Summary:
Create a script for simple generation from BLT
Test Plan:
```
python -m bytelatent.generate_blt config=../internal-blt/configs/eval_blt.yaml
```
This commit does/fixes the following:
1. Adds unit tests for byte and patch packing to ensure it works correctly
2. Fixes a bug where for batches that end up with <max_length number of bytes (e.g., short patches), the mask was including elements that had value pad_id. This fixes the mask by setting it to be !=pad_id, if its not specified.
3. Correctly handles the last batch, where previously it would crash. This didn't affect training since we had enough data and/or looped iterators, but for evaluation perplexity, it comes up if we validation on an entire file.
4. Correctly forward the mask if it exists for byte packing
Test Plan:
```
pytest bytelatent/
```
Testing these changes more thoroughly in a stacked PR that fixes evals
- Create a common class to use in all inheritance for states
- Add a limit iterator that we can use in evals
- Modify ArrowFileIterator behavior to not do arrow path inference if file_format='json'
- Make EvalArgs valid
- Move testing iterators to a common directory to allow usage in multiple test files
- Make it so that SequenceIterator can take a None rng_state, to disable all rng ops (for eval mainly)
Test Plan:
- `pytest bytelatent`
- `python -m bytelatent.train config=../internal-blt/configs/entropy_model.yaml logging.wandb=null eval=null`
Summary:
Currently, arrow iterator can only read arrow files. However, the pyarrow library can read
other formats, including jsonlines. This allows the same ArrowIterator to read from jsonlines,
so we can read from the original source data, and simply omit the entropy column when doing so
Test Plan:
Run train script until dataloader starts
* allow loading of the entropy model directly
* remove unused argument
* remove spammy warning
* allow patch_batch_size to be adjusted in the forward() method
* revert to original patcher style, fix warning
* allow grads when calculating entropies
* fix grad flow
* return preds from calculate_entropies()
* remove legacy arg
* fix an error with monotonicity and small sequence lengths
* ensure patcher is serializable
* revert patcher to original
* remove unused import
Summary:
- Refactor local model configs to be separate and clearer
- Add attention arguments and correct which attention is used in local models
- Preparation for being able to have an entropy train script
- Fix failing unit tests
Test Plan:
Summary:
For compatibility with either local/nfs or S3 datasets, swap to fsspec.
Add a tool to compare local and remote filesystems
Test Plan:
- Ran regular train script
- Ran with config with data in S3
