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21 September 2021, 22:50:56 UTC
Revision f8ee093244ad3a1666d282ca5bb1c26c8768a175 authored by Vincent Dutordoir on 26 February 2020, 09:52:47 UTC, committed by GitHub on 26 February 2020, 09:52:47 UTC 
Remove the use of generator in dispatcher (#1264)
 
GPflow makes use of a multipledispatch Dispatcher that internally uses a generator.  However, according to TensorFlow [Capabilities and Limitations](https://github.com/tensorflow/tensorflow/blob/560e2575ecad30bedff5b192f33f6d06b19ccaeb/tensorflow/python/autograph/LIMITATIONS.md) generators are not supported by AutoGraph and probably will never be. Thus, compiling code that passed though the dispatcher led to the following warnings:
```bash
WARNING:tensorflow:Entity <bound method Dispatcher.dispatch_iter of <dispatched sample_conditional>> appears to be a generator function. It will not be converted by AutoGraph.
WARNING: Entity <bound method Dispatcher.dispatch_iter of <dispatched sample_conditional>> appears to be a generator function. It will not be converted by AutoGraph.
WARNING:tensorflow:Entity <bound method Dispatcher.dispatch_iter of <dispatched conditional>> appears to be a generator function. It will not be converted by AutoGraph.
WARNING: Entity <bound method Dispatcher.dispatch_iter of <dispatched conditional>> appears to be a generator function. It will not be converted by AutoGraph.
```

This PR still uses the same dispatcher, but overwrites the problematic method that uses python generators and replaces it by a simple list.


### NOTE
As of this PR we do not need to write `autograph=False` in `tf.function` anymore, and all the code inside dispatching gets compiled the same way as everything else :) !
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Tip revision: f8ee093244ad3a1666d282ca5bb1c26c8768a175 authored by Vincent Dutordoir on 26 February 2020, 09:52:47 UTC
Remove the use of generator in dispatcher (#1264)
Tip revision: f8ee093
GLOSSARY.md 
## Glossary

GPflow does not always follow standard Python naming conventions,
and instead tries to apply the notation in the relevant GP papers.\
The following is the convention we aim to use in the code.

---

<dl>
  <dt>GPR</dt>
  <dd>Gaussian process regression</dd>

  <dt>SVGP</dt>
  <dd>stochastic variational inference for Gaussian process models</dd>

  <dt>Shape constructions [..., A, B]</dt>
  <dd>the way of describing tensor shapes in docstrings and comments. Example: <i>[..., N, D, D]</i>, this is a tensor with an arbitrary number of leading dimensions indicated using the ellipsis sign, and the last two dimensions are equal</dd>

  <dt>X</dt>
  <dd>(and variations like Xnew) refers to input points; always of rank 2, e.g. shape <i>[N, D]</i>, even when <i>D=1</i></dd>

  <dt>Y</dt>
  <dd>(and variations like Ynew) refers to observed output values, potentially with multiple output dimensions; always of rank 2, e.g. shape <i>[N, P]</i>, even when <i>P=1</i></dd>

  <dt>Z</dt>
  <dd>refers to inducing points</dd>

  <dt>M</dt>
  <dd>stands for the number of inducing features (e.g. length of Z)</dd>

  <dt>N</dt>
  <dd>stands for the number of data or minibatch size in docstrings and shape constructions</dd>

  <dt>P</dt>
  <dd>stands for the number of output dimensions in docstrings and shape constructions</dd>

  <dt>D</dt>
  <dd>stands for the number of input dimensions in docstrings and shape constructions</dd>
</dl>
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