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3outeille HF staff commited on Jul 3, 2024

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Upload llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128

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llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/bench.slurm +111 -0
llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/config.yaml +90 -0
llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/log.out +531 -0
llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/status.txt +1 -0

llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/bench.slurm ADDED Viewed

	@@ -0,0 +1,111 @@

+#!/bin/bash
+#SBATCH --job-name=bench_cluster
+#SBATCH --time=02:00:00
+#SBATCH --partition=hopper-prod
+#SBATCH --nodes=1
+#SBATCH --gres=gpu:8
+#SBATCH --qos=normal
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=96
+#SBATCH --exclusive
+#SBATCH --output=/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/log.out
+#SBATCH --error=/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/log.out
+# Function to update status based on squeue output
+update_status() {
+    job_id=$1
+    status_file=$2
+    # For unknown reasons, it doenst update status for pending. It only works for running
+    while true; do
+        job_status=$(squeue --job $job_id --noheader --format=%T)
+        echo "Job status: $job_status"
+        if [ -z "$job_status" ]; then
+            # Job has finished or is not found
+            break
+        elif [ "$job_status" = "RUNNING" ]; then
+            printf "running" > $status_file
+            break
+        fi
+        sleep 10
+    done
+}
+# Misc initializations.
+echo "========================"
+echo "START TIME: $(date)"
+source /fsx/ferdinandmom/miniforge3/etc/profile.d/conda.sh
+conda activate /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster
+echo python3 version = $(python3 --version)
+echo "========================"
+# Slurm stuff
+export HOSTNAMES=$(scontrol show hostnames "$SLURM_JOB_NODELIST")
+export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_PORT=$((1024 + RANDOM % 64511))
+export TMPDIR=/scratch
+export HF_DATASETS_CACHE="/admin/home/ferdinand_mom/.cache"
+export CUBLAS_WORKSPACE_CONFIG=":4096:8"
+export CUDA_DEVICE_MAX_CONNECTIONS="1"
+huggingface-cli login --token $HUGGINGFACE_TOKEN
+NANOTRON_REPO="/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron"
+CMD="$NANOTRON_REPO/run_train.py --config-file /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/config.yaml"
+LAUNCHER="torchrun \
+   --nproc_per_node 8 \
+   --nnodes 1 \
+   --rdzv_endpoint ${MASTER_ADDR}:${MASTER_PORT} \
+   --rdzv_backend c10d \
+   --max_restarts 0 \
+   --tee 3 \
+   --node_rank ${SLURM_PROCID}"
+# Checkout the bench_cluster branch
+cd $NANOTRON_REPO
+git checkout bench_cluster
+cd ..
+# Get the current job ID
+job_id=${SLURM_JOB_ID}
+# Update status to "pending" or "running" in the background
+update_status $job_id /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/status.txt &
+# Run the main command
+srun -u $LAUNCHER $CMD
+exit_status=$?
+# Update status based on the exit status of `srun`
+if [ $exit_status -eq 0 ]; then
+    printf "completed" > /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/status.txt
+else
+    if grep -q "OutOfMemoryError" /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/log.out; then
+        printf "oom" > /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/status.txt
+    elif grep -q " CUDA error: an illegal memory access" /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/log.out; then
+        printf "oom" > /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/status.txt
+    elif grep -q "Timeout at NCCL" /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/log.out; then
+        printf "timeout" > /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/status.txt
+    else
+        printf "fail" > /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/status.txt
+    fi
+fi
+# Run the report script if the job completed successfully
+if [ $exit_status -eq 0 ]; then
+    python /fsx/ferdinandmom/ferdinand-hf/bench_cluster/main.py report --inp_dir /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128 --is_logs
+    python /fsx/ferdinandmom/ferdinand-hf/bench_cluster/main.py report --inp_dir /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128 --is_profiler
+fi
+# Push to hub the folder using huggingface_cli
+huggingface-cli upload nanotron/bench_cluster /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128 llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128 --commit-message "Upload llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128"
+# Verify the upload
+if [ $? -eq 0 ]; then
+    echo "Uploading to Huggingface Hub successful"
+else
+    echo "Failed to upload to Huggingface Hub"
+fi

llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/config.yaml ADDED Viewed

	@@ -0,0 +1,90 @@

+general:
+  project: bench_cluster
+  seed: 42
+model:
+  ddp_bucket_cap_mb: 25
+  dtype: bfloat16
+  init_method:
+    std: 0.025
+  make_vocab_size_divisible_by: 1
+  model_config:
+    bos_token_id: 1
+    eos_token_id: 2
+    hidden_act: silu
+    hidden_size: 2048
+    initializer_range: 0.02
+    intermediate_size: 4096
+    is_llama_config: true
+    max_position_embeddings: 4096
+    num_attention_heads: 32
+    num_hidden_layers: 24
+    num_key_value_heads: 32
+    pad_token_id: null
+    pretraining_tp: 1
+    rms_norm_eps: 1.0e-05
+    rope_scaling: null
+    rope_theta: 10000.0
+    tie_word_embeddings: true
+    use_cache: true
+    vocab_size: 50257
+optimizer:
+  accumulate_grad_in_fp32: true
+  clip_grad: 1.0
+  learning_rate_scheduler:
+    learning_rate: 0.0001
+    lr_decay_style: linear
+    lr_warmup_style: linear
+    lr_warmup_steps: 1
+    min_decay_lr: 1.0e-05
+  optimizer_factory:
+    adam_beta1: 0.9
+    adam_beta2: 0.95
+    adam_eps: 1.0e-08
+    name: adamW
+    torch_adam_is_fused: true
+  weight_decay: 0.01
+  zero_stage: 1
+parallelism:
+  dp: 8
+  expert_parallel_size: 1
+  pp: 1
+  pp_engine: 1f1b
+  tp: 1
+  tp_linear_async_communication: false
+  tp_mode: REDUCE_SCATTER
+profiler:
+  profiler_export_path: /fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128
+tokenizer:
+  tokenizer_max_length: null
+  tokenizer_name_or_path: openai-community/gpt2
+  tokenizer_revision: null
+data_stages:
+- name: Training Stage
+  start_training_step: 1
+  data:
+    dataset:
+      dataset_overwrite_cache: false
+      dataset_processing_num_proc_per_process: 64
+      hf_dataset_config_name: null
+      hf_dataset_or_datasets: roneneldan/TinyStories
+      hf_dataset_splits: train
+      text_column_name: text
+    num_loading_workers: 0
+    seed: 42
+lighteval: null
+tokens:
+  train_steps: 20
+  val_check_interval: -1
+  batch_accumulation_per_replica: 1
+  limit_test_batches: 0
+  limit_val_batches: 0
+  micro_batch_size: 128
+  sequence_length: 4096
+logging:
+  iteration_step_info_interval: 1
+  log_level: info
+  log_level_replica: info
+checkpoints:
+  checkpoint_interval: 100000
+  checkpoints_path: /dev/null
+  resume_checkpoint_path: null

llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/log.out ADDED Viewed

	@@ -0,0 +1,531 @@

+========================
+START TIME: Wed Jul  3 20:50:21 UTC 2024
+python3 version = Python 3.10.14
+========================
+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.
+Token is valid (permission: write).
+Your token has been saved to /admin/home/ferdinand_mom/.cache/huggingface/token
+Login successful
+Already on 'bench_cluster'
+M	examples/config_tiny_llama.py
+M	examples/config_tiny_llama.yaml
+M	examples/train_tiny_llama.sh
+M	src/nanotron/models/llama.py
+M	src/nanotron/trainer.py
+Your branch is up to date with 'origin/bench_cluster'.
+Job status: RUNNING
+W0703 20:50:29.341000 140462812669760 torch/distributed/run.py:757]
+W0703 20:50:29.341000 140462812669760 torch/distributed/run.py:757] *****************************************
+W0703 20:50:29.341000 140462812669760 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.
+W0703 20:50:29.341000 140462812669760 torch/distributed/run.py:757] *****************************************
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: Config:
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: Config(general=GeneralArgs(project='bench_cluster',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                            run='%date_%jobid',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                            seed=42,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                            step=None,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                            consumed_train_samples=None,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                            benchmark_csv_path=None,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                            ignore_sanity_checks=True),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:        parallelism=ParallelismArgs(dp=8,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                    pp=1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                    tp=1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                    pp_engine=<nanotron.parallel.pipeline_parallel.engine.OneForwardOneBackwardPipelineEngine object at 0x7ff7a0888820>,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                    tp_mode=<TensorParallelLinearMode.REDUCE_SCATTER: 2>,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                    tp_linear_async_communication=False,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                    expert_parallel_size=1),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:        model=ModelArgs(model_config=LlamaConfig(bos_token_id=1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 eos_token_id=2,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 hidden_act='silu',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 hidden_size=2048,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 initializer_range=0.02,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 intermediate_size=4096,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 is_llama_config=True,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 max_position_embeddings=4096,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 num_attention_heads=32,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 num_hidden_layers=24,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 num_key_value_heads=32,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 pad_token_id=None,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 pretraining_tp=1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 rms_norm_eps=1e-05,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 rope_scaling=None,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 rope_theta=10000.0,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 tie_word_embeddings=True,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 use_cache=True,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                 vocab_size=50257),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                        init_method=RandomInit(std=0.025),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                        dtype=torch.bfloat16,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                        make_vocab_size_divisible_by=1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                        ddp_bucket_cap_mb=25),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:        tokenizer=TokenizerArgs(tokenizer_name_or_path='openai-community/gpt2',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                tokenizer_revision=None,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                tokenizer_max_length=None),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:        checkpoints=CheckpointsArgs(checkpoints_path=Path('/dev/null'),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                    checkpoint_interval=100000,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                    save_initial_state=False,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                    resume_checkpoint_path=None,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                    checkpoints_path_is_shared_file_system=False),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:        logging=LoggingArgs(log_level='info',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                            log_level_replica='info',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                            iteration_step_info_interval=1),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:        tokens=TokensArgs(sequence_length=4096,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                          train_steps=20,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                          micro_batch_size=128,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                          batch_accumulation_per_replica=1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                          val_check_interval=-1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                          limit_val_batches=0,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                          limit_test_batches=0),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:        optimizer=OptimizerArgs(optimizer_factory=AdamWOptimizerArgs(adam_eps=1e-08,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                     adam_beta1=0.9,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                     adam_beta2=0.95,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                     torch_adam_is_fused=True,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                     name='adamW'),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                zero_stage=1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                weight_decay=0.01,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                clip_grad=1.0,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                accumulate_grad_in_fp32=True,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                learning_rate_scheduler=LRSchedulerArgs(learning_rate=0.0001,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                        lr_warmup_steps=1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                        lr_warmup_style='linear',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                        lr_decay_style='linear',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                        lr_decay_steps=19,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                        lr_decay_starting_step=None,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                        min_decay_lr=1e-05)),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:        data_stages=[DatasetStageArgs(name='Training Stage',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                      start_training_step=1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                      data=DataArgs(dataset=PretrainDatasetsArgs(hf_dataset_or_datasets='roneneldan/TinyStories',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                                 hf_dataset_splits='train',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                                 hf_dataset_config_name=None,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                                 dataset_processing_num_proc_per_process=64,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                                 dataset_overwrite_cache=False,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                                                 text_column_name='text'),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                    seed=42,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:                                                    num_loading_workers=0))],
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:        profiler=ProfilerArgs(profiler_export_path=Path('/fsx/ferdinandmom/ferdinand-hf/bench_cluster/results/llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128')),
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:        lighteval=None)
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: Model Config:
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: LlamaConfig(bos_token_id=1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             eos_token_id=2,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             hidden_act='silu',
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             hidden_size=2048,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             initializer_range=0.02,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             intermediate_size=4096,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             is_llama_config=True,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             max_position_embeddings=4096,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             num_attention_heads=32,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             num_hidden_layers=24,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             num_key_value_heads=32,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             pad_token_id=None,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             pretraining_tp=1,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             rms_norm_eps=1e-05,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             rope_scaling=None,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             rope_theta=10000.0,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             tie_word_embeddings=True,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             use_cache=True,
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:             vocab_size=50257)
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: Building model..
+[default0]:07/03/2024 20:50:50 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: Setting PP block ranks...
+[default2]:07/03/2024 20:50:59 [INFO|DP=2|PP=0|TP=0|ip-26-0-174-36]: No checkpoint path provided.
+[default1]:07/03/2024 20:50:59 [INFO|DP=1|PP=0|TP=0|ip-26-0-174-36]: No checkpoint path provided.
+[default0]:07/03/2024 20:50:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: Total number of parameters: 1.11G (2116.51MiB)
+[default0]:07/03/2024 20:50:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: Local number of parameters: 1.11G (2116.51MiB)
+[default0]:07/03/2024 20:50:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [After model building] Memory usage: 2140.53MiB. Peak allocated: 2338.88MiB Peak reserved: 2392.00MiB
+[default0]:07/03/2024 20:50:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: No checkpoint path provided.
+[default0]:07/03/2024 20:50:59 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: Parametrizing model parameters using StandardParametrizator
+[default3]:07/03/2024 20:50:59 [INFO|DP=3|PP=0|TP=0|ip-26-0-174-36]: No checkpoint path provided.
+[default4]:07/03/2024 20:50:59 [INFO|DP=4|PP=0|TP=0|ip-26-0-174-36]: No checkpoint path provided.
+[default7]:07/03/2024 20:50:59 [INFO|DP=7|PP=0|TP=0|ip-26-0-174-36]: No checkpoint path provided.
+[default6]:07/03/2024 20:50:59 [INFO|DP=6|PP=0|TP=0|ip-26-0-174-36]: No checkpoint path provided.
+[default5]:07/03/2024 20:50:59 [INFO|DP=5|PP=0|TP=0|ip-26-0-174-36]: No checkpoint path provided.
+[default0]:07/03/2024 20:51:06 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [Optimizer Building] Using LearningRateForSP as learning rate
+[default0]:07/03/2024 20:51:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [ZeRO sharding] Size of optimizer params per rank:
+[default0]:07/03/2024 20:51:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [ZeRO sharding] DP Rank 0 has 139M out of 1.11G (12.50%) params' optimizer states
+[default0]:07/03/2024 20:51:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [ZeRO sharding] DP Rank 1 has 139M out of 1.11G (12.50%) params' optimizer states
+[default0]:07/03/2024 20:51:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [ZeRO sharding] DP Rank 2 has 139M out of 1.11G (12.50%) params' optimizer states
+[default0]:07/03/2024 20:51:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [ZeRO sharding] DP Rank 3 has 139M out of 1.11G (12.50%) params' optimizer states
+[default0]:07/03/2024 20:51:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [ZeRO sharding] DP Rank 4 has 139M out of 1.11G (12.50%) params' optimizer states
+[default0]:07/03/2024 20:51:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [ZeRO sharding] DP Rank 5 has 139M out of 1.11G (12.50%) params' optimizer states
+[default0]:07/03/2024 20:51:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [ZeRO sharding] DP Rank 6 has 139M out of 1.11G (12.50%) params' optimizer states
+[default0]:07/03/2024 20:51:07 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [ZeRO sharding] DP Rank 7 has 139M out of 1.11G (12.50%) params' optimizer states
+[default0]:07/03/2024 20:51:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [Training Plan] Stage Training Stage has 19 remaining training steps and has consumed 0 samples
+[default0]:07/03/2024 20:51:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: Using `datasets` library
+[default0]:07/03/2024 20:51:08 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: Loading tokenizer from openai-community/gpt2 and transformers/hf_hub versions ('4.41.2', '0.23.4')
+[default0]:07/03/2024 20:51:08 [WARNING|DP=0|PP=0|TP=0|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty.
+[default0]:Repo card metadata block was not found. Setting CardData to empty.
+[default0]:07/03/2024 20:51:11 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [Training Plan] There are 1 training stages
+[default0]:07/03/2024 20:51:11 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [Stage Training Stage] start from step 1
+[default0]:07/03/2024 20:51:11 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:
+[default0]:07/03/2024 20:51:11 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: [Start training] datetime: 2024-07-03 20:51:11.242128 | mbs: 128 | grad_accum: 1 | global_batch_size: 1024 | sequence_length: 4096 | train_steps: 20 | start_iteration_step: 0 | consumed_train_samples: 0
+[default0]:07/03/2024 20:51:11 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]: Resuming training from stage Training Stage, it has trained for 0 samples and has 19 remaining train steps
+[default0]:07/03/2024 20:51:11 [INFO|DP=0|PP=0|TP=0|ip-26-0-174-36]:  Memory usage: 6904.53MiB. Peak allocated 6904.53MiB. Peak reserved: 7156.00MiB
+[default3]:Repo card metadata block was not found. Setting CardData to empty.
+[default6]:Repo card metadata block was not found. Setting CardData to empty.
+[default1]:Repo card metadata block was not found. Setting CardData to empty.
+[default2]:Repo card metadata block was not found. Setting CardData to empty.
+[default7]:Repo card metadata block was not found. Setting CardData to empty.
+[default1]:07/03/2024 20:51:11 [WARNING|DP=1|PP=0|TP=0|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty.
+[default2]:07/03/2024 20:51:11 [WARNING|DP=2|PP=0|TP=0|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty.
+[default6]:07/03/2024 20:51:11 [WARNING|DP=6|PP=0|TP=0|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty.
+[default7]:07/03/2024 20:51:11 [WARNING|DP=7|PP=0|TP=0|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty.
+[default4]:07/03/2024 20:51:11 [WARNING|DP=4|PP=0|TP=0|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty.
+[default4]:Repo card metadata block was not found. Setting CardData to empty.
+[default3]:07/03/2024 20:51:11 [WARNING|DP=3|PP=0|TP=0|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty.
+[default5]:07/03/2024 20:51:11 [WARNING|DP=5|PP=0|TP=0|ip-26-0-174-36]: Repo card metadata block was not found. Setting CardData to empty.
+[default5]:Repo card metadata block was not found. Setting CardData to empty.
+[default4]:[rank4]: Traceback (most recent call last):
+[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
+[default4]:[rank4]:     trainer.train(dataloader)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
+[default4]:[rank4]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
+[default4]:[rank4]:     outputs = self.pipeline_engine.train_batch_iter(
+[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
+[default4]:[rank4]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
+[default4]:[rank4]:     output = model(**micro_batch)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default4]:[rank4]:     return forward_call(*args, **kwargs)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
+[default4]:[rank4]:     sharded_logits = self.model(
+[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default4]:[rank4]:     return forward_call(*args, **kwargs)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
+[default4]:[rank4]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
+[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
+[default4]:[rank4]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default4]:[rank4]:     return forward_call(*args, **kwargs)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
+[default4]:[rank4]:     output = self.pp_block(**new_kwargs)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default4]:[rank4]:     return forward_call(*args, **kwargs)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
+[default4]:[rank4]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default4]:[rank4]:     return self._call_impl(*args, **kwargs)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default4]:[rank4]:     return forward_call(*args, **kwargs)
+[default4]:[rank4]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward
+[default4]:[rank4]:     key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous()
+[default4]:[rank4]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 4.00 GiB. GPU  has a total capacity of 79.33 GiB of which 557.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 64.85 GiB is allocated by PyTorch, and 2.18 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[default0]:[rank0]: Traceback (most recent call last):
+[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
+[default0]:[rank0]:     trainer.train(dataloader)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
+[default0]:[rank0]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
+[default0]:[rank0]:     outputs = self.pipeline_engine.train_batch_iter(
+[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
+[default0]:[rank0]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
+[default0]:[rank0]:     output = model(**micro_batch)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default0]:[rank0]:     return forward_call(*args, **kwargs)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
+[default0]:[rank0]:     sharded_logits = self.model(
+[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default0]:[rank0]:     return forward_call(*args, **kwargs)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
+[default0]:[rank0]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
+[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
+[default0]:[rank0]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default0]:[rank0]:     return forward_call(*args, **kwargs)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
+[default0]:[rank0]:     output = self.pp_block(**new_kwargs)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default0]:[rank0]:     return forward_call(*args, **kwargs)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
+[default0]:[rank0]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default0]:[rank0]:     return self._call_impl(*args, **kwargs)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default0]:[rank0]:     return forward_call(*args, **kwargs)
+[default0]:[rank0]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward
+[default0]:[rank0]:     key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous()
+[default0]:[rank0]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 4.00 GiB. GPU
+[default1]:[rank1]: Traceback (most recent call last):
+[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
+[default1]:[rank1]:     trainer.train(dataloader)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
+[default1]:[rank1]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
+[default1]:[rank1]:     outputs = self.pipeline_engine.train_batch_iter(
+[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
+[default1]:[rank1]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
+[default1]:[rank1]:     output = model(**micro_batch)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default1]:[rank1]:     return forward_call(*args, **kwargs)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
+[default1]:[rank1]:     sharded_logits = self.model(
+[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default1]:[rank1]:     return forward_call(*args, **kwargs)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
+[default1]:[rank1]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
+[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
+[default1]:[rank1]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default1]:[rank1]:     return forward_call(*args, **kwargs)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
+[default1]:[rank1]:     output = self.pp_block(**new_kwargs)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default1]:[rank1]:     return forward_call(*args, **kwargs)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
+[default1]:[rank1]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default1]:[rank1]:     return self._call_impl(*args, **kwargs)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default1]:[rank1]:     return forward_call(*args, **kwargs)
+[default1]:[rank1]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward
+[default1]:[rank1]:     key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous()
+[default1]:[rank1]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 4.00 GiB. GPU  has a total capacity of 79.33 GiB of which 557.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 64.85 GiB is allocated by PyTorch, and 2.18 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[default3]:[rank3]: Traceback (most recent call last):
+[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
+[default3]:[rank3]:     trainer.train(dataloader)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
+[default3]:[rank3]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
+[default3]:[rank3]:     outputs = self.pipeline_engine.train_batch_iter(
+[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
+[default3]:[rank3]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
+[default3]:[rank3]:     output = model(**micro_batch)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default3]:[rank3]:     return forward_call(*args, **kwargs)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
+[default3]:[rank3]:     sharded_logits = self.model(
+[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default3]:[rank3]:     return forward_call(*args, **kwargs)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
+[default3]:[rank3]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
+[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
+[default3]:[rank3]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default3]:[rank3]:     return forward_call(*args, **kwargs)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
+[default3]:[rank3]:     output = self.pp_block(**new_kwargs)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default3]:[rank3]:     return forward_call(*args, **kwargs)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
+[default3]:[rank3]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default3]:[rank3]:     return self._call_impl(*args, **kwargs)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default3]:[rank3]:     return forward_call(*args, **kwargs)
+[default3]:[rank3]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward
+[default3]:[rank3]:     key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous()
+[default3]:[rank3]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 4.00 GiB. GPU  has a total capacity of 79.33 GiB of which 557.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 64.85 GiB is allocated by PyTorch, and 2.18 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[default5]:[rank5]: Traceback (most recent call last):
+[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
+[default5]:[rank5]:     trainer.train(dataloader)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
+[default5]:[rank5]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
+[default5]:[rank5]:     outputs = self.pipeline_engine.train_batch_iter(
+[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
+[default5]:[rank5]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
+[default5]:[rank5]:     output = model(**micro_batch)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default5]:[rank5]:     return forward_call(*args, **kwargs)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
+[default5]:[rank5]:     sharded_logits = self.model(
+[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default5]:[rank5]:     return forward_call(*args, **kwargs)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
+[default5]:[rank5]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
+[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
+[default5]:[rank5]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default5]:[rank5]:     return forward_call(*args, **kwargs)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
+[default5]:[rank5]:     output = self.pp_block(**new_kwargs)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default5]:[rank5]:     return forward_call(*args, **kwargs)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
+[default5]:[rank5]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default5]:[rank5]:     return self._call_impl(*args, **kwargs)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default5]:[rank5]:     return forward_call(*args, **kwargs)
+[default5]:[rank5]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward
+[default5]:[rank5]:     key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous()
+[default5]:[rank5]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 4.00 GiB. GPU  has a total capacity of 79.33 GiB of which 557.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 64.85 GiB is allocated by PyTorch, and 2.18 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+[default2]:[rank2]: Traceback (most recent call last):
+[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
+[default2]:[rank2]:     trainer.train(dataloader)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
+[default2]:[rank2]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
+[default2]:[rank2]:     outputs = self.pipeline_engine.train_batch_iter(
+[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
+[default2]:[rank2]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
+[default2]:[rank2]:     output = model(**micro_batch)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default2]:[rank2]:     return forward_call(*args, **kwargs)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
+[default2]:[rank2]:     sharded_logits = self.model(
+[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default2]:[rank2]:     return forward_call(*args, **kwargs)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
+[default2]:[rank2]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
+[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
+[default2]:[rank2]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default2]:[rank2]:     return forward_call(*args, **kwargs)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
+[default2]:[rank2]:     output = self.pp_block(**new_kwargs)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default2]:[rank2]:     return forward_call(*args, **kwargs)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
+[default2]:[rank2]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default2]:[rank2]:     return self._call_impl(*args, **kwargs)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default2]:[rank2]:     return forward_call(*args, **kwargs)
+[default2]:[rank2]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward
+[default2]:[rank2]:     key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous()
+[default2]:[rank2]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 4.00 GiB. GPU  has a total capacity of 79.33 GiB of which 557.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 64.85 GiB is allocated by PyTorch, and 2.18 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+W0703 20:51:19.664000 140462812669760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 214255 closing signal SIGTERM
+W0703 20:51:19.664000 140462812669760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 214256 closing signal SIGTERM
+W0703 20:51:19.664000 140462812669760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 214258 closing signal SIGTERM
+W0703 20:51:19.665000 140462812669760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 214259 closing signal SIGTERM
+W0703 20:51:19.665000 140462812669760 torch/distributed/elastic/multiprocessing/api.py:851] Sending process 214260 closing signal SIGTERM
+[default6]:[rank6]: Traceback (most recent call last):
+[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py", line 237, in <module>
+[default6]:[rank6]:     trainer.train(dataloader)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 429, in train
+[default6]:[rank6]:     outputs, loss_avg = self.training_step(dataloader=self.current_dataloader)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/trainer.py", line 462, in training_step
+[default6]:[rank6]:     outputs = self.pipeline_engine.train_batch_iter(
+[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 278, in train_batch_iter
+[default6]:[rank6]:     output = self.forward(context=context, state=state, micro_batch=micro_batch, model=model)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/engine.py", line 44, in forward
+[default6]:[rank6]:     output = model(**micro_batch)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default6]:[rank6]:     return forward_call(*args, **kwargs)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 891, in forward
+[default6]:[rank6]:     sharded_logits = self.model(
+[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default6]:[rank6]:     return forward_call(*args, **kwargs)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 764, in forward
+[default6]:[rank6]:     return self.forward_with_hidden_states(input_ids=input_ids, input_mask=input_mask)[0]
+[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 780, in forward_with_hidden_states
+[default6]:[rank6]:     hidden_encoder_states = encoder_block(**hidden_encoder_states)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default6]:[rank6]:     return forward_call(*args, **kwargs)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/parallel/pipeline_parallel/block.py", line 151, in forward
+[default6]:[rank6]:     output = self.pp_block(**new_kwargs)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default6]:[rank6]:     return forward_call(*args, **kwargs)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 631, in forward
+[default6]:[rank6]:     output = self.attn(hidden_states=hidden_states, sequence_mask=sequence_mask)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
+[default6]:[rank6]:     return self._call_impl(*args, **kwargs)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
+[default6]:[rank6]:     return forward_call(*args, **kwargs)
+[default6]:[rank6]:   File "/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/src/nanotron/models/llama.py", line 565, in forward
+[default6]:[rank6]:     key_value_states = key_value_states.permute(1, 2, 0, 3, 4).contiguous()
+[default6]:[rank6]: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 4.00 GiB. GPU  has a total capacity of 79.33 GiB of which 557.94 MiB is free. Including non-PyTorch memory, this process has 78.77 GiB memory in use. Of the allocated memory 64.85 GiB is allocated by PyTorch, and 2.18 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+E0703 20:51:21.188000 140462812669760 torch/distributed/elastic/multiprocessing/api.py:826] failed (exitcode: 1) local_rank: 0 (pid: 214253) of binary: /fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/python3.10
+Traceback (most recent call last):
+  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/bin/torchrun", line 8, in <module>
+    sys.exit(main())
+  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
+    return f(*args, **kwargs)
+  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 879, in main
+    run(args)
+  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/run.py", line 870, in run
+    elastic_launch(
+  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/fsx/ferdinandmom/miniforge3/envs/env-bench-cluster/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 263, in launch_agent
+    raise ChildFailedError(
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
+============================================================
+/fsx/ferdinandmom/ferdinand-hf/bench_cluster/nanotron/run_train.py FAILED
+------------------------------------------------------------
+Failures:
+[1]:
+  time      : 2024-07-03_20:51:19
+  host      : ip-26-0-174-36.ec2.internal
+  rank      : 1 (local_rank: 1)
+  exitcode  : 1 (pid: 214254)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+[2]:
+  time      : 2024-07-03_20:51:19
+  host      : ip-26-0-174-36.ec2.internal
+  rank      : 4 (local_rank: 4)
+  exitcode  : 1 (pid: 214257)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+------------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2024-07-03_20:51:19
+  host      : ip-26-0-174-36.ec2.internal
+  rank      : 0 (local_rank: 0)
+  exitcode  : 1 (pid: 214253)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+============================================================
+srun: error: ip-26-0-174-36: task 0: Exited with exit code 1
+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.

llama-1B/8_GPUS/dp-8_tp-1_pp-1_mbz-128/status.txt ADDED Viewed

	@@ -0,0 +1 @@


1	+ oom