// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #pragma once #include "paddle/fluid/lite/core/kernel.h" #include "paddle/fluid/lite/core/types.h" #include "paddle/fluid/lite/cuda/blas.h" #include "paddle/fluid/lite/operators/op_params.h" namespace paddle { namespace lite { namespace kernels { namespace cuda { template void mul_compute(const lite::cuda::Blas& blas, const T* x, int x_h, int x_w, const T* y, int y_h, int y_w, T* out) { blas.sgemm(CUBLAS_OP_N, CUBLAS_OP_N, x_h, y_w, x_w, nullptr, x, x_w, y, y_w, nullptr, out, x_h); } class MulCompute : public KernelLite { public: using param_t = operators::MulParam; void Run() override { CHECK(context_) << "running context should be set first"; auto& context = context_->AsCudaContext(); CHECK(context.blas_fp32) << "blas should init first"; /* auto& blas = *context.blas_fp32; CHECK(param.x->target() == TARGET(kCUDA)); auto* x = param.x->data(); int x_h = param.x->dims()[0]; int x_w = param.x->dims()[1]; auto* y = param.y->data(); int y_h = param.y->dims()[0]; int y_w = param.y->dims()[1]; */ const auto& param = Param(); param.output->mutable_data(TARGET(kCUDA)); LOG(INFO) << "mul output memory size " << param.output->memory_size(); // mul_compute(blas, x, x_h, x_w, y, y_h, y_w, out); } virtual ~MulCompute() = default; }; } // namespace cuda } // namespace kernels } // namespace lite } // namespace paddle