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pythia-70m_default

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Tristan commited on
Commit
e007fe4
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1 Parent(s): 0a6af0c

Training in progress, epoch 1

Browse files
Files changed (6) hide show
  1. config.json +30 -0
  2. eval_job_output.txt +126 -0
  3. logs/events.out.tfevents.1715496757.sphinx2 +3 -0
  4. model.safetensors +3 -0
  5. train_job_output.txt +180 -0
  6. training_args.bin +3 -0
config.json ADDED
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+ {
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+ "_name_or_path": "EleutherAI/pythia-70m",
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+ "architectures": [
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+ "GPTNeoXForCausalLM"
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+ ],
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+ "attention_bias": true,
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 0,
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+ "classifier_dropout": 0.1,
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+ "eos_token_id": 0,
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+ "hidden_act": "gelu",
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+ "hidden_dropout": 0.0,
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+ "hidden_size": 512,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 2048,
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 2048,
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+ "model_type": "gpt_neox",
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+ "num_attention_heads": 8,
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+ "num_hidden_layers": 6,
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+ "rope_scaling": null,
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+ "rotary_emb_base": 10000,
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+ "rotary_pct": 0.25,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.39.3",
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+ "use_cache": true,
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+ "use_parallel_residual": true,
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+ "vocab_size": 50304
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+ }
eval_job_output.txt ADDED
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+ slurm submission log: 2024-05-11 22:52:02.402564
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+ created following sbatch script:
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+
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+ ###############################
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+
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+ #!/bin/bash
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+
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+ #SBATCH --account=nlp
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+ #SBATCH --cpus-per-task=16
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+ #SBATCH --dependency=afterok:
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+ #SBATCH --gres=gpu:1
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+ #SBATCH --job-name=tthrush-job-4888498
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+ #SBATCH --mem=60G
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+ #SBATCH --nodelist=sphinx2
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+ #SBATCH --open-mode=append
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+ #SBATCH --output=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default/eval_job_output.txt
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+ #SBATCH --partition=sphinx
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+ #SBATCH --time=14-0
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+
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+ # activate your desired anaconda environment
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+ . /nlp/scr/tthrush/miniconda3/etc/profile.d/conda.sh ; conda activate pretraining-coreset-selection
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+
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+ # cd to working directory
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+ cd .
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+
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+ # launch commands
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+ srun --unbuffered run_as_child_processes 'lm_eval --model hf --model_args pretrained=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default,revision=main,dtype=float16,trust_remote_code=True --tasks xnli_en,xnli_fr,sciq,piqa,lambada,arc_easy --device cuda --output_path /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default/perf'
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+
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+ ###############################
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+
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+ submission to slurm complete!
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+
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+
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+ ###############################
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+ slurm submission output
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+
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+
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+
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+ sbatch: error: Batch job submission failed: Job dependency problem
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+
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+ ###############################
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+
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+ slurm submission log: 2024-05-11 22:53:20.065335
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+ created following sbatch script:
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+
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+ ###############################
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+
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+ #!/bin/bash
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+
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+ #SBATCH --account=nlp
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+ #SBATCH --cpus-per-task=16
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+ #SBATCH --dependency=afterok:7599822
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+ #SBATCH --gres=gpu:1
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+ #SBATCH --job-name=tthrush-job-2562954
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+ #SBATCH --mem=60G
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+ #SBATCH --nodelist=sphinx2
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+ #SBATCH --open-mode=append
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+ #SBATCH --output=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default/eval_job_output.txt
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+ #SBATCH --partition=sphinx
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+ #SBATCH --time=14-0
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+
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+ # activate your desired anaconda environment
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+ . /nlp/scr/tthrush/miniconda3/etc/profile.d/conda.sh ; conda activate pretraining-coreset-selection
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+
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+ # cd to working directory
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+ cd .
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+
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+ # launch commands
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+ srun --unbuffered run_as_child_processes 'lm_eval --model hf --model_args pretrained=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default,revision=main,dtype=float16,trust_remote_code=True --tasks xnli_en,xnli_fr,sciq,piqa,lambada,arc_easy --device cuda --output_path /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default/perf'
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+
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+ ###############################
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+
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+ submission to slurm complete!
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+
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+
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+ ###############################
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+ slurm submission output
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+
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+ Submitted batch job 7599823
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+
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+
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+
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+ ###############################
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+
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+ slurm submission log: 2024-05-11 23:09:48.287855
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+ created following sbatch script:
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+
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+ ###############################
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+
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+ #!/bin/bash
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+
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+ #SBATCH --account=nlp
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+ #SBATCH --cpus-per-task=16
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+ #SBATCH --dependency=afterok:7599868
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+ #SBATCH --gres=gpu:1
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+ #SBATCH --job-name=tthrush-job-4073620
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+ #SBATCH --mem=60G
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+ #SBATCH --nodelist=sphinx2
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+ #SBATCH --open-mode=append
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+ #SBATCH --output=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default/eval_job_output.txt
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+ #SBATCH --partition=sphinx
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+ #SBATCH --time=14-0
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+
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+ # activate your desired anaconda environment
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+ . /nlp/scr/tthrush/miniconda3/etc/profile.d/conda.sh ; conda activate pretraining-coreset-selection
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+
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+ # cd to working directory
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+ cd .
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+
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+ # launch commands
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+ srun --unbuffered run_as_child_processes 'lm_eval --model hf --model_args pretrained=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default,revision=main,dtype=float16,trust_remote_code=True --tasks xnli_en,xnli_fr,sciq,piqa,lambada,arc_easy --device cuda --output_path /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default/perf'
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+
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+ ###############################
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+
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+ submission to slurm complete!
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+
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+
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+ ###############################
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+ slurm submission output
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+
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+ Submitted batch job 7599869
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+
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+
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+
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+ ###############################
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+
logs/events.out.tfevents.1715496757.sphinx2 ADDED
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model.safetensors ADDED
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train_job_output.txt ADDED
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161
  {'loss': 7.289, 'grad_norm': 0.367520809173584, 'learning_rate': 0.0001403180542563143, 'epoch': 0.2}
 
162
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  2%|▏ | 175/10682 [01:39<1:26:11, 2.03it/s]{'loss': 6.8807, 'grad_norm': 0.33734890818595886, 'learning_rate': 0.00016370439663236668, 'epoch': 0.23}
188
 
 
189
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  2%|▏ | 200/10682 [01:51<1:25:57, 2.03it/s]{'loss': 6.5556, 'grad_norm': 0.46203550696372986, 'learning_rate': 0.00018709073900841907, 'epoch': 0.26}
 
215
 
216
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  2%|▏ | 225/10682 [02:04<1:25:38, 2.04it/s]{'loss': 6.2908, 'grad_norm': 0.7612385153770447, 'learning_rate': 0.00021047708138447147, 'epoch': 0.29}
 
242
 
243
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  2%|▏ | 250/10682 [02:16<1:25:30, 2.03it/s]{'loss': 6.0883, 'grad_norm': 0.3854532241821289, 'learning_rate': 0.00023386342376052386, 'epoch': 0.33}
269
 
 
270
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271
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296
  {'loss': 5.9181, 'grad_norm': 0.7595835328102112, 'learning_rate': 0.00025724976613657625, 'epoch': 0.36}
 
297
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298
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  3%|▎ | 300/10682 [02:40<1:25:04, 2.03it/s]{'loss': 5.7819, 'grad_norm': 0.6112937927246094, 'learning_rate': 0.0002806361085126286, 'epoch': 0.39}
 
323
 
324
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350
  {'loss': 5.6576, 'grad_norm': 1.0010818243026733, 'learning_rate': 0.00030402245088868103, 'epoch': 0.43}
 
351
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352
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  3%|▎ | 350/10682 [03:05<1:24:40, 2.03it/s]{'loss': 5.5561, 'grad_norm': 0.5823507308959961, 'learning_rate': 0.00032740879326473337, 'epoch': 0.46}
377
 
 
378
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383
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401
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405
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430
  4%|▎ | 400/10682 [03:30<1:24:31, 2.03it/s]{'loss': 5.375, 'grad_norm': 0.6424997448921204, 'learning_rate': 0.00037418147801683815, 'epoch': 0.52}
 
431
 
432
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433
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457
  4%|▍ | 425/10682 [03:42<1:24:03, 2.03it/s]{'loss': 5.2944, 'grad_norm': 0.4700624942779541, 'learning_rate': 0.0003975678203928906, 'epoch': 0.56}
 
458
 
459
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460
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467
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469
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471
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485
  {'loss': 5.2223, 'grad_norm': 0.4889560043811798, 'learning_rate': 0.00042095416276894293, 'epoch': 0.59}
 
486
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487
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510
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511
  4%|▍ | 475/10682 [04:07<1:23:40, 2.03it/s]{'loss': 5.1492, 'grad_norm': 0.5106998682022095, 'learning_rate': 0.0004443405051449954, 'epoch': 0.62}
 
512
 
513
  4%|▍ | 475/10682 [04:07<1:23:40, 2.03it/s]
514
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515
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517
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520
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522
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527
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530
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531
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532
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534
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536
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537
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538
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539
  {'loss': 5.0961, 'grad_norm': 0.5852717161178589, 'learning_rate': 0.0004677268475210477, 'epoch': 0.66}
 
540
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541
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542
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550
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551
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553
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
  5%|▍ | 525/10682 [04:31<1:23:17, 2.03it/s]{'loss': 5.0379, 'grad_norm': 0.4721851348876953, 'learning_rate': 0.0004911131898971, 'epoch': 0.69}
 
566
 
567
  5%|▍ | 525/10682 [04:31<1:23:17, 2.03it/s]
568
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569
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570
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571
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572
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573
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574
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575
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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587
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588
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590
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591
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592
  5%|▌ | 550/10682 [04:44<1:23:07, 2.03it/s]
593
  {'loss': 4.9823, 'grad_norm': 0.5419530272483826, 'learning_rate': 0.0005144995322731525, 'epoch': 0.72}
 
594
  5%|▌ | 550/10682 [04:44<1:23:07, 2.03it/s]
595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
  5%|▌ | 574/10682 [04:55<1:22:51, 2.03it/s]
619
  5%|▌ | 575/10682 [04:56<1:22:54, 2.03it/s]
620
  {'loss': 4.9327, 'grad_norm': 0.5166158080101013, 'learning_rate': 0.0005378858746492049, 'epoch': 0.75}
 
621
  5%|▌ | 575/10682 [04:56<1:22:54, 2.03it/s]
622
  5%|▌ | 576/10682 [04:56<1:23:00, 2.03it/s]
623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
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634
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635
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636
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637
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638
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639
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640
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641
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642
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643
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644
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645
  6%|▌ | 599/10682 [05:08<1:22:39, 2.03it/s]
646
  6%|▌ | 600/10682 [05:08<1:22:42, 2.03it/s]
647
  {'loss': 4.8904, 'grad_norm': 0.47772836685180664, 'learning_rate': 0.0005612722170252572, 'epoch': 0.79}
 
648
  6%|▌ | 600/10682 [05:08<1:22:42, 2.03it/s]
649
  6%|▌ | 601/10682 [05:09<1:22:56, 2.03it/s]
650
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651
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652
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653
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654
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655
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656
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657
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658
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659
  6%|▌ | 611/10682 [05:14<1:25:59, 1.95it/s]
660
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661
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662
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663
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664
  6%|▌ | 616/10682 [05:16<1:30:13, 1.86it/s]
665
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666
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667
  6%|▌ | 619/10682 [05:18<1:25:07, 1.97it/s]
668
  6%|▌ | 620/10682 [05:18<1:24:15, 1.99it/s]
669
  6%|▌ | 621/10682 [05:19<1:23:44, 2.00it/s]
670
  6%|▌ | 622/10682 [05:19<1:23:18, 2.01it/s]
671
  6%|▌ | 623/10682 [05:20<1:23:01, 2.02it/s]
672
  6%|▌ | 624/10682 [05:20<1:22:56, 2.02it/s]
673
  6%|▌ | 625/10682 [05:21<1:22:46, 2.03it/s]
674
  {'loss': 4.84, 'grad_norm': 0.46007564663887024, 'learning_rate': 0.0005846585594013096, 'epoch': 0.82}
 
675
  6%|▌ | 625/10682 [05:21<1:22:46, 2.03it/s]
676
  6%|▌ | 626/10682 [05:21<1:22:47, 2.02it/s]
677
  6%|▌ | 627/10682 [05:22<1:22:42, 2.03it/s]
678
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679
  6%|▌ | 629/10682 [05:23<1:22:35, 2.03it/s]
680
  6%|▌ | 630/10682 [05:23<1:22:34, 2.03it/s]
681
  6%|▌ | 631/10682 [05:24<1:22:36, 2.03it/s]
682
  6%|▌ | 632/10682 [05:24<1:22:34, 2.03it/s]
683
  6%|▌ | 633/10682 [05:25<1:22:31, 2.03it/s]
684
  6%|▌ | 634/10682 [05:25<1:22:29, 2.03it/s]
685
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686
  6%|▌ | 636/10682 [05:26<1:22:26, 2.03it/s]
687
  6%|▌ | 637/10682 [05:27<1:22:25, 2.03it/s]
688
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689
  6%|▌ | 639/10682 [05:28<1:22:22, 2.03it/s]
690
  6%|▌ | 640/10682 [05:28<1:22:25, 2.03it/s]
691
  6%|▌ | 641/10682 [05:29<1:22:22, 2.03it/s]
692
  6%|▌ | 642/10682 [05:29<1:22:34, 2.03it/s]
693
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694
  6%|▌ | 644/10682 [05:30<1:22:26, 2.03it/s]
695
  6%|▌ | 645/10682 [05:31<1:22:23, 2.03it/s]
696
  6%|▌ | 646/10682 [05:31<1:22:21, 2.03it/s]
697
  6%|▌ | 647/10682 [05:32<1:22:23, 2.03it/s]
698
  6%|▌ | 648/10682 [05:32<1:22:19, 2.03it/s]
699
  6%|▌ | 649/10682 [05:33<1:22:13, 2.03it/s]
700
  6%|▌ | 650/10682 [05:33<1:22:13, 2.03it/s]{'loss': 4.8005, 'grad_norm': 0.5678160190582275, 'learning_rate': 0.0006080449017773621, 'epoch': 0.85}
 
701
 
702
  6%|▌ | 650/10682 [05:33<1:22:13, 2.03it/s]
703
  6%|▌ | 651/10682 [05:34<1:22:16, 2.03it/s]
704
  6%|▌ | 652/10682 [05:34<1:22:10, 2.03it/s]
705
  6%|▌ | 653/10682 [05:35<1:22:16, 2.03it/s]
706
  6%|▌ | 654/10682 [05:35<1:22:15, 2.03it/s]
707
  6%|▌ | 655/10682 [05:35<1:22:15, 2.03it/s]
708
  6%|▌ | 656/10682 [05:36<1:22:15, 2.03it/s]
709
  6%|▌ | 657/10682 [05:36<1:22:10, 2.03it/s]
710
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711
  6%|▌ | 659/10682 [05:37<1:22:11, 2.03it/s]
712
  6%|▌ | 660/10682 [05:38<1:22:09, 2.03it/s]
713
  6%|▌ | 661/10682 [05:38<1:22:13, 2.03it/s]
714
  6%|▌ | 662/10682 [05:39<1:22:08, 2.03it/s]
715
  6%|▌ | 663/10682 [05:39<1:22:09, 2.03it/s]
716
  6%|▌ | 664/10682 [05:40<1:22:09, 2.03it/s]
717
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718
  6%|▌ | 666/10682 [05:41<1:22:07, 2.03it/s]
719
  6%|▌ | 667/10682 [05:41<1:22:09, 2.03it/s]
720
  6%|▋ | 668/10682 [05:42<1:22:09, 2.03it/s]
721
  6%|▋ | 669/10682 [05:42<1:22:11, 2.03it/s]
722
  6%|▋ | 670/10682 [05:43<1:22:10, 2.03it/s]
723
  6%|▋ | 671/10682 [05:43<1:22:05, 2.03it/s]
724
  6%|▋ | 672/10682 [05:44<1:22:01, 2.03it/s]
725
  6%|▋ | 673/10682 [05:44<1:22:07, 2.03it/s]
726
  6%|▋ | 674/10682 [05:45<1:22:03, 2.03it/s]
727
  6%|▋ | 675/10682 [05:45<1:22:01, 2.03it/s]
728
  {'loss': 4.7671, 'grad_norm': 0.4880385100841522, 'learning_rate': 0.0006314312441534145, 'epoch': 0.88}
 
729
  6%|▋ | 675/10682 [05:45<1:22:01, 2.03it/s]
730
  6%|▋ | 676/10682 [05:46<1:22:10, 2.03it/s]
731
  6%|▋ | 677/10682 [05:46<1:22:05, 2.03it/s]
732
  6%|▋ | 678/10682 [05:47<1:22:01, 2.03it/s]
733
  6%|▋ | 679/10682 [05:47<1:22:00, 2.03it/s]
734
  6%|▋ | 680/10682 [05:48<1:22:00, 2.03it/s]
735
  6%|▋ | 681/10682 [05:48<1:21:58, 2.03it/s]
736
  6%|▋ | 682/10682 [05:49<1:22:00, 2.03it/s]
737
  6%|▋ | 683/10682 [05:49<1:22:00, 2.03it/s]
738
  6%|▋ | 684/10682 [05:50<1:21:53, 2.03it/s]
739
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740
  6%|▋ | 686/10682 [05:51<1:21:58, 2.03it/s]
741
  6%|▋ | 687/10682 [05:51<1:21:59, 2.03it/s]
742
  6%|▋ | 688/10682 [05:52<1:21:58, 2.03it/s]
743
  6%|▋ | 689/10682 [05:52<1:21:58, 2.03it/s]
744
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745
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746
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747
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748
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749
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750
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751
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752
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753
  7%|▋ | 699/10682 [05:57<1:21:52, 2.03it/s]
754
  7%|▋ | 700/10682 [05:58<1:21:56, 2.03it/s]{'loss': 4.7325, 'grad_norm': 0.42659100890159607, 'learning_rate': 0.0006548175865294667, 'epoch': 0.92}
 
755
 
756
  7%|▋ | 700/10682 [05:58<1:21:56, 2.03it/s]
757
  7%|▋ | 701/10682 [05:58<1:22:04, 2.03it/s]
758
  7%|▋ | 702/10682 [05:59<1:22:01, 2.03it/s]
759
  7%|▋ | 703/10682 [05:59<1:21:58, 2.03it/s]
760
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761
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762
  7%|▋ | 706/10682 [06:01<1:21:51, 2.03it/s]
763
  7%|▋ | 707/10682 [06:01<1:21:50, 2.03it/s]
764
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765
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766
  7%|▋ | 710/10682 [06:03<1:21:50, 2.03it/s]
767
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768
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769
  7%|▋ | 713/10682 [06:04<1:21:42, 2.03it/s]
770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
  7%|▋ | 724/10682 [06:09<1:21:41, 2.03it/s]
781
  7%|▋ | 725/10682 [06:10<1:21:41, 2.03it/s]{'loss': 4.7051, 'grad_norm': 0.42874085903167725, 'learning_rate': 0.0006782039289055192, 'epoch': 0.95}
782
 
 
783
  7%|▋ | 725/10682 [06:10<1:21:41, 2.03it/s]
784
  7%|▋ | 726/10682 [06:10<1:21:43, 2.03it/s]
785
  7%|▋ | 727/10682 [06:11<1:21:45, 2.03it/s]
786
  7%|▋ | 728/10682 [06:11<1:21:43, 2.03it/s]
787
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788
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789
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790
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791
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792
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793
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794
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795
  7%|▋ | 737/10682 [06:16<1:21:37, 2.03it/s]
796
  7%|▋ | 738/10682 [06:16<1:21:36, 2.03it/s]
797
  7%|▋ | 739/10682 [06:17<1:21:32, 2.03it/s]
798
  7%|▋ | 740/10682 [06:17<1:21:32, 2.03it/s]
799
  7%|▋ | 741/10682 [06:18<1:21:29, 2.03it/s]
800
  7%|▋ | 742/10682 [06:18<1:21:31, 2.03it/s]
801
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802
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803
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804
  7%|▋ | 746/10682 [06:20<1:21:29, 2.03it/s]
805
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806
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807
  7%|▋ | 749/10682 [06:22<1:21:30, 2.03it/s]
808
  7%|▋ | 750/10682 [06:22<1:21:26, 2.03it/s]
809
  {'loss': 4.6758, 'grad_norm': 0.3773520588874817, 'learning_rate': 0.0007015902712815716, 'epoch': 0.98}
 
810
  7%|▋ | 750/10682 [06:22<1:21:26, 2.03it/s]
811
  7%|▋ | 751/10682 [06:23<1:21:31, 2.03it/s]
812
  7%|▋ | 752/10682 [06:23<1:21:26, 2.03it/s]
813
  7%|▋ | 753/10682 [06:24<1:21:25, 2.03it/s]
814
  7%|▋ | 754/10682 [06:24<1:21:27, 2.03it/s]
815
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816
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817
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818
  7%|▋ | 758/10682 [06:26<1:21:23, 2.03it/s]
819
  7%|▋ | 759/10682 [06:27<1:21:27, 2.03it/s]
820
  7%|▋ | 760/10682 [06:27<1:21:26, 2.03it/s]
821
  7%|▋ | 761/10682 [06:28<1:21:26, 2.03it/s]
822
  7%|▋ | 762/10682 [06:28<1:21:29, 2.03it/s]
823
  7%|▋ | 763/10682 [06:29<1:20:48, 2.05it/s]
824
  7%|▋ | 764/10682 [06:41<10:54:45, 3.96s/it]
825
  7%|▋ | 765/10682 [06:41<8:02:42, 2.92s/it]
826
  7%|▋ | 766/10682 [06:42<6:02:29, 2.19s/it]
827
  7%|▋ | 767/10682 [06:42<4:38:05, 1.68s/it]
828
  7%|▋ | 768/10682 [06:43<3:39:03, 1.33s/it]
829
  7%|▋ | 769/10682 [06:43<2:57:38, 1.08s/it]
830
  7%|▋ | 770/10682 [06:44<2:28:48, 1.11it/s]
831
  7%|▋ | 771/10682 [06:44<2:09:50, 1.27it/s]
832
  7%|▋ | 772/10682 [06:45<1:55:25, 1.43it/s]
833
  7%|▋ | 773/10682 [06:45<1:45:07, 1.57it/s]
834
  7%|▋ | 774/10682 [06:46<1:38:06, 1.68it/s]
835
  7%|▋ | 775/10682 [06:46<1:33:05, 1.77it/s]{'loss': 4.6275, 'grad_norm': 0.46725699305534363, 'learning_rate': 0.0007249766136576241, 'epoch': 1.02}
836
 
 
837
  7%|▋ | 775/10682 [06:46<1:33:05, 1.77it/s]
838
  7%|▋ | 776/10682 [06:47<1:29:45, 1.84it/s]
839
  7%|▋ | 777/10682 [06:47<1:27:14, 1.89it/s]
840
  7%|▋ | 778/10682 [06:48<1:25:30, 1.93it/s]
841
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842
  7%|▋ | 780/10682 [06:49<1:23:16, 1.98it/s]
843
  7%|▋ | 781/10682 [06:49<1:22:32, 2.00it/s]
844
  7%|▋ | 782/10682 [06:50<1:22:18, 2.00it/s]
845
  7%|▋ | 783/10682 [06:50<1:21:57, 2.01it/s]
846
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847
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+ slurm submission log: 2024-05-11 22:52:02.103456
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+ created following sbatch script:
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+
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+ ###############################
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+
6
+ #!/bin/bash
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+
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+ #SBATCH --account=nlp
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+ #SBATCH --cpus-per-task=16
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+ #SBATCH --dependency=afterok:
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+ #SBATCH --gres=gpu:2
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+ #SBATCH --job-name=tthrush-job-2343873
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+ #SBATCH --mem=400G
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+ #SBATCH --nodelist=sphinx2
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+ #SBATCH --open-mode=append
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+ #SBATCH --output=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default/train_job_output.txt
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+ #SBATCH --partition=sphinx
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+ #SBATCH --time=14-0
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+
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+ # activate your desired anaconda environment
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+ . /nlp/scr/tthrush/miniconda3/etc/profile.d/conda.sh ; conda activate pretraining-coreset-selection
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+
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+ # cd to working directory
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+ cd .
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+
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+ # launch commands
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+ srun --unbuffered run_as_child_processes 'torchrun --master_port 29499 --nproc_per_node=2 train_llm.py --dataset_id /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/default --output_dir /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default --output_hub_id pythia-70m_default --model_id EleutherAI/pythia-70m --num_train_epochs 14 --learning_rate 1e-3 --warmup_ratio=0.1 --gradient_accumulation_steps 2'
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+
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+ ###############################
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+
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+ submission to slurm complete!
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+
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+
34
+ ###############################
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+ slurm submission output
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+
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+
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+
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+ sbatch: error: Batch job submission failed: Job dependency problem
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+
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+ ###############################
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+
43
+ slurm submission log: 2024-05-11 22:53:19.792950
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+ created following sbatch script:
45
+
46
+ ###############################
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+
48
+ #!/bin/bash
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+
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+ #SBATCH --account=nlp
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+ #SBATCH --cpus-per-task=16
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+ #SBATCH --dependency=afterok:7599821
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+ #SBATCH --gres=gpu:2
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+ #SBATCH --job-name=tthrush-job-4621093
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+ #SBATCH --mem=400G
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+ #SBATCH --nodelist=sphinx2
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+ #SBATCH --open-mode=append
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+ #SBATCH --output=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default/train_job_output.txt
59
+ #SBATCH --partition=sphinx
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+ #SBATCH --time=14-0
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+
62
+ # activate your desired anaconda environment
63
+ . /nlp/scr/tthrush/miniconda3/etc/profile.d/conda.sh ; conda activate pretraining-coreset-selection
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+
65
+ # cd to working directory
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+ cd .
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+
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+ # launch commands
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+ srun --unbuffered run_as_child_processes 'torchrun --master_port 29499 --nproc_per_node=2 train_llm.py --dataset_id /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/default --output_dir /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default --output_hub_id pythia-70m_default --model_id EleutherAI/pythia-70m --num_train_epochs 14 --learning_rate 1e-3 --warmup_ratio=0.1 --gradient_accumulation_steps 2'
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+
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+ ###############################
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+
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+ submission to slurm complete!
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+
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+
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+ ###############################
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+ slurm submission output
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+
79
+ Submitted batch job 7599822
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+
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+
82
+
83
+ ###############################
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+
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+ slurm submission log: 2024-05-11 23:09:47.984388
86
+ created following sbatch script:
87
+
88
+ ###############################
89
+
90
+ #!/bin/bash
91
+
92
+ #SBATCH --account=nlp
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+ #SBATCH --cpus-per-task=16
94
+ #SBATCH --dependency=afterok:7599867
95
+ #SBATCH --gres=gpu:2
96
+ #SBATCH --job-name=tthrush-job-4866328
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+ #SBATCH --mem=400G
98
+ #SBATCH --nodelist=sphinx2
99
+ #SBATCH --open-mode=append
100
+ #SBATCH --output=/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default/train_job_output.txt
101
+ #SBATCH --partition=sphinx
102
+ #SBATCH --time=14-0
103
+
104
+ # activate your desired anaconda environment
105
+ . /nlp/scr/tthrush/miniconda3/etc/profile.d/conda.sh ; conda activate pretraining-coreset-selection
106
+
107
+ # cd to working directory
108
+ cd .
109
+
110
+ # launch commands
111
+ srun --unbuffered run_as_child_processes 'torchrun --master_port 29499 --nproc_per_node=2 train_llm.py --dataset_id /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/default --output_dir /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default --output_hub_id pythia-70m_default --model_id EleutherAI/pythia-70m --num_train_epochs 14 --learning_rate 1e-3 --warmup_ratio=0.1 --gradient_accumulation_steps 2'
112
+
113
+ ###############################
114
+
115
+ submission to slurm complete!
116
+
117
+
118
+ ###############################
119
+ slurm submission output
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+
121
+ Submitted batch job 7599868
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+
123
+
124
+
125
+ ###############################
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+
127
+ ###############################
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+ start time: 2024-05-11 23:52:22.564861
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+ machine: sphinx2
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+ conda env: pretraining-coreset-selection
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+ ###############################
132
+ running following processes
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+
134
+ torchrun --master_port 29499 --nproc_per_node=2 train_llm.py --dataset_id /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/default --output_dir /juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default --output_hub_id pythia-70m_default --model_id EleutherAI/pythia-70m --num_train_epochs 14 --learning_rate 1e-3 --warmup_ratio=0.1 --gradient_accumulation_steps 2
135
+
136
+
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+ ###############################
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+ command outputs:
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+
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+
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+ [2024-05-11 23:52:24,535] torch.distributed.run: [WARNING]
142
+ [2024-05-11 23:52:24,535] torch.distributed.run: [WARNING] *****************************************
143
+ [2024-05-11 23:52:24,535] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
144
+ [2024-05-11 23:52:24,535] torch.distributed.run: [WARNING] *****************************************
145
+ 05/11/2024 23:52:30 - INFO - __main__ - Script parameters ScriptArguments(dataset_id='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/default', output_dir='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default', output_hub_id='pythia-70m_default', hf_hub_token=True, model_id='EleutherAI/pythia-70m', per_device_train_batch_size=256, num_train_epochs=14, learning_rate=0.001, gradient_accumulation_steps=2, from_scratch=True, warmup_ratio=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, weight_decay=0.01, lr_scheduler_type='cosine', local_rank=0, resume_from_checkpoint=False, deepspeed=None, peft=False)
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+ 05/11/2024 23:52:30 - INFO - __main__ - Script parameters ScriptArguments(dataset_id='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/train_data_5/default', output_dir='/juice5/scr5/tthrush/pretraining-coreset-selection/llm_pretraining/llms_5/pythia-70m_default', output_hub_id='pythia-70m_default', hf_hub_token=True, model_id='EleutherAI/pythia-70m', per_device_train_batch_size=256, num_train_epochs=14, learning_rate=0.001, gradient_accumulation_steps=2, from_scratch=True, warmup_ratio=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, weight_decay=0.01, lr_scheduler_type='cosine', local_rank=0, resume_from_checkpoint=False, deepspeed=None, peft=False)
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  0%| | 0/10682 [00:00<?, ?it/s][rank0]:[W reducer.cpp:1360] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
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+ [rank1]:[W reducer.cpp:1360] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
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316
  {'loss': 7.289, 'grad_norm': 0.367520809173584, 'learning_rate': 0.0001403180542563143, 'epoch': 0.2}
317
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  2%|▏ | 175/10682 [01:39<1:26:11, 2.03it/s]{'loss': 6.8807, 'grad_norm': 0.33734890818595886, 'learning_rate': 0.00016370439663236668, 'epoch': 0.23}
344
 
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371
  2%|▏ | 200/10682 [01:51<1:25:57, 2.03it/s]{'loss': 6.5556, 'grad_norm': 0.46203550696372986, 'learning_rate': 0.00018709073900841907, 'epoch': 0.26}
372
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374
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  2%|▏ | 225/10682 [02:04<1:25:38, 2.04it/s]{'loss': 6.2908, 'grad_norm': 0.7612385153770447, 'learning_rate': 0.00021047708138447147, 'epoch': 0.29}
400
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402
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  2%|▏ | 250/10682 [02:16<1:25:30, 2.03it/s]{'loss': 6.0883, 'grad_norm': 0.3854532241821289, 'learning_rate': 0.00023386342376052386, 'epoch': 0.33}
428
 
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456
  {'loss': 5.9181, 'grad_norm': 0.7595835328102112, 'learning_rate': 0.00025724976613657625, 'epoch': 0.36}
457
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  3%|▎ | 300/10682 [02:40<1:25:04, 2.03it/s]{'loss': 5.7819, 'grad_norm': 0.6112937927246094, 'learning_rate': 0.0002806361085126286, 'epoch': 0.39}
484
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512
  {'loss': 5.6576, 'grad_norm': 1.0010818243026733, 'learning_rate': 0.00030402245088868103, 'epoch': 0.43}
513
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530
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539
  3%|▎ | 350/10682 [03:05<1:24:40, 2.03it/s]{'loss': 5.5561, 'grad_norm': 0.5823507308959961, 'learning_rate': 0.00032740879326473337, 'epoch': 0.46}
540
 
541
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542
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544
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545
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546
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547
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548
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550
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551
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552
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553
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
 
569
+
570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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585
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586
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588
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590
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591
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593
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595
  4%|▎ | 400/10682 [03:30<1:24:31, 2.03it/s]{'loss': 5.375, 'grad_norm': 0.6424997448921204, 'learning_rate': 0.00037418147801683815, 'epoch': 0.52}
596
+
597
 
598
  4%|▎ | 400/10682 [03:30<1:24:31, 2.03it/s]
599
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600
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602
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608
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610
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611
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613
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614
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618
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619
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620
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621
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622
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623
  4%|▍ | 425/10682 [03:42<1:24:03, 2.03it/s]{'loss': 5.2944, 'grad_norm': 0.4700624942779541, 'learning_rate': 0.0003975678203928906, 'epoch': 0.56}
624
+
625
 
626
  4%|▍ | 425/10682 [03:42<1:24:03, 2.03it/s]
627
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628
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629
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630
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631
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632
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633
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635
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650
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651
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652
  {'loss': 5.2223, 'grad_norm': 0.4889560043811798, 'learning_rate': 0.00042095416276894293, 'epoch': 0.59}
653
+
654
  4%|▍ | 450/10682 [03:54<1:23:56, 2.03it/s]
655
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658
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663
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664
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665
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668
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669
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670
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672
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676
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677
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678
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679
  4%|▍ | 475/10682 [04:07<1:23:40, 2.03it/s]{'loss': 5.1492, 'grad_norm': 0.5106998682022095, 'learning_rate': 0.0004443405051449954, 'epoch': 0.62}
680
+
681
 
682
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683
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684
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686
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690
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691
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692
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694
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697
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698
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700
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703
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704
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705
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706
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707
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708
  {'loss': 5.0961, 'grad_norm': 0.5852717161178589, 'learning_rate': 0.0004677268475210477, 'epoch': 0.66}
709
+
710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
  5%|▍ | 525/10682 [04:31<1:23:17, 2.03it/s]{'loss': 5.0379, 'grad_norm': 0.4721851348876953, 'learning_rate': 0.0004911131898971, 'epoch': 0.69}
736
+
737
 
738
  5%|▍ | 525/10682 [04:31<1:23:17, 2.03it/s]
739
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740
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741
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742
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743
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744
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745
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746
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747
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748
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749
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750
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751
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752
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753
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754
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755
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756
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757
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758
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759
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760
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761
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762
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763
  5%|▌ | 550/10682 [04:44<1:23:07, 2.03it/s]
764
  {'loss': 4.9823, 'grad_norm': 0.5419530272483826, 'learning_rate': 0.0005144995322731525, 'epoch': 0.72}
765
+
766
  5%|▌ | 550/10682 [04:44<1:23:07, 2.03it/s]
767
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768
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769
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770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
  5%|▌ | 569/10682 [04:53<1:22:49, 2.03it/s]
786
  5%|▌ | 570/10682 [04:53<1:22:49, 2.03it/s]
787
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788
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789
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790
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791
  5%|▌ | 575/10682 [04:56<1:22:54, 2.03it/s]
792
  {'loss': 4.9327, 'grad_norm': 0.5166158080101013, 'learning_rate': 0.0005378858746492049, 'epoch': 0.75}
793
+
794
  5%|▌ | 575/10682 [04:56<1:22:54, 2.03it/s]
795
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796
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797
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798
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799
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800
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801
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802
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803
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804
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805
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806
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807
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808
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809
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810
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811
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812
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813
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814
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815
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816
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817
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818
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819
  6%|▌ | 600/10682 [05:08<1:22:42, 2.03it/s]
820
  {'loss': 4.8904, 'grad_norm': 0.47772836685180664, 'learning_rate': 0.0005612722170252572, 'epoch': 0.79}
821
+
822
  6%|▌ | 600/10682 [05:08<1:22:42, 2.03it/s]
823
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824
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825
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826
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827
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828
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829
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830
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831
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832
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833
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834
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835
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836
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837
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838
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839
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840
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841
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842
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843
  6%|▌ | 621/10682 [05:19<1:23:44, 2.00it/s]
844
  6%|▌ | 622/10682 [05:19<1:23:18, 2.01it/s]
845
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846
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847
  6%|▌ | 625/10682 [05:21<1:22:46, 2.03it/s]
848
  {'loss': 4.84, 'grad_norm': 0.46007564663887024, 'learning_rate': 0.0005846585594013096, 'epoch': 0.82}
849
+
850
  6%|▌ | 625/10682 [05:21<1:22:46, 2.03it/s]
851
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852
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853
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854
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855
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856
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857
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858
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859
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860
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861
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862
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863
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864
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865
  6%|▌ | 640/10682 [05:28<1:22:25, 2.03it/s]
866
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867
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868
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869
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870
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871
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872
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873
  6%|▌ | 648/10682 [05:32<1:22:19, 2.03it/s]
874
  6%|▌ | 649/10682 [05:33<1:22:13, 2.03it/s]
875
  6%|▌ | 650/10682 [05:33<1:22:13, 2.03it/s]{'loss': 4.8005, 'grad_norm': 0.5678160190582275, 'learning_rate': 0.0006080449017773621, 'epoch': 0.85}
876
+
877
 
878
  6%|▌ | 650/10682 [05:33<1:22:13, 2.03it/s]
879
  6%|▌ | 651/10682 [05:34<1:22:16, 2.03it/s]
880
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881
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882
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883
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884
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885
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886
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887
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888
  6%|▌ | 660/10682 [05:38<1:22:09, 2.03it/s]
889
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890
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891
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892
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893
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894
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895
  6%|▌ | 667/10682 [05:41<1:22:09, 2.03it/s]
896
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897
  6%|▋ | 669/10682 [05:42<1:22:11, 2.03it/s]
898
  6%|▋ | 670/10682 [05:43<1:22:10, 2.03it/s]
899
  6%|▋ | 671/10682 [05:43<1:22:05, 2.03it/s]
900
  6%|▋ | 672/10682 [05:44<1:22:01, 2.03it/s]
901
  6%|▋ | 673/10682 [05:44<1:22:07, 2.03it/s]
902
  6%|▋ | 674/10682 [05:45<1:22:03, 2.03it/s]
903
  6%|▋ | 675/10682 [05:45<1:22:01, 2.03it/s]
904
  {'loss': 4.7671, 'grad_norm': 0.4880385100841522, 'learning_rate': 0.0006314312441534145, 'epoch': 0.88}
905
+
906
  6%|▋ | 675/10682 [05:45<1:22:01, 2.03it/s]
907
  6%|▋ | 676/10682 [05:46<1:22:10, 2.03it/s]
908
  6%|▋ | 677/10682 [05:46<1:22:05, 2.03it/s]
909
  6%|▋ | 678/10682 [05:47<1:22:01, 2.03it/s]
910
  6%|▋ | 679/10682 [05:47<1:22:00, 2.03it/s]
911
  6%|▋ | 680/10682 [05:48<1:22:00, 2.03it/s]
912
  6%|▋ | 681/10682 [05:48<1:21:58, 2.03it/s]
913
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914
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915
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916
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917
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918
  6%|▋ | 687/10682 [05:51<1:21:59, 2.03it/s]
919
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920
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921
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922
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923
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924
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925
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926
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927
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928
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929
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930
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931
  7%|▋ | 700/10682 [05:58<1:21:56, 2.03it/s]{'loss': 4.7325, 'grad_norm': 0.42659100890159607, 'learning_rate': 0.0006548175865294667, 'epoch': 0.92}
932
+
933
 
934
  7%|▋ | 700/10682 [05:58<1:21:56, 2.03it/s]
935
  7%|▋ | 701/10682 [05:58<1:22:04, 2.03it/s]
936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
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950
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951
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952
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953
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954
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955
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956
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957
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958
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959
  7%|▋ | 725/10682 [06:10<1:21:41, 2.03it/s]{'loss': 4.7051, 'grad_norm': 0.42874085903167725, 'learning_rate': 0.0006782039289055192, 'epoch': 0.95}
960
 
961
+
962
  7%|▋ | 725/10682 [06:10<1:21:41, 2.03it/s]
963
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964
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965
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966
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967
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968
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969
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970
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971
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972
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973
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974
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975
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976
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977
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978
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979
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980
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981
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982
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983
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984
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985
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986
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987
  7%|▋ | 750/10682 [06:22<1:21:26, 2.03it/s]
988
  {'loss': 4.6758, 'grad_norm': 0.3773520588874817, 'learning_rate': 0.0007015902712815716, 'epoch': 0.98}
989
+
990
  7%|▋ | 750/10682 [06:22<1:21:26, 2.03it/s]
991
  7%|▋ | 751/10682 [06:23<1:21:31, 2.03it/s]
992
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993
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994
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995
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996
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997
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998
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999
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1000
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1001
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1002
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1003
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1004
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1005
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1006
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1007
  7%|▋ | 767/10682 [06:42<4:38:05, 1.68s/it]
1008
  7%|▋ | 768/10682 [06:43<3:39:03, 1.33s/it]
1009
  7%|▋ | 769/10682 [06:43<2:57:38, 1.08s/it]
1010
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1011
  7%|▋ | 771/10682 [06:44<2:09:50, 1.27it/s]
1012
  7%|▋ | 772/10682 [06:45<1:55:25, 1.43it/s]
1013
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1014
  7%|▋ | 774/10682 [06:46<1:38:06, 1.68it/s]
1015
  7%|▋ | 775/10682 [06:46<1:33:05, 1.77it/s]{'loss': 4.6275, 'grad_norm': 0.46725699305534363, 'learning_rate': 0.0007249766136576241, 'epoch': 1.02}
1016
 
1017
+
1018
  7%|▋ | 775/10682 [06:46<1:33:05, 1.77it/s]
1019
  7%|▋ | 776/10682 [06:47<1:29:45, 1.84it/s]
1020
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training_args.bin ADDED
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+ size 5112