wehub-resource-sync
139 lines
4.4 KiB
C++
139 lines
4.4 KiB
C++
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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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,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <vector>
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#include "paddle/common/layout.h"
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#include "paddle/phi/common/backend.h"
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#include "paddle/phi/common/data_type.h"
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#include "paddle/phi/core/ddim.h"
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#include "paddle/utils/any.h"
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#include "paddle/utils/optional.h"
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#include "paddle/utils/test_macros.h"
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namespace phi {
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/*
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* LoD is short for Level of Details.
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*
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* - in a level, each element indicates relative offset of the lower level
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* - the first element should be 0 and that indicates that this sequence start
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* from 0
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* - each sequence's begin and end(no-inclusive) is level[id, id+1]
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*
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* For example:
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* 3-level LoD stores
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*
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* 0 2 3
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* 0 2 4 7
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* 0 2 5 7 10 12 15 20
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*/
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using LegacyLoD = std::vector<std::vector<size_t>>;
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using LoD = LegacyLoD;
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/// \brief The meta data of dense tensor. Take the structure type
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/// and use all default operations.
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///
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struct PADDLE_API DenseTensorMeta {
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DenseTensorMeta();
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DenseTensorMeta(DataType dtype, const DDim& dims);
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DenseTensorMeta(DataType dtype, const DDim& dims, const DDim& stride);
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DenseTensorMeta(DataType dtype,
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const DDim& dims,
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DataLayout layout,
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size_t offset = 0);
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DenseTensorMeta(DataType dtype,
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const DDim& dims,
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DataLayout layout,
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const LegacyLoD& legacy_lod,
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size_t offset = 0);
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DenseTensorMeta(const DenseTensorMeta& other);
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DenseTensorMeta& operator=(const DenseTensorMeta& other);
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DenseTensorMeta& operator=(DenseTensorMeta&& other);
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static DDim calc_strides(const DDim& dims);
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/// \brief Test whether the metadata is valid. Does not throw exceptions.
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/// \return Whether the metadata is valid.
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bool valid() const noexcept;
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bool is_contiguous() const;
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bool is_scalar{false};
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/// \brief Determine whether using gpudnn speed-up library in the new dygraph.
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/// It maybe also support OneDNN library in the near future.
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bool use_gpudnn{true};
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DDim dims;
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DataType dtype{DataType::UNDEFINED};
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DataLayout layout{DataLayout::NCHW};
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LegacyLoD legacy_lod;
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size_t offset{0};
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DDim strides;
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};
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inline bool operator==(const DenseTensorMeta& lhs, const DenseTensorMeta& rhs) {
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return (lhs.is_scalar == rhs.is_scalar) && lhs.use_gpudnn == rhs.use_gpudnn &&
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(lhs.dims == rhs.dims) && (lhs.dtype == rhs.dtype) &&
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(lhs.layout == rhs.layout) && (lhs.legacy_lod == rhs.legacy_lod) &&
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(lhs.offset == rhs.offset) && (lhs.strides == rhs.strides);
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}
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struct PADDLE_API StringTensorMeta {
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StringTensorMeta() = default;
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explicit StringTensorMeta(const DDim& dims);
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/// \brief Test whether the metadata is valid. Does not throw exceptions.
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/// \return Whether the metadata is valid.
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bool valid() const noexcept;
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/// During the entire life cycle of a DenseTensor, the following attributes
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/// marked with `const` are expected to remain unchanged.
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bool is_scalar{false};
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DDim dims;
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size_t offset{0};
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};
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inline bool operator==(const StringTensorMeta& lhs,
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const StringTensorMeta& rhs) {
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return (lhs.is_scalar == rhs.is_scalar) && (lhs.dims == rhs.dims) &&
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(lhs.offset == rhs.offset);
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}
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struct SparseTensorMeta {
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SparseTensorMeta() = default;
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explicit SparseTensorMeta(const DDim& dims);
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explicit SparseTensorMeta(const DDim& dims, const DataLayout& layout);
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/// \brief Test whether the metadata is valid. Does not throw exceptions.
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/// \return Whether the metadata is valid.
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bool valid() const noexcept;
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DDim dims;
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DataType dtype{DataType::UNDEFINED};
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DataLayout layout{DataLayout::UNDEFINED};
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};
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inline bool operator==(const SparseTensorMeta& lhs,
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const SparseTensorMeta& rhs) {
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return (lhs.dims == rhs.dims) && (lhs.layout == rhs.layout);
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}
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} // namespace phi
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namespace paddle {
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using DenseTensorMeta = phi::DenseTensorMeta;
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}
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