# Tensor: An Unified Data Type in PaddlePaddle ## Pain Point In this week, we discussed several potential weaknesses of PaddlePaddle caused by rapid iteration and development to promote new business products on the line in recent four years. For instance, current Matrix/Vector implementation in PaddlePaddle are long and tedious to read, which interfered seriously with the contribution of both fresh and professional engineers. More seriously for this issue, it will also become too challenging to maintain over time. ## Learn from Majel Consequently, we decide to refactor PaddlePaddle step-by-step. First, refactor and replace Matrix/Vector to Tensor, a modern terminology in the deep learning system. Fortunately, we can learn from Majel how to define a Tensor. To simplify heterogeneous resource allocation in any dimensions (1-9) and types (double, float, float16), Majel consists of several primitives such as `Dim`, `Place` and `Array`, all of them are standard C++ class templates. 1. `Place`: memory location [i.e. CPU/GPU]. 2. `Allocation`: heterogeneous resource allocator [i.e. 20MB in GPU]. 3. `Dim`: size of each dimension. [i.e. Dim<4>({10, 2, 5, 1})] 4. `Array`: dynamic array consists of `Place`, `Dim`, and a pointer to memory. If you dig deeper into Majel source code, you will find Majel heavily use `boost.variant`. The variant class template is a safe, generic, stack-based discriminated union container, **offering a simple solution for manipulating an object from a heterogeneous set of types in a uniform manner**. Whereas standard containers such as std::vector may be thought of as "multi-value, single type," variant is "multi-type, single value." As a simple example, consider the following: ```c++ #include "boost/variant.hpp" #include class my_visitor : public boost::static_visitor { public: int operator()(int i) const { return i; } int operator()(const std::string & str) const { return str.length(); } }; int main() { boost::variant< int, std::string > u("hello world"); std::cout << u; // output: hello world int result = boost::apply_visitor( my_visitor(), u ); std::cout << result; // output: 11 (i.e., length of "hello world") } ``` In Majel, `DDimVar` is derived from `Dim`, `DArrayVar` is from `Array`. ```c++ template struct Dim { ... int head; Dim tail; } ``` ```c++ template class Array : public Buffer { ... private: Dim size_; Dim stride_; T* ptr_; }; ``` ```c++ typedef boost::variant Place; typedef boost::variant, Dim<2>, Dim<3>, Dim<4>, Dim<5>, Dim<6>, Dim<7>, Dim<8>, Dim<9>> DDimVar; typedef boost::variant< Array, Array, Array, Array, Array, Array, Array, Array, Array, Array, Array, Array > DArrayVar; ``` Because `variant` may be thought of as "multi-type, single value", we can utilize it to implement unified interfaces for PaddlePaddle. ## Implement Tensor in Paddle We want to create a Tensor class to replace Vector and Matrix, and to support high-dimensional data. The operations on Tensor are implemented in both CPU and GPU. We also want to make sure that the Tensor interface is friendly to its callers. Tensor is only responsible for describing computing. It will not take charge of memory allocation policy, handles of some CUDA library context(e.g. cublasHandle, cudnnHandle), and dispatching CUDA kernels. Paddle has realize the initialization and resources management of hardware. Before writing code, please make sure you already look through Majel Source Code and grabbed the design philosophy of `DArray` in Majel. ### Memory Management `Allocation` manages a block of memory in device(CPU/GPU). We use `Place` to decribe memory location. The details of memory allocation and deallocation are implememted in `Allocator` and `DeAllocator`. Related low-level API such as `hl_malloc_device()` and `hl_malloc_host()` are provided by Paddle. ### Dim and Array #### Dim `Dim` decribes the dimension information of an array. `DDimVar` is an alias of a specializd class of boost.variant class template. `DDim` is introduced to represent a dynamically sized dimension. For example: ``` Dim<2> d1 = make_dim(3, 3); DDim d2 = make_ddim({1, 2, 3}); ``` You must appoint a concrete sized dimension to Dim, whereas DDim can represent a dynamically sized dimension. #### Array `Array` represents for a tensor with specific type and size. `DArrarVar` is an alias of a specialized class of boost.variant class template. `DArray` is introduced to represent a dynamically typed array. For example: ``` Array a1(Dim<2>(2, 2)); DArray a2 = make_darray(make_ddim({3, 4}), 0.0, CpuPlace()); ``` You must appoint the type and dimension of a Array, whereas DArray can represent a dynanmically typed array. Please reference the section of `Learn from Majel` for more details. ### ArrayView `ViewIterator` is a class template which implements basic iterator operation, including increment(++), decrement(--), dereference(*), equalit comparisons(==) and so on. `ArrayView` is an encapsulation of `Array`， which introduces extra iterator menthods, such as `begin()` and `end()`. The `begin()` method returns an iterator pointing to the first element in the ArrayView. And the `end()` method returns an iterator pointing to the pass-the-end element in the ArrayView. `ArrayView` make the visting and manipulating an array more efficently, flexibly and safely. A global function `make_view` is provided to transform an array to corresponding arrayview. ``` template ArrayView make_view(const Array& in) { return in; } ``` A global function `make_iterator` is provided to make iterator of an array. ``` template ViewIterator> make_iterator(const Array& in, Dim idx) { return make_iterator(make_view(in), idx); } ``` ### Basic Operations The operations that manipulate DArray are defined as global functions, such as `ones`, `zeros`, `reshape`, `gemm` and so on. An array will be trasformed into an arrayview and then passed to the operation launching on a specific deviec(CPU/GPU).