/* Copyright (c) 2022 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 #include "paddle/phi/core/ddim.h" #include "paddle/phi/core/utils/type_registry.h" #ifdef PADDLE_WITH_DNNL #include "dnnl.hpp" // NOLINT #endif namespace phi { struct StorageProperties { public: virtual ~StorageProperties() = default; TypeInfo type_info() const { return type_info_; } private: template friend class TypeInfoTraits; TypeInfo type_info_{ TypeInfo::kUnknownType}; }; struct NPUStorageProperties : public StorageProperties, public TypeInfoTraits { virtual ~NPUStorageProperties() = default; static const char* name() { return "NPUStorageProperties"; } int64_t storage_format{-1}; DDim storage_dims; }; #ifdef PADDLE_WITH_XPU struct XPUStorageProperties : public StorageProperties, public TypeInfoTraits { XPUStorageProperties() = default; explicit XPUStorageProperties(float value) : xpu_scale_value(value) {} virtual ~XPUStorageProperties() = default; static const char* name() { return "XPUStorageProperties"; } static constexpr float default_xpu_scale_value = -1.0f; float xpu_scale_value{default_xpu_scale_value}; }; #endif // Add OneDNNStorageProperties firstly for unittest coverage #ifdef PADDLE_WITH_DNNL struct OneDNNStorageProperties : public StorageProperties, public TypeInfoTraits { virtual ~OneDNNStorageProperties() = default; static const char* name() { return "OneDNNStorageProperties"; } /** * @brief the detail format of memory block which have layout as ONEDNN * * @note ONEDNN lib support various memory format like nchw, nhwc, nChw8C, * nChw16c, etc. For a ONEDNN memory block, layout will be set as * DataLayout::ONEDNN meanwhile detail memory format will be kept in * this field. */ dnnl::memory::format_tag format = dnnl::memory::format_tag::undef; /// \brief memory descriptor of tensor which have layout set as ONEDNN dnnl::memory::desc mem_desc; }; #endif std::unique_ptr CopyStorageProperties( const std::unique_ptr& sp); } // namespace phi