}
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else
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throw std::runtime_error("batched gemm cuda: this mode is not implemented yet !");
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if (status != CUBLAS_STATUS_SUCCESS)
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throw cuda_exception::build("MmulHelper::mmulMxM cuda failed !", status);
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auto cudaResult = cudaStreamSynchronize(*stream);
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if (cudaResult != 0)
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throw cuda_exception::build("MmulHelper::mmulMxM cuda failed !", cudaResult);
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for(int i = 0; i < bS; ++i)
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if(vC[i]->ews() != 1)
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vC[i]->assign(pC[i]);
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for(int i = toDelete.size() - 1; i >= 0; --i)
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delete toDelete[i];
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}
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}
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}
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}
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