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https://github.com/facebookresearch/blt.git
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afedb16598
Summary: - Make the data/checkpoint code fsspec compatible - Still will not work with s3 saves, due to `torch.distributed.checkpoint.save` not being out of the box workable with `fsspec`. Will implement in followup PR Test Plan: Run unit tests and the commands below ``` python -m bytelatent.train config=internal/configs/s3_debug.yaml eval=null checkpoint.dump.every=100 ``` ``` torchrun --nproc-per-node 8 -m bytelatent.train config=internal/configs/s3_debug.yaml eval=null checkpoint.dump.every=100 ``` These currently won't work due to the torch distributed save, but theses hould be tested at a later date ``` python -m bytelatent.train config=internal/configs/s3_debug.yaml eval=null checkpoint.dump.every=100 dump_dir=s3://blt/scratch/checkpoint-test/ ``` ``` torchrun --nproc-per-node 8 -m bytelatent.train config=internal/configs/s3_debug.yaml eval=null checkpoint.dump.every=100 dump_dir=s3://blt/scratch/checkpoint-test/ ``` |
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| .. | ||
| configs | ||
| data | ||
| model | ||
| plotting | ||
| preprocess | ||
| tokenizers | ||
| .DS_Store | ||
| __init__.py | ||
| args.py | ||
| base_transformer.py | ||
| checkpoint.py | ||
| constants.py | ||
| distributed.py | ||
| entropy_model.py | ||
| eval.py | ||
| float8.py | ||
| generate.py | ||
| logger.py | ||
| metrics.py | ||
| norms.py | ||
| optim.py | ||
| probe.py | ||
| profiling.py | ||
| stool.py | ||
| test_blt.py | ||
| test_entropy_model.py | ||
| train.py | ||
| transformer.py | ||