| Status | Accepted |
|---|---|
| Author | [email protected] |
| Sponsor | [email protected] |
| Updated | 2018-12-21 |
Expose a tf.raw_ops namespace containing all raw operations in TensorFlow.
Some parts of the TensorFlow Python API, such as variables, optimizers, and control flow, are currently not implementable by third parties. Moreover, with the tf.contrib deprecation, there is now no valid Python endpoint from which to use many TF operations.
We'll add a tf.raw_ops namespace to TensorFlow with Python bindings to all
non-deprecated TensorFlow ops which is usable in a backwards-compatible
way. This is designed to be consumed by downstream library writers and not end
users.
The namespace will be automatically populated with generated bindings for every operation in TensorFlow. These generated bindings will be similar to the ones currently used for the python API, with the following differences:
- All arguments are keyword arguments.
- This allows us to add new attributes to existing ops without breaking users
who call by positional arguments (given that there is an always-last
nameargument added by the tf op binding generator). - This also prevents users from assuming that calling conventions from the existing python bindings apply to the raw versions (we often do argument reordering in our python bindings, for example).
- Any op marked as deprecated will be in the namespace but will raise an exception when used.
- This includes ops which take or produce ref tensors.
- This allows us to deprecate ops eventually and to be less strict with the API here than with the main API.
- This is mostly OK since only library writers are supposed to use these symbols, and the deprecation messages should include upgrading instructions.
- Naming: tf.raw_ops is the name
- Backward compatibility policy: we'll document on tf.org
- Flat namespace vs nested? flat
- Will not include protocol buffers