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Upload llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16

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Files changed (8) hide show
  1. .gitattributes +1 -0
  2. llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/bench.slurm +111 -0
  3. llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/config.yaml +90 -0
  4. llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/log.out +320 -0
  5. llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/log_metrics.csv +21 -0
  6. llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/profiler.csv +2 -0
  7. llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/profiler/ip-26-0-161-178_137134.1719929907506010984.pt.trace.json +3 -0
  8. llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/status.txt +1 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/profiler/ip-26-0-161-178_137134.1719929907506010984.pt.trace.json filter=lfs diff=lfs merge=lfs -text
llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/bench.slurm ADDED
@@ -0,0 +1,111 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+
3
+ #SBATCH --job-name=bench_cluster
4
+ #SBATCH --time=00:59:00
5
+ #SBATCH --partition=hopper-prod
6
+ #SBATCH --nodes=2
7
+ #SBATCH --gres=gpu:8
8
+ #SBATCH --qos=high
9
+ #SBATCH --ntasks-per-node=1
10
+ #SBATCH --cpus-per-task=96
11
+ #SBATCH --exclusive
12
+ #SBATCH --output=/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/log.out
13
+ #SBATCH --error=/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/log.out
14
+
15
+ # Function to update status based on squeue output
16
+ update_status() {
17
+ job_id=$1
18
+ status_file=$2
19
+ # For unknown reasons, it doenst update status for pending. It only works for running
20
+ while true; do
21
+ job_status=$(squeue --job $job_id --noheader --format=%T)
22
+ echo "Job status: $job_status"
23
+ if [ -z "$job_status" ]; then
24
+ # Job has finished or is not found
25
+ break
26
+ elif [ "$job_status" = "RUNNING" ]; then
27
+ printf "running" > $status_file
28
+ break
29
+ fi
30
+ sleep 10
31
+ done
32
+ }
33
+
34
+ # Misc initializations.
35
+ echo "========================"
36
+ echo "START TIME: $(date)"
37
+ source /fsx/ferdinandmom/miniforge3/etc/profile.d/conda.sh
38
+ conda activate /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster
39
+ echo python3 version = $(python3 --version)
40
+ echo "========================"
41
+
42
+ # Slurm stuff
43
+ export HOSTNAMES=$(scontrol show hostnames "$SLURM_JOB_NODELIST")
44
+ export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
45
+ export MASTER_PORT=$((1024 + RANDOM % 64511))
46
+
47
+ export TMPDIR=/scratch
48
+ export HF_DATASETS_CACHE="/admin/home/ferdinand_mom/.cache"
49
+ export CUBLAS_WORKSPACE_CONFIG=":4096:8"
50
+ export CUDA_DEVICE_MAX_CONNECTIONS="1"
51
+
52
+ huggingface-cli login --token $HUGGINGFACE_TOKEN
53
+
54
+
55
+ NANOTRON_REPO="/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron"
56
+ CMD="$NANOTRON_REPO/run_train.py --config-file /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/config.yaml"
57
+
58
+ LAUNCHER="torchrun \
59
+ --nproc_per_node 8 \
60
+ --nnodes 2 \
61
+ --rdzv_endpoint ${MASTER_ADDR}:${MASTER_PORT} \
62
+ --rdzv_backend c10d \
63
+ --max_restarts 0 \
64
+ --tee 3 \
65
+ --node_rank ${SLURM_PROCID}"
66
+
67
+ # Checkout the bench_cluster branch
68
+ cd $NANOTRON_REPO
69
+ git checkout bench_cluster
70
+ cd ..
71
+ # Get the current job ID
72
+ job_id=${SLURM_JOB_ID}
73
+
74
+ # Update status to "pending" or "running" in the background
75
+ update_status $job_id /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/status.txt &
76
+
77
+ # Run the main command
78
+ srun -u $LAUNCHER $CMD
79
+ exit_status=$?
80
+
81
+ # Update status based on the exit status of `srun`
82
+ if [ $exit_status -eq 0 ]; then
83
+ printf "completed" > /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/status.txt
84
+ else
85
+ if grep -q "OutOfMemoryError" /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/log.out; then
86
+ printf "oom" > /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/status.txt
87
+ elif grep -q " CUDA error: an illegal memory access" /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/log.out; then
88
+ printf "oom" > /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/status.txt
89
+ elif grep -q "Timeout at NCCL" /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/log.out; then
90
+ printf "timeout" > /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/status.txt
91
+ else
92
+ printf "fail" > /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/status.txt
93
+ fi
94
+ fi
95
+
96
+ # Run the report script if the job completed successfully
97
+ if [ $exit_status -eq 0 ]; then
98
+ python /fsx/ferdinandmom/ferdinand-hf/bench_cluster/main.py report --inp_dir /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16 --is_logs
99
+ python /fsx/ferdinandmom/ferdinand-hf/bench_cluster/main.py report --inp_dir /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16 --is_profiler
100
+ fi
101
+
102
+
103
+ # Push to hub the folder using huggingface_cli
104
+ huggingface-cli upload nanotron/bench_cluster /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16 llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16 --commit-message "Upload llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16"
105
+
106
+ # Verify the upload
107
+ if [ $? -eq 0 ]; then
108
+ echo "Uploading to Huggingface Hub successful"
109
+ else
110
+ echo "Failed to upload to Huggingface Hub"
111
+ fi
llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/config.yaml ADDED
@@ -0,0 +1,90 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ general:
2
+ project: bench_cluster
3
+ seed: 42
4
+ model:
5
+ ddp_bucket_cap_mb: 25
6
+ dtype: bfloat16
7
+ init_method:
8
+ std: 0.025
9
+ make_vocab_size_divisible_by: 1
10
+ model_config:
11
+ bos_token_id: 1
12
+ eos_token_id: 2
13
+ hidden_act: silu
14
+ hidden_size: 2048
15
+ initializer_range: 0.02
16
+ intermediate_size: 4096
17
+ is_llama_config: true
18
+ max_position_embeddings: 4096
19
+ num_attention_heads: 32
20
+ num_hidden_layers: 24
21
+ num_key_value_heads: 32
22
+ pad_token_id: null
23
+ pretraining_tp: 1
24
+ rms_norm_eps: 1.0e-05
25
+ rope_scaling: null
26
+ rope_theta: 10000.0
27
+ tie_word_embeddings: true
28
+ use_cache: true
29
+ vocab_size: 50257
30
+ optimizer:
31
+ accumulate_grad_in_fp32: true
32
+ clip_grad: 1.0
33
+ learning_rate_scheduler:
34
+ learning_rate: 0.0001
35
+ lr_decay_style: linear
36
+ lr_warmup_style: linear
37
+ lr_warmup_steps: 1
38
+ min_decay_lr: 1.0e-05
39
+ optimizer_factory:
40
+ adam_beta1: 0.9
41
+ adam_beta2: 0.95
42
+ adam_eps: 1.0e-08
43
+ name: adamW
44
+ torch_adam_is_fused: true
45
+ weight_decay: 0.01
46
+ zero_stage: 1
47
+ parallelism:
48
+ dp: 4
49
+ expert_parallel_size: 1
50
+ pp: 1
51
+ pp_engine: 1f1b
52
+ tp: 4
53
+ tp_linear_async_communication: false
54
+ tp_mode: REDUCE_SCATTER
55
+ profiler:
56
+ profiler_export_path: /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16
57
+ tokenizer:
58
+ tokenizer_max_length: null
59
+ tokenizer_name_or_path: openai-community/gpt2
60
+ tokenizer_revision: null
61
+ data_stages:
62
+ - name: Training Stage
63
+ start_training_step: 1
64
+ data:
65
+ dataset:
66
+ dataset_overwrite_cache: false
67
+ dataset_processing_num_proc_per_process: 64
68
+ hf_dataset_config_name: null
69
+ hf_dataset_or_datasets: roneneldan/TinyStories
70
+ hf_dataset_splits: train
71
+ text_column_name: text
72
+ num_loading_workers: 32
73
+ seed: 42
74
+ lighteval: null
75
+ tokens:
76
+ train_steps: 20
77
+ val_check_interval: -1
78
+ batch_accumulation_per_replica: 16
79
+ limit_test_batches: 0
80
+ limit_val_batches: 0
81
+ micro_batch_size: 16
82
+ sequence_length: 4096
83
+ logging:
84
+ iteration_step_info_interval: 1
85
+ log_level: info
86
+ log_level_replica: info
87
+ checkpoints:
88
+ checkpoint_interval: 100000
89
+ checkpoints_path: /dev/null
90
+ resume_checkpoint_path: null
llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/log.out ADDED
@@ -0,0 +1,320 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ========================
2
+ START TIME: Tue Jul 2 14:15:28 UTC 2024
3
+ python3 version = Python 3.10.14
4
+ ========================
5
+ The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.
6
+ Token is valid (permission: write).
7
+ Your token has been saved to /admin/home/ferdinand_mom/.cache/huggingface/token
8
+ Login successful
9
+ Already on 'bench_cluster'
10
+ M examples/config_tiny_llama.py
11
+ M examples/config_tiny_llama.yaml
12
+ M examples/train_tiny_llama.sh
13
+ M src/nanotron/models/llama.py
14
+ M src/nanotron/trainer.py
15
+ Your branch is up to date with 'origin/bench_cluster'.
16
+ Job status: RUNNING
17
+ W0702 14:15:31.127000 139972170110784 torch/distributed/run.py:757]
18
+ W0702 14:15:31.127000 139972170110784 torch/distributed/run.py:757] *****************************************
19
+ W0702 14:15:31.127000 139972170110784 torch/distributed/run.py:757] 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.
20
+ W0702 14:15:31.127000 139972170110784 torch/distributed/run.py:757] *****************************************
21
+ W0702 14:15:31.128000 140537828120384 torch/distributed/run.py:757]
22
+ W0702 14:15:31.128000 140537828120384 torch/distributed/run.py:757] *****************************************
23
+ W0702 14:15:31.128000 140537828120384 torch/distributed/run.py:757] 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.
24
+ W0702 14:15:31.128000 140537828120384 torch/distributed/run.py:757] *****************************************
25
+ [default0]:07/02/2024 14:15:49 [WARNING|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Vocab Size Padding] Padded vocab (size: 50257) with 3 dummy tokens (new size: 50260)
26
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Config:
27
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Config(general=GeneralArgs(project='bench_cluster',
28
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: run='%date_%jobid',
29
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: seed=42,
30
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: step=None,
31
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: consumed_train_samples=None,
32
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: benchmark_csv_path=None,
33
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: ignore_sanity_checks=True),
34
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: parallelism=ParallelismArgs(dp=4,
35
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pp=1,
36
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tp=4,
37
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7f11253ac910>,
38
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
39
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tp_linear_async_communication=False,
40
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: expert_parallel_size=1),
41
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
42
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: eos_token_id=2,
43
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hidden_act='silu',
44
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hidden_size=2048,
45
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: initializer_range=0.02,
46
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: intermediate_size=4096,
47
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: is_llama_config=True,
48
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: max_position_embeddings=4096,
49
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_attention_heads=32,
50
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_hidden_layers=24,
51
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_key_value_heads=32,
52
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pad_token_id=None,
53
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pretraining_tp=1,
54
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rms_norm_eps=1e-05,
55
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rope_scaling=None,
56
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rope_theta=10000.0,
57
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tie_word_embeddings=True,
58
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: use_cache=True,
59
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: vocab_size=50260),
60
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: init_method=RandomInit(std=0.025),
61
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: dtype=torch.bfloat16,
62
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: make_vocab_size_divisible_by=1,
63
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: ddp_bucket_cap_mb=25),
64
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
65
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tokenizer_revision=None,
66
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tokenizer_max_length=None),
67
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
68
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: checkpoint_interval=100000,
69
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: save_initial_state=False,
70
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: resume_checkpoint_path=None,
71
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: checkpoints_path_is_shared_file_system=False),
72
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: logging=LoggingArgs(log_level='info',
73
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: log_level_replica='info',
74
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration_step_info_interval=1),
75
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tokens=TokensArgs(sequence_length=4096,
76
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: train_steps=20,
77
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: micro_batch_size=16,
78
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: batch_accumulation_per_replica=16,
79
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: val_check_interval=-1,
80
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: limit_val_batches=0,
81
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: limit_test_batches=0),
82
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
83
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: adam_beta1=0.9,
84
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: adam_beta2=0.95,
85
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: torch_adam_is_fused=True,
86
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: name='adamW'),
87
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: zero_stage=1,
88
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: weight_decay=0.01,
89
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: clip_grad=1.0,
90
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: accumulate_grad_in_fp32=True,
91
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
92
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lr_warmup_steps=1,
93
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lr_warmup_style='linear',
94
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lr_decay_style='linear',
95
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lr_decay_steps=19,
96
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lr_decay_starting_step=None,
97
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: min_decay_lr=1e-05)),
98
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: data_stages=[DatasetStageArgs(name='Training Stage',
99
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: start_training_step=1,
100
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
101
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hf_dataset_splits='train',
102
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hf_dataset_config_name=None,
103
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: dataset_processing_num_proc_per_process=64,
104
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: dataset_overwrite_cache=False,
105
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: text_column_name='text'),
106
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: seed=42,
107
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_loading_workers=32))],
108
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16')),
109
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: lighteval=None)
110
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Model Config:
111
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: LlamaConfig(bos_token_id=1,
112
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: eos_token_id=2,
113
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hidden_act='silu',
114
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: hidden_size=2048,
115
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: initializer_range=0.02,
116
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: intermediate_size=4096,
117
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: is_llama_config=True,
118
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: max_position_embeddings=4096,
119
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_attention_heads=32,
120
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_hidden_layers=24,
121
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: num_key_value_heads=32,
122
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pad_token_id=None,
123
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: pretraining_tp=1,
124
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rms_norm_eps=1e-05,
125
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rope_scaling=None,
126
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: rope_theta=10000.0,
127
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: tie_word_embeddings=True,
128
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: use_cache=True,
129
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: vocab_size=50260)
130
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Building model..
131
+ [default0]:07/02/2024 14:15:49 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Setting PP block ranks...
132
+ [default0]:07/02/2024 14:16:02 [INFO|DP=2|PP=0|TP=0|ip-26-0-163-147]: No checkpoint path provided.
133
+ [default1]:07/02/2024 14:16:02 [INFO|DP=2|PP=0|TP=1|ip-26-0-163-147]: No checkpoint path provided.
134
+ [default3]:07/02/2024 14:16:02 [INFO|DP=2|PP=0|TP=3|ip-26-0-163-147]: No checkpoint path provided.
135
+ [default2]:07/02/2024 14:16:02 [INFO|DP=2|PP=0|TP=2|ip-26-0-163-147]: No checkpoint path provided.
136
+ [default4]:07/02/2024 14:16:02 [INFO|DP=1|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided.
137
+ [default6]:07/02/2024 14:16:02 [INFO|DP=1|PP=0|TP=2|ip-26-0-161-178]: No checkpoint path provided.
138
+ [default4]:07/02/2024 14:16:02 [INFO|DP=3|PP=0|TP=0|ip-26-0-163-147]: No checkpoint path provided.
139
+ [default6]:07/02/2024 14:16:02 [INFO|DP=3|PP=0|TP=2|ip-26-0-163-147]: No checkpoint path provided.
140
+ [default5]:07/02/2024 14:16:02 [INFO|DP=3|PP=0|TP=1|ip-26-0-163-147]: No checkpoint path provided.
141
+ [default2]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=2|ip-26-0-161-178]: Local number of parameters: 277M (529.27MiB)
142
+ [default2]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=2|ip-26-0-161-178]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
143
+ [default2]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=2|ip-26-0-161-178]: No checkpoint path provided.
144
+ [default1]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=1|ip-26-0-161-178]: Local number of parameters: 277M (529.27MiB)
145
+ [default1]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=1|ip-26-0-161-178]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
146
+ [default1]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided.
147
+ [default0]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Total number of parameters: 1.11G (2117.09MiB)
148
+ [default0]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Local number of parameters: 277M (529.27MiB)
149
+ [default0]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
150
+ [default3]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=3|ip-26-0-161-178]: Local number of parameters: 277M (529.27MiB)
151
+ [default3]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=3|ip-26-0-161-178]: [After model building] Memory usage: 554.21MiB. Peak allocated: 606.24MiB Peak reserved: 608.00MiB
152
+ [default7]:07/02/2024 14:16:02 [INFO|DP=1|PP=0|TP=3|ip-26-0-161-178]: No checkpoint path provided.
153
+ [default0]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: No checkpoint path provided.
154
+ [default0]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Parametrizing model parameters using StandardParametrizator
155
+ [default5]:07/02/2024 14:16:02 [INFO|DP=1|PP=0|TP=1|ip-26-0-161-178]: No checkpoint path provided.
156
+ [default3]:07/02/2024 14:16:02 [INFO|DP=0|PP=0|TP=3|ip-26-0-161-178]: No checkpoint path provided.
157
+ [default7]:07/02/2024 14:16:02 [INFO|DP=3|PP=0|TP=3|ip-26-0-163-147]: No checkpoint path provided.
158
+ [default0]:07/02/2024 14:16:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Optimizer Building] Using LearningRateForSP as learning rate
159
+ [default0]:07/02/2024 14:16:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] Size of optimizer params per rank:
160
+ [default0]:07/02/2024 14:16:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 0 has 69.4M out of 277M (25.00%) params' optimizer states
161
+ [default0]:07/02/2024 14:16:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 1 has 69.4M out of 277M (25.00%) params' optimizer states
162
+ [default0]:07/02/2024 14:16:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 2 has 69.4M out of 277M (25.00%) params' optimizer states
163
+ [default0]:07/02/2024 14:16:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [ZeRO sharding] DP Rank 3 has 69.4M out of 277M (25.00%) params' optimizer states
164
+ [default0]:07/02/2024 14:16:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
165
+ [default0]:07/02/2024 14:16:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Using `datasets` library
166
+ [default0]:07/02/2024 14:16:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
167
+ [default0]:Repo card metadata block was not found. Setting CardData to empty.
168
+ [default0]:07/02/2024 14:16:06 [WARNING|DP=0|PP=0|TP=0|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
169
+ [default0]:07/02/2024 14:16:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Training Plan] There are 1 training stages
170
+ [default0]:07/02/2024 14:16:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Stage Training Stage] start from step 1
171
+ [default0]:07/02/2024 14:16:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]:
172
+ [default0]:07/02/2024 14:16:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: [Start training] datetime: 2024-07-02 14:16:07.269098 | mbs: 16 | grad_accum: 16 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
173
+ [default0]:07/02/2024 14:16:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
174
+ [default0]:07/02/2024 14:16:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 1877.40MiB. Peak allocated 1877.40MiB. Peak reserved: 1934.00MiB
175
+ [default5]:Repo card metadata block was not found. Setting CardData to empty.
176
+ [default4]:07/02/2024 14:16:07 [WARNING|DP=1|PP=0|TP=0|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
177
+ [default6]:07/02/2024 14:16:07 [WARNING|DP=1|PP=0|TP=2|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
178
+ [default4]:Repo card metadata block was not found. Setting CardData to empty.
179
+ [default5]:Repo card metadata block was not found. Setting CardData to empty.
180
+ [default3]:Repo card metadata block was not found. Setting CardData to empty.
181
+ [default0]:Repo card metadata block was not found. Setting CardData to empty.
182
+ [default4]:07/02/2024 14:16:07 [WARNING|DP=3|PP=0|TP=0|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
183
+ [default6]:07/02/2024 14:16:07 [WARNING|DP=3|PP=0|TP=2|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
184
+ [default0]:07/02/2024 14:16:07 [WARNING|DP=2|PP=0|TP=0|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
185
+ [default1]:Repo card metadata block was not found. Setting CardData to empty.
186
+ [default5]:07/02/2024 14:16:07 [WARNING|DP=3|PP=0|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
187
+ [default1]:07/02/2024 14:16:07 [WARNING|DP=2|PP=0|TP=1|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
188
+ [default4]:Repo card metadata block was not found. Setting CardData to empty.
189
+ [default2]:07/02/2024 14:16:07 [WARNING|DP=2|PP=0|TP=2|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
190
+ [default7]:Repo card metadata block was not found. Setting CardData to empty.
191
+ [default2]:07/02/2024 14:16:07 [WARNING|DP=0|PP=0|TP=2|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
192
+ [default5]:07/02/2024 14:16:07 [WARNING|DP=1|PP=0|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
193
+ [default3]:07/02/2024 14:16:07 [WARNING|DP=0|PP=0|TP=3|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
194
+ [default7]:07/02/2024 14:16:07 [WARNING|DP=3|PP=0|TP=3|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
195
+ [default2]:Repo card metadata block was not found. Setting CardData to empty.
196
+ [default2]:Repo card metadata block was not found. Setting CardData to empty.
197
+ [default6]:Repo card metadata block was not found. Setting CardData to empty.
198
+ [default6]:Repo card metadata block was not found. Setting CardData to empty.
199
+ [default3]:07/02/2024 14:16:07 [WARNING|DP=2|PP=0|TP=3|ip-26-0-163-147]: Repo card metadata block was not found. Setting CardData to empty.
200
+ [default1]:Repo card metadata block was not found. Setting CardData to empty.
201
+ [default1]:07/02/2024 14:16:07 [WARNING|DP=0|PP=0|TP=1|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
202
+ [default3]:Repo card metadata block was not found. Setting CardData to empty.
203
+ [default7]:Repo card metadata block was not found. Setting CardData to empty.
204
+ [default7]:07/02/2024 14:16:07 [WARNING|DP=1|PP=0|TP=3|ip-26-0-161-178]: Repo card metadata block was not found. Setting CardData to empty.
205
+ [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
206
+ [default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
207
+ [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
208
+ [default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
209
+ [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
210
+ [default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
211
+ [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
212
+ [default6]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
213
+ [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
214
+ [default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
215
+ [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
216
+ [default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
217
+ [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
218
+ [default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
219
+ [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
220
+ [default0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
221
+ [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
222
+ [default4]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
223
+ [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
224
+ [default5]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
225
+ [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
226
+ [default7]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
227
+ [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
228
+ [default6]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
229
+ [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
230
+ [default0]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
231
+ [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
232
+ [default1]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
233
+ [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
234
+ [default3]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
235
+ [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/autograd/graph.py:744: UserWarning: c10d::allreduce_: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at ../torch/csrc/autograd/autograd_not_implemented_fallback.cpp:63.)
236
+ [default2]: return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
237
+ [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
238
+ [default4]: warnings.warn(
239
+ [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
240
+ [default7]: warnings.warn(
241
+ [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
242
+ [default5]: warnings.warn(
243
+ [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
244
+ [default6]: warnings.warn(
245
+ [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
246
+ [default3]: warnings.warn(
247
+ [default0]:07/02/2024 14:16:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 1954.53MiB. Peak allocated 47013.00MiB. Peak reserved: 48732.00MiB
248
+ [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
249
+ [default1]: warnings.warn(
250
+ [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
251
+ [default2]: warnings.warn(
252
+ [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
253
+ [default0]: warnings.warn(
254
+ [default4]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
255
+ [default4]: warnings.warn(
256
+ [default5]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
257
+ [default5]: warnings.warn(
258
+ [default7]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
259
+ [default7]: warnings.warn(
260
+ [default6]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
261
+ [default6]: warnings.warn(
262
+ [default2]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
263
+ [default2]: warnings.warn(
264
+ [default0]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
265
+ [default0]: warnings.warn(
266
+ [default1]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
267
+ [default1]: warnings.warn(
268
+ [default3]:/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py:2261: UserWarning: torch.distributed.all_reduce_coalesced will be deprecated. If you must use it, please revisit our documentation later at https://pytorch.org/docs/master/distributed.html#collective-functions
269
+ [default3]: warnings.warn(
270
+ [default0]:07/02/2024 14:16:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 1 / 20 | consumed_tokens: 4.19M | elapsed_time_per_iteration_ms: 22.3K | tokens_per_sec: 188K | tokens_per_sec_per_gpu: 11.7K | global_batch_size: 1.02K | lm_loss: 11.4 | lr: 0.0001 | model_tflops_per_gpu: 107 | hardware_tflops_per_gpu: 107 | grad_norm: 20.6 | cuda_memory_allocated: 2.61G | cuda_max_memory_reserved: 51.1G | hd_total_memory_tb: 312G | hd_used_memory_tb: 69.1G | hd_free_memory_tb: 243G
271
+ [default0]:07/02/2024 14:16:29 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 3681.66MiB. Peak reserved: 48778.00MiB
272
+ [default0]:07/02/2024 14:16:38 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.80MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
273
+ [default0]:07/02/2024 14:16:39 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 2 / 20 | consumed_tokens: 8.39M | elapsed_time_per_iteration_ms: 10.4K | tokens_per_sec: 404K | tokens_per_sec_per_gpu: 25.3K | global_batch_size: 1.02K | lm_loss: 11.4 | lr: 9.53e-05 | model_tflops_per_gpu: 229 | hardware_tflops_per_gpu: 229 | grad_norm: 20.7 | cuda_memory_allocated: 2.61G | cuda_max_memory_reserved: 51.2G | hd_total_memory_tb: 312G | hd_used_memory_tb: 69.1G | hd_free_memory_tb: 243G
274
+ [default0]:07/02/2024 14:16:40 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 3681.67MiB. Peak reserved: 48782.00MiB
275
+ [default0]:07/02/2024 14:16:48 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.80MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
276
+ [default0]:07/02/2024 14:16:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 3 / 20 | consumed_tokens: 12.6M | elapsed_time_per_iteration_ms: 10.3K | tokens_per_sec: 408K | tokens_per_sec_per_gpu: 25.5K | global_batch_size: 1.02K | lm_loss: 11.6 | lr: 9.05e-05 | model_tflops_per_gpu: 232 | hardware_tflops_per_gpu: 232 | grad_norm: 195 | cuda_memory_allocated: 2.61G | cuda_max_memory_reserved: 51.2G | hd_total_memory_tb: 312G | hd_used_memory_tb: 69.1G | hd_free_memory_tb: 243G
277
+ [default0]:07/02/2024 14:16:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 3681.67MiB. Peak reserved: 48782.00MiB
278
+ [default0]:STAGE:2024-07-02 14:16:50 137134:137134 ActivityProfilerController.cpp:314] Completed Stage: Warm Up
279
+ [default0]:07/02/2024 14:16:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.80MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
280
+ [default0]:07/02/2024 14:17:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 4 / 20 | consumed_tokens: 16.8M | elapsed_time_per_iteration_ms: 10.3K | tokens_per_sec: 407K | tokens_per_sec_per_gpu: 25.4K | global_batch_size: 1.02K | lm_loss: 13.6 | lr: 8.58e-05 | model_tflops_per_gpu: 231 | hardware_tflops_per_gpu: 231 | grad_norm: 28.1 | cuda_memory_allocated: 2.61G | cuda_max_memory_reserved: 51.2G | hd_total_memory_tb: 312G | hd_used_memory_tb: 69.1G | hd_free_memory_tb: 243G
281
+ [default0]:07/02/2024 14:17:00 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 3681.67MiB. Peak reserved: 48782.00MiB
282
+ [default0]:07/02/2024 14:17:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 5 / 20 | consumed_tokens: 21M | elapsed_time_per_iteration_ms: 10.3K | tokens_per_sec: 407K | tokens_per_sec_per_gpu: 25.5K | global_batch_size: 1.02K | lm_loss: 12 | lr: 8.11e-05 | model_tflops_per_gpu: 231 | hardware_tflops_per_gpu: 231 | grad_norm: 48.8
283
+ [default0]:07/02/2024 14:17:10 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
284
+ [default0]:07/02/2024 14:17:21 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 6 / 20 | consumed_tokens: 25.2M | elapsed_time_per_iteration_ms: 10.4K | tokens_per_sec: 404K | tokens_per_sec_per_gpu: 25.2K | global_batch_size: 1.02K | lm_loss: 10.9 | lr: 7.63e-05 | model_tflops_per_gpu: 229 | hardware_tflops_per_gpu: 229 | grad_norm: 19.7
285
+ [default0]:STAGE:2024-07-02 14:17:30 137134:137134 ActivityProfilerController.cpp:320] Completed Stage: Collection
286
+ [default0]:STAGE:2024-07-02 14:17:30 137134:137134 ActivityProfilerController.cpp:324] Completed Stage: Post Processing
287
+ [default0]:07/02/2024 14:18:43 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
288
+ [default0]:07/02/2024 14:18:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 7 / 20 | consumed_tokens: 29.4M | elapsed_time_per_iteration_ms: 10.1K | tokens_per_sec: 414K | tokens_per_sec_per_gpu: 25.8K | global_batch_size: 1.02K | lm_loss: 10.4 | lr: 7.16e-05 | model_tflops_per_gpu: 235 | hardware_tflops_per_gpu: 235 | grad_norm: 8.64
289
+ [default0]:07/02/2024 14:18:53 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:19:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 8 / 20 | consumed_tokens: 33.6M | elapsed_time_per_iteration_ms: 10.2K | tokens_per_sec: 411K | tokens_per_sec_per_gpu: 25.7K | global_batch_size: 1.02K | lm_loss: 9.66 | lr: 6.68e-05 | model_tflops_per_gpu: 233 | hardware_tflops_per_gpu: 233 | grad_norm: 6.86
291
+ [default0]:07/02/2024 14:19:04 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:19:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 9 / 20 | consumed_tokens: 37.7M | elapsed_time_per_iteration_ms: 10.5K | tokens_per_sec: 400K | tokens_per_sec_per_gpu: 25K | global_batch_size: 1.02K | lm_loss: 11.2 | lr: 6.21e-05 | model_tflops_per_gpu: 227 | hardware_tflops_per_gpu: 227 | grad_norm: 52.7
293
+ [default0]:07/02/2024 14:19:14 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
294
+ [default0]:07/02/2024 14:19:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 10 / 20 | consumed_tokens: 41.9M | elapsed_time_per_iteration_ms: 10.4K | tokens_per_sec: 403K | tokens_per_sec_per_gpu: 25.2K | global_batch_size: 1.02K | lm_loss: 9.08 | lr: 5.74e-05 | model_tflops_per_gpu: 228 | hardware_tflops_per_gpu: 228 | grad_norm: 15.1
295
+ [default0]:07/02/2024 14:19:25 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:19:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 11 / 20 | consumed_tokens: 46.1M | elapsed_time_per_iteration_ms: 10.3K | tokens_per_sec: 409K | tokens_per_sec_per_gpu: 25.5K | global_batch_size: 1.02K | lm_loss: 8.54 | lr: 5.26e-05 | model_tflops_per_gpu: 232 | hardware_tflops_per_gpu: 232 | grad_norm: 6.78
297
+ [default0]:07/02/2024 14:19:35 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:19:45 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 12 / 20 | consumed_tokens: 50.3M | elapsed_time_per_iteration_ms: 10.4K | tokens_per_sec: 403K | tokens_per_sec_per_gpu: 25.2K | global_batch_size: 1.02K | lm_loss: 8.34 | lr: 4.79e-05 | model_tflops_per_gpu: 229 | hardware_tflops_per_gpu: 229 | grad_norm: 5.79
299
+ [default0]:07/02/2024 14:19:45 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:19:56 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 13 / 20 | consumed_tokens: 54.5M | elapsed_time_per_iteration_ms: 10.3K | tokens_per_sec: 408K | tokens_per_sec_per_gpu: 25.5K | global_batch_size: 1.02K | lm_loss: 8.12 | lr: 4.32e-05 | model_tflops_per_gpu: 232 | hardware_tflops_per_gpu: 232 | grad_norm: 5.58
301
+ [default0]:07/02/2024 14:19:56 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:20:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 14 / 20 | consumed_tokens: 58.7M | elapsed_time_per_iteration_ms: 10.2K | tokens_per_sec: 409K | tokens_per_sec_per_gpu: 25.6K | global_batch_size: 1.02K | lm_loss: 7.86 | lr: 3.84e-05 | model_tflops_per_gpu: 232 | hardware_tflops_per_gpu: 232 | grad_norm: 5.35
303
+ [default0]:07/02/2024 14:20:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:20:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 15 / 20 | consumed_tokens: 62.9M | elapsed_time_per_iteration_ms: 10.2K | tokens_per_sec: 410K | tokens_per_sec_per_gpu: 25.6K | global_batch_size: 1.02K | lm_loss: 7.65 | lr: 3.37e-05 | model_tflops_per_gpu: 232 | hardware_tflops_per_gpu: 232 | grad_norm: 4.87
305
+ [default0]:07/02/2024 14:20:16 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:20:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 16 / 20 | consumed_tokens: 67.1M | elapsed_time_per_iteration_ms: 10.3K | tokens_per_sec: 408K | tokens_per_sec_per_gpu: 25.5K | global_batch_size: 1.02K | lm_loss: 7.53 | lr: 2.89e-05 | model_tflops_per_gpu: 231 | hardware_tflops_per_gpu: 231 | grad_norm: 5.03
307
+ [default0]:07/02/2024 14:20:26 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:20:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 17 / 20 | consumed_tokens: 71.3M | elapsed_time_per_iteration_ms: 10.4K | tokens_per_sec: 402K | tokens_per_sec_per_gpu: 25.1K | global_batch_size: 1.02K | lm_loss: 7.46 | lr: 2.42e-05 | model_tflops_per_gpu: 228 | hardware_tflops_per_gpu: 228 | grad_norm: 5.58
309
+ [default0]:07/02/2024 14:20:37 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:20:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 18 / 20 | consumed_tokens: 75.5M | elapsed_time_per_iteration_ms: 10.5K | tokens_per_sec: 401K | tokens_per_sec_per_gpu: 25.1K | global_batch_size: 1.02K | lm_loss: 7.33 | lr: 1.95e-05 | model_tflops_per_gpu: 228 | hardware_tflops_per_gpu: 228 | grad_norm: 5.46
311
+ [default0]:07/02/2024 14:20:47 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:20:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 19 / 20 | consumed_tokens: 79.7M | elapsed_time_per_iteration_ms: 10.6K | tokens_per_sec: 395K | tokens_per_sec_per_gpu: 24.7K | global_batch_size: 1.02K | lm_loss: 7.19 | lr: 1.47e-05 | model_tflops_per_gpu: 224 | hardware_tflops_per_gpu: 224 | grad_norm: 3.17
313
+ [default0]:07/02/2024 14:20:58 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: Memory usage: 2490.79MiB. Peak allocated 47549.27MiB. Peak reserved: 48782.00MiB
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+ [default0]:07/02/2024 14:21:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-161-178]: iteration: 20 / 20 | consumed_tokens: 83.9M | elapsed_time_per_iteration_ms: 10.5K | tokens_per_sec: 400K | tokens_per_sec_per_gpu: 25K | global_batch_size: 1.02K | lm_loss: 7.14 | lr: 1e-05 | model_tflops_per_gpu: 227 | hardware_tflops_per_gpu: 227 | grad_norm: 3.61
315
+ W0702 14:21:27.717000 139966503290624 torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1252] The node 'ip-26-0-163-147.ec2.internal_96886_0' has failed to send a keep-alive heartbeat to the rendezvous 'none' due to an error of type RendezvousTimeoutError.
316
+ Saved 1 csv files over 1 completed logs
317
+ Processing file: /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/profiler/ip-26-0-161-178_137134.1719929907506010984.pt.trace.json
318
+ Results written to /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/16_GPUS/dp-4_tp-4_pp-1_mbz-16/profiler.csv
319
+ Consider using `hf_transfer` for faster uploads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.
320
+
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