- PPL/validation: Works now and uses multi-gpu. For some reason 1 GPU differs from multi-GPU, can debug in a followup PR
- Generation evals likely work, but are very slow, so disabled for now
Test Plan:
```
torchrun --nproc-per-node 8 -m bytelatent.eval config=../internal-blt/configs/eval.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
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
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
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
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
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`
- 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:
Make it possible to specify multiple config files.
Parsing CLI is not a special case anymore, just uses the same config inheritance method.
Test Plan:
Test that this iterpolates in the right order via unit tests
Sample usage, loads the internal config, which references bytelatent/configs/entropy_model.yaml. The precendence order is:
- Default pydantic args
- Included configs, eg `config`
- CLI args
```
python -m bytelatent.print_config config=internal/configs/entropy_model.yaml eval=null
```
Summary:
Test Plan:
