TensorFlow in other languages

Background

This document is intended as a guide for those interested in the creation or development of TensorFlow functionality in other programming languages. It describes the features of TensorFlow and recommended steps for making the same available in other programming languages.

Python was the first client language supported by TensorFlow and currently supports the most features. More and more of that functionality is being moved into the core of TensorFlow (implemented in C++) and exposed via a C API. Client languages should use the language's foreign function interface (FFI) to call into this C API to provide TensorFlow functionality.

Overview

Providing TensorFlow functionality in a programming language can be broken down into broad categories:

Run a predefined graph: Given a GraphDef (or MetaGraphDef) protocol message, be able to create a session, run queries, and get tensor results. This is sufficient for a mobile app or server that wants to run inference on a pre-trained model.
Graph construction: At least one function per defined TensorFlow op that adds an operation to the graph. Ideally these functions would be automatically generated so they stay in sync as the op definitions are modified.
Gradients (AKA automatic differentiation): Given a graph and a list of input and output operations, add operations to the graph that compute the partial derivatives (gradients) of the inputs with respect to the outputs. Allows for customization of the gradient function for a particular operation in the graph.
Functions: Define a subgraph that may be called in multiple places in the main GraphDef. Defines a FunctionDef in the FunctionDefLibrary included in a GraphDef.
Control Flow: Construct "If" and "While" with user-specified subgraphs. Ideally these work with gradients (see above).
Neural Network library: A number of components that together support the creation of neural network models and training them (possibly in a distributed setting). While it would be convenient to have this available in other languages, there are currently no plans to support this in languages other than Python. These libraries are typically wrappers over the features described above.

At a minimum, a language binding should support running a predefined graph, but most should also support graph construction. The TensorFlow Python API provides all these features.

Current Status

New language support should be built on top of the C API. However, as you can see in the table below, not all functionality is available in C yet. Providing more functionality in the C API is an ongoing project.

Feature	Python	C
Run a predefined Graph	`tf.import_graph_def`, `tf.Session`	`TF_GraphImportGraphDef`, `TF_NewSession`
Graph construction with generated op functions	Yes	Yes (The C API supports client languages that do this)
Gradients	`tf.gradients`
Functions	`tf.python.framework.function.Defun`
Control Flow	`tf.cond`, `tf.while_loop`
Neural Network library	`tf.train`, `tf.nn`, `tf.contrib.layers`, `tf.contrib.slim`

Recommended Approach

Run a predefined graph

A language binding is expected to define the following classes:

Graph: A graph representing a TensorFlow computation. Consists of operations (represented in the client language by Operations) and corresponds to a TF_Graph in the C API. Mainly used as an argument when creating new Operation objects and when starting a Session. Also supports iterating through the operations in the graph (TF_GraphNextOperation), looking up operations by name (TF_GraphOperationByName), and converting to and from a GraphDef protocol message (TF_GraphToGraphDef and TF_GraphImportGraphDef in the C API).
Operation: Represents a computation node in the graph. Corresponds to a TF_Operation in the C API.
Output: Represents one of the outputs of an operation in the graph. Has a DataType (and eventually a shape). May be passed as an input argument to a function for adding operations to a graph, or to a Session's Run() method to fetch that output as a tensor. Corresponds to a TF_Output in the C API.
Session: Represents a client to a particular instance of the TensorFlow runtime. Its main job is to be constructed with a Graph and some options and then field calls to Run() the graph. Corresponds to a TF_Session in the C API.
Tensor: Represents an N-dimensional (rectangular) array with elements all the same DataType. Gets data into and out of a Session's Run() call. Corresponds to a TF_Tensor in the C API.
DataType: An enumerant with all the possible tensor types supported by TensorFlow. Corresponds to TF_DataType in the C API and often referred to as dtype in the Python API.

Graph construction

TensorFlow has many ops, and the list is not static, so we recommend generating the functions for adding ops to a graph instead of writing them by individually by hand (though writing a few by hand is a good way to figure out what the generator should generate). The information needed to generate a function is contained in an OpDef protocol message.

There are a few ways to get a list of the OpDefs for the registered ops:

TF_GetAllOpList in the C API retrieves all registered OpDef protocol messages. This can be used to write the generator in the client language. This requires that the client language have protocol buffer support in order to interpret the OpDef messages.
The C++ function OpRegistry::Global()->GetRegisteredOps() returns the same list of all registered OpDefs (defined in tensorflow/core/framework/op.h). This can be used to write the generator in C++ (particularly useful for languages that do not have protocol buffer support).
The ASCII-serialized version of that list is periodically checked in to tensorflow/core/ops/ops.pbtxt by an automated process.

The OpDef specifies the following:

Name of the op in CamelCase. For generated functions follow the conventions of the language. For example, if the language uses snake_case, use that instead of CamelCase for the op's function name.
A list of inputs and outputs. The types for these may be polymorphic by referencing attributes, as described in the inputs and outputs section of Adding an op.
A list of attributes, along with their default values (if any). Note that some of these will be inferred (if they are determined by an input), some will be optional (if they have a default), and some will be required (no default).
Documentation for the op in general and the inputs, outputs, and non-inferred attributes.
Some other fields that are used by the runtime and can be ignored by the code generators.

An OpDef can be converted into the text of a function that adds that op to the graph using the TF_OperationDescription C API (wrapped in the language's FFI):

Start with TF_NewOperation() to create the TF_OperationDescription*.
Call TF_AddInput() or TF_AddInputList() once per input (depending on whether the input has a list type).
Call TF_SetAttr*() functions to set non-inferred attributes. May skip attributes with defaults if you don't want to override the default value.
Set optional fields if necessary:
- TF_SetDevice(): force the operation onto a specific device.
- TF_AddControlInput(): add requirements that another operation finish before this operation starts running
- TF_SetAttrString("_kernel") to set the kernel label (rarely used)
- TF_ColocateWith() to colocate one op with another
Call TF_FinishOperation() when done. This adds the operation to the graph, after which it can't be modified.

The existing examples run the code generator as part of the build process (using a Bazel genrule). Alternatively, the code generator can be run by an automated cron process, possibly checking in the result. This creates a risk of divergence between the generated code and the OpDefs checked into the repository, but is useful for languages where code is expected to be generated ahead of time like go get for Go and cargo ops for Rust. At the other end of the spectrum, for some languages the code could be generated dynamically from tensorflow/core/ops/ops.pbtxt.

Handling Constants

Calling code will be much more concise if users can provide constants to input arguments. The generated code should convert those constants to operations that are added to the graph and used as input to the op being instantiated.

Optional parameters

If the language allows for optional parameters to a function (like keyword arguments with defaults in Python), use them for optional attributes, operation names, devices, control inputs etc. In some languages, these optional parameters can be set using dynamic scopes (like "with" blocks in Python). Without these features, the library may resort to the "builder pattern", as is done in the C++ version of the TensorFlow API.

Name scopes

It is a good idea to have support for naming graph operations using some sort of scoping hierarchy, especially considering the fact that TensorBoard relies on it to display large graphs in a reasonable way. The existing Python and C++ APIs take different approaches: In Python, the "directory" part of the name (everything up to the last "/") comes from with blocks. In effect, there is a thread-local stack with the scopes defining the name hierarchy. The last component of the name is either supplied explicitly by the user (using the optional name keyword argument) or defaults to the name of the type of the op being added. In C++ the "directory" part of the name is stored in an explicit Scope object. The NewSubScope() method appends to that part of the name and returns a new Scope. The last component of the name is set using the WithOpName() method, and like Python defaults to the name of the type of op being added. Scope objects are explicitly passed around to specify the name of the context.

Wrappers

It may make sense to keep the generated functions private for some ops so that wrapper functions that do a little bit of additional work can be used instead. This also gives an escape hatch for supporting features outside the scope of generated code.

One use of a wrapper is for supporting SparseTensor input and output. A SparseTensor is a tuple of 3 dense tensors: indices, values, and shape. values is a vector size [n], shape is a vector size [rank], and indices is a matrix size [n, rank]. There are some sparse ops that use this triple to represent a single sparse tensor.

Another reason to use wrappers is for ops that hold state. There are a few such ops (e.g. a variable) that have several companion ops for operating on that state. The Python API has classes for these ops where the constructor creates the op, and methods on that class add operations to the graph that operate on the state.

Other Considerations

It is good to have a list of keywords used to rename op functions and arguments that collide with language keywords (or other symbols that will cause trouble, like the names of library functions or variables referenced in the generated code).
The function for adding a Const operation to a graph typically is a wrapper since the generated function will typically have redundant DataType inputs.

Gradients, functions and control flow

At this time, support for gradients, functions and control flow operations ("if" and "while") is not available in languages other than Python. This will be updated when the C API provides necessary support.

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