// Copyright (c) 2024 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/ap/include/adt/adt.h" #include "paddle/ap/include/axpr/type.h" #include "paddle/ap/include/code_module/data_type.h" #include "paddle/ap/include/kernel_dispatch/arg_value.h" #include "paddle/ap/include/kernel_dispatch/device_ctx.h" #include "paddle/ap/include/kernel_dispatch/typed_buffer.h" #include "paddle/ap/include/rt_module/module.h" #include "paddle/phi/core/dense_tensor.h" namespace phi { class DenseTensor; } namespace ap::kernel_dispatch { using RtModuleImpl = std::variant>; struct RtModule : public RtModuleImpl { using RtModuleImpl::RtModuleImpl; ADT_DEFINE_VARIANT_METHODS(RtModuleImpl); }; template struct DispatchRawCtxImpl { DeviceCtx device_ctx; adt::List inputs; adt::List outputs; RtModule rt_module; bool operator==(const DispatchRawCtxImpl& other) const { return &other == this; } Result LaunchCudaKernel( const std::string& func_name, int64_t num_blocks, int64_t num_threads, const adt::List& kernel_args) const; }; template ADT_DEFINE_RC(DispatchRawCtx, DispatchRawCtxImpl); } // namespace ap::kernel_dispatch namespace ap::axpr { template struct TypeImpl> : public std::monostate { using value_type = ap::kernel_dispatch::DispatchRawCtx; const char* Name() const { return "DispatchRawCtx"; } }; } // namespace ap::axpr