Metrics - refactoring, adding support for download and distributed metrics
Author: thomwolf | Pull request: yes | State: closed

Refactoring metrics to have a similar loading API than the datasets and improving the import system.

# Import system
The import system has ben upgraded. There are now three types of imports allowed:
1. `library` imports (identified as "absolute imports")
```python
import seqeval
```
=> we'll test all the imports before running the scripts and if one cannot be imported we'll display an error message like this one:
`ImportError: To be able to use this metric/dataset, you need to install the following dependencies ['seqeval'] using 'pip install seqeval' for instance'`

2. `internal` imports (identified as "relative imports")
```python
import .c4_utils
```
=> we'll assume this point to a file in the same directory/S3-directory as the main script and download this file.

2. `external` imports (identified as "relative imports" with a comment starting with `# From:`)
```python
from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
```
=> we'll assume this point to the URL of a python script (if it's a link to a github file, we'll take the raw file automatically).
=> the script is downloaded and renamed to the import name (here above renamed from `bleu.py` to `nmt_bleu.py`). Renaming the file can be necessary if the distant file has the same name as the dataset/metric processing script. If you forgot to rename the distant script and it has the same name as the dataset/metric, you'll have an explicit error message asking to rename the import anyway.

# Hosting metrics

Metrics are hosted on a S3 bucket like the dataset processing scripts.

# Metrics scripts

Metrics scripts have a lot in common with datasets processing scripts. They also have a `metric.info` including citations, descriptions and links to relevant pages.

Metrics have more documentation to supply to ensure they are used well.

Four examples are already included for reference in [./metrics](./metrics): BLEU, ROUGE, SacreBLEU and SeqEVAL.

# Automatic support for distributed/multi-processing metric computation

We've also added support for automatic distributed/multi-processing metric computation (e.g. when using DistributedDataParallel). We leverage our own dataset format for smart caching in this case. 

Here is a quick gist of a standard use of metrics (the simplest usage):
```python
import nlp
bleu_metric = nlp.load_metric('bleu')

# If you only have a single iteration, you can easily compute the score like this
predictions = model(inputs)
score = bleu_metric.compute(predictions, references)

# If you have a loop, you can "add" your predictions and references at each iteration instead of having to save them yourself (the metric object store them efficiently for you)
for batch in dataloader:
    model_input, targets = batch
    predictions = model(model_inputs)
    bleu.add(predictions, targets)
score = bleu_metric.compute()  # Compute the score from all the stored predictions/references
```

Here is a quick gist of a use in a distributed torch setup (should work for any python multi-process setup actually). It's pretty much identical to the second example above:
```python
import nlp
# You need to give the total number of parallel python processes (num_process) and the id of each process (process_id)
bleu = nlp.load_metric('bleu', process_id=torch.distributed.get_rank(),b num_process=torch.distributed.get_world_size())

for batch in dataloader:
    model_input, targets = batch
    predictions = model(model_inputs)
    bleu.add(predictions, targets)
score = bleu_metric.compute()  # Compute the score on the first node by default (can be set to compute on each node as well)
```