Pack small PPL eval sets together #199
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dirkgr
approved these changes
Jun 6, 2023
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| tokenizer: | ||
| identifier: gpt2 | ||
| truncate_direction: right | ||
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| save_folder: ${path.choose:${oc.env:SCRATCH_DIR,no_exist}/checkpoints,/results}/${oc.env:SLURM_JOB_ID,${run_name}} | ||
| save_overwrite: false | ||
| # Sharded checkpoints (best for restarts) | ||
| save_interval: 1000 | ||
| save_num_checkpoints_to_keep: 2 | ||
| # Unsharded checkpoints (for final storage) | ||
| save_interval_unsharded: 10000 | ||
| save_num_unsharded_checkpoints_to_keep: -1 | ||
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| load_path: null | ||
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| max_duration: 476837 # 2T tokens | ||
| global_train_batch_size: 2048 | ||
| device_train_microbatch_size: 4 | ||
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| precision: amp_bf16 | ||
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| max_grad_norm: 1.0 | ||
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| speed_monitor: | ||
| window_size: 20 |
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What's all this? Should it be in this PR?
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yes, just moved so we don't have to scroll past all of the evaluation settings to find the other settings.
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Adds the ability to handle multiple language modeling evaluation datasets in a single
Evaluatorwhile still tracking metrics for each dataset separately.This allows us to pack small perplexity evaluation datasets together to make the evaluation loop more efficient and to handle datasets that are too small to use otherwise (because they don't have enough examples for a single batch on all GPUs).
I did a test run to validate the code. Here are the results: https://wandb.ai/ai2-llm/c4-small/reports/Packed-evaluations--Vmlldzo0NTY2Mzg2