[tab] /p/project1/envcomp/mihir/logs/train/qwen3-0.6b-sft-torch-crowd-code/qwen_8b_no_lora_bs_32_13034357.log (log)
#!/usr/bin/env bash

#SBATCH --nodes=64
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --mem=200GB
#SBATCH --account=envcomp
#SBATCH --nodelist=jwb[0700-0799]
#SBATCH --signal=USR1@60
#SBATCH --output=/p/project1/envcomp/mihir/logs/train/qwen3-0.6b-sft-torch-crowd-code/%x_%j.log
#SBATCH --error=/p/project1/envcomp/mihir/logs/train/qwen3-0.6b-sft-torch-crowd-code/%x_%j.log
#SBATCH --job-name=qwen_8b_no_lora_bs_32


trap 'echo "Job killed by signal"' USR1

cat $0

module load CUDA/12
module load cuDNN/9.5.0.50-CUDA-12
module load NCCL/default-CUDA-12
module load Clang/18.1.8

source .venv/bin/activate

srun sh slurm/jobs/mihir/juelich/crowd-pilot/1-wave-anta-santa/qwen_8b_no_lora_bs_32_ctx_parallel.shHAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
W1229 11:34:54.385000 1834365 torch/distributed/run.py:774] 
W1229 11:34:54.385000 1834365 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 1834365 torch/distributed/run.py:774] 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. 
W1229 11:34:54.385000 1834365 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 2039335 torch/distributed/run.py:774] 
W1229 11:34:54.385000 2039335 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 2039335 torch/distributed/run.py:774] 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. 
W1229 11:34:54.385000 2039335 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 3358348 torch/distributed/run.py:774] 
W1229 11:34:54.385000 3358348 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 3358348 torch/distributed/run.py:774] 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. 
W1229 11:34:54.385000 3358348 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 1871253 torch/distributed/run.py:774] 
W1229 11:34:54.385000 1871253 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 1871253 torch/distributed/run.py:774] 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. 
W1229 11:34:54.385000 1871253 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 2969284 torch/distributed/run.py:774] 
W1229 11:34:54.385000 2969284 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 2969284 torch/distributed/run.py:774] 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. 
W1229 11:34:54.385000 2969284 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1884110 torch/distributed/run.py:774] 
W1229 11:34:54.386000 1884110 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1884110 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 1884110 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2298516 torch/distributed/run.py:774] 
W1229 11:34:54.386000 2298516 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2298516 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 2298516 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1086274 torch/distributed/run.py:774] 
W1229 11:34:54.386000 1086274 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1086274 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 1086274 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4158530 torch/distributed/run.py:774] 
W1229 11:34:54.386000 4158530 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4158530 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 4158530 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4108767 torch/distributed/run.py:774] 
W1229 11:34:54.386000 4108767 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4108767 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 4108767 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 3875004 torch/distributed/run.py:774] 
W1229 11:34:54.386000 3875004 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 3875004 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 3875004 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 990580 torch/distributed/run.py:774] 
W1229 11:34:54.386000 990580 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 990580 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 990580 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1840514 torch/distributed/run.py:774] 
W1229 11:34:54.386000 1840514 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1840514 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 1840514 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4061004 torch/distributed/run.py:774] 
W1229 11:34:54.386000 4061004 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4061004 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 4061004 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2748290 torch/distributed/run.py:774] 
W1229 11:34:54.386000 2748290 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2748290 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 2748290 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4016919 torch/distributed/run.py:774] 
W1229 11:34:54.386000 4016919 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4016919 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 4016919 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2552375 torch/distributed/run.py:774] 
W1229 11:34:54.386000 2552375 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2552375 torch/distributed/run.py:774] 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. 
W1229 11:34:54.386000 2552375 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2368347 torch/distributed/run.py:774] 
W1229 11:34:54.387000 2368347 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2368347 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 2368347 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 842944 torch/distributed/run.py:774] 
W1229 11:34:54.387000 842944 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 842944 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 842944 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 132179 torch/distributed/run.py:774] 
W1229 11:34:54.387000 132179 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 132179 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 132179 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1852400 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1852400 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1852400 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 1852400 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1005532 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1005532 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1005532 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 1005532 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2095552 torch/distributed/run.py:774] 
W1229 11:34:54.387000 2095552 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2095552 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 2095552 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1095854 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1095854 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1095854 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 1095854 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3080522 torch/distributed/run.py:774] 
W1229 11:34:54.387000 3080522 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3080522 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 3080522 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 707844 torch/distributed/run.py:774] 
W1229 11:34:54.387000 707844 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 707844 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 707844 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3137823 torch/distributed/run.py:774] 
W1229 11:34:54.387000 3137823 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3137823 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 3137823 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2323410 torch/distributed/run.py:774] 
W1229 11:34:54.387000 2323410 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2323410 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 2323410 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1269189 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1269189 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1269189 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 1269189 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3620889 torch/distributed/run.py:774] 
W1229 11:34:54.387000 3620889 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3620889 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 3620889 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 91366 torch/distributed/run.py:774] 
W1229 11:34:54.387000 91366 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 91366 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 91366 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1548729 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1548729 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1548729 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 1548729 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2386424 torch/distributed/run.py:774] 
W1229 11:34:54.387000 2386424 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2386424 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 2386424 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1557718 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1557718 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1557718 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 1557718 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 4031052 torch/distributed/run.py:774] 
W1229 11:34:54.387000 4031052 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 4031052 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 4031052 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3783091 torch/distributed/run.py:774] 
W1229 11:34:54.387000 3783091 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3783091 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 3783091 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1178019 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1178019 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1178019 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 1178019 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 899066 torch/distributed/run.py:774] 
W1229 11:34:54.387000 899066 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 899066 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 899066 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 345543 torch/distributed/run.py:774] 
W1229 11:34:54.387000 345543 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 345543 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 345543 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2849074 torch/distributed/run.py:774] 
W1229 11:34:54.387000 2849074 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2849074 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 2849074 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3612398 torch/distributed/run.py:774] 
W1229 11:34:54.387000 3612398 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3612398 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 3612398 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 918553 torch/distributed/run.py:774] 
W1229 11:34:54.387000 918553 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 918553 torch/distributed/run.py:774] 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. 
W1229 11:34:54.387000 918553 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.388000 1757992 torch/distributed/run.py:774] 
W1229 11:34:54.388000 1757992 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.388000 1757992 torch/distributed/run.py:774] 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. 
W1229 11:34:54.388000 1757992 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.541379593 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.527260014 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.586277454 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.582444648 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.571390347 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.559538858 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.563675351 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.630557535 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.528052271 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.551887205 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.579562425 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.522469639 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.586596965 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.552159159 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.551018915 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.493031740 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.591701163 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.530094206 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.565537583 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.587545704 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.511421638 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.542838593 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.554129777 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.521274648 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.504918077 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.552602076 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.636914066 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.493396967 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.559351518 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.566958114 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.602183519 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.539473580 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.602500735 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.578982338 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.521941991 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.551296087 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.555244670 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.579882601 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.474139360 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.577089905 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.603936282 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.607322181 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.394000 1432841 torch/distributed/run.py:774] 
W1229 11:34:54.394000 1432841 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.394000 1432841 torch/distributed/run.py:774] 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. 
W1229 11:34:54.394000 1432841 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.396000 2628166 torch/distributed/run.py:774] 
W1229 11:34:54.396000 2628166 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.396000 2628166 torch/distributed/run.py:774] 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. 
W1229 11:34:54.396000 2628166 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 3540252 torch/distributed/run.py:774] 
W1229 11:34:54.397000 3540252 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 3540252 torch/distributed/run.py:774] 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. 
W1229 11:34:54.397000 3540252 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 2927834 torch/distributed/run.py:774] 
W1229 11:34:54.397000 2927834 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 2927834 torch/distributed/run.py:774] 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. 
W1229 11:34:54.397000 2927834 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 797134 torch/distributed/run.py:774] 
W1229 11:34:54.397000 797134 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 797134 torch/distributed/run.py:774] 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. 
W1229 11:34:54.397000 797134 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.635862783 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.400000 1554814 torch/distributed/run.py:774] 
W1229 11:34:54.400000 1554814 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.400000 1554814 torch/distributed/run.py:774] 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. 
W1229 11:34:54.400000 1554814 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.608740631 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.580115231 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.553827512 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.630624359 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.565226863 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.418000 1833893 torch/distributed/run.py:774] 
W1229 11:34:54.418000 1833893 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.418000 1833893 torch/distributed/run.py:774] 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. 
W1229 11:34:54.418000 1833893 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.421000 2561980 torch/distributed/run.py:774] 
W1229 11:34:54.421000 2561980 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.421000 2561980 torch/distributed/run.py:774] 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. 
W1229 11:34:54.421000 2561980 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.424000 974116 torch/distributed/run.py:774] 
W1229 11:34:54.424000 974116 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.424000 974116 torch/distributed/run.py:774] 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. 
W1229 11:34:54.424000 974116 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.583009494 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.425000 2410687 torch/distributed/run.py:774] 
W1229 11:34:54.425000 2410687 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.425000 2410687 torch/distributed/run.py:774] 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. 
W1229 11:34:54.425000 2410687 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.580310799 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.427000 4157487 torch/distributed/run.py:774] 
W1229 11:34:54.427000 4157487 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.427000 4157487 torch/distributed/run.py:774] 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. 
W1229 11:34:54.427000 4157487 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.581389857 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.559651052 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.563428384 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.436000 2767470 torch/distributed/run.py:774] 
W1229 11:34:54.436000 2767470 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.436000 2767470 torch/distributed/run.py:774] 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. 
W1229 11:34:54.436000 2767470 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.439000 3870112 torch/distributed/run.py:774] 
W1229 11:34:54.439000 3870112 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.439000 3870112 torch/distributed/run.py:774] 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. 
W1229 11:34:54.439000 3870112 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.549147387 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.629703578 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.632870242 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.459000 1490116 torch/distributed/run.py:774] 
W1229 11:34:54.459000 1490116 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.459000 1490116 torch/distributed/run.py:774] 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. 
W1229 11:34:54.459000 1490116 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.459000 646070 torch/distributed/run.py:774] 
W1229 11:34:54.459000 646070 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.459000 646070 torch/distributed/run.py:774] 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. 
W1229 11:34:54.459000 646070 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.460000 1459219 torch/distributed/run.py:774] 
W1229 11:34:54.460000 1459219 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.460000 1459219 torch/distributed/run.py:774] 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. 
W1229 11:34:54.460000 1459219 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.572513589 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.652650896 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.657995004 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.485000 1041813 torch/distributed/run.py:774] 
W1229 11:34:54.485000 1041813 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.485000 1041813 torch/distributed/run.py:774] 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. 
W1229 11:34:54.485000 1041813 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.713181214 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.492000 99787 torch/distributed/run.py:774] 
W1229 11:34:54.492000 99787 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.492000 99787 torch/distributed/run.py:774] 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. 
W1229 11:34:54.492000 99787 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.496000 2478330 torch/distributed/run.py:774] 
W1229 11:34:54.496000 2478330 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.496000 2478330 torch/distributed/run.py:774] 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. 
W1229 11:34:54.496000 2478330 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.669494269 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.675272944 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.551000 2540709 torch/distributed/run.py:774] 
W1229 11:34:54.551000 2540709 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.551000 2540709 torch/distributed/run.py:774] 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. 
W1229 11:34:54.551000 2540709 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.652256814 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.667000 3797500 torch/distributed/run.py:774] 
W1229 11:34:54.667000 3797500 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.667000 3797500 torch/distributed/run.py:774] 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. 
W1229 11:34:54.667000 3797500 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.848902604 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[W1229 11:35:24.484760444 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.485805703 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.489251192 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.459992538 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.489942888 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.460937653 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.492088956 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.492731191 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.464859118 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.465588133 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.522249987 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.523162015 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.468057438 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.468808373 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.498549100 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.451139770 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.525375784 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.499406467 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.451971335 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.539019356 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.526103771 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.500251683 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.539886275 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.554018360 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.554258851 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.554840732 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.500944238 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.554995003 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.529571821 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530274123 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.533859667 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530525984 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530805405 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.530860186 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.530541570 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.534791237 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.455949998 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531524088 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.524114303 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.456584091 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.456610331 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531927390 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.457330155 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.525140933 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.533045885 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.533380046 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.555856448 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.475174321 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.533835030 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.534203573 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.459268397 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.476405636 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.556757825 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.534568887 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.476045065 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.459791070 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.534941840 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.477461011 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.535353654 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.554668231 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.521633957 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.536141871 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.555208846 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.558775240 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.555926632 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.478202026 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.509199898 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.556201845 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.522977094 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.536048197 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.485792333 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.486036125 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.559991190 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.510282377 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.479447422 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.502307801 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.537124107 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.481229491 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.487042333 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.524320871 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.487145333 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.446420436 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.595746331 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.488083405 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.561561952 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.543208041 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.488526378 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.551501745 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.503828455 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.525565387 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.447837419 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.482782810 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.552034480 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.490636184 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.453695613 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530286379 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.489512475 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544393833 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.562847591 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.489722116 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.491053839 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.544615405 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.552709667 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.460355180 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.460361440 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.597326137 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.597639132 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.460666223 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.454761569 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.589791813 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531664718 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.455097041 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.492066699 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.553651866 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.598729900 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.545720566 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.492256181 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.490436119 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.461587772 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.504924222 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.461777613 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.554196501 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.462154737 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.491599938 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.456084876 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.506211242 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.436233759 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.591351264 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.506233851 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.555178931 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.506343262 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542437713 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.491867859 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.516252870 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.492910547 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.437545324 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.507322109 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.534201093 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.548081179 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.507749082 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.543755828 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.517261423 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.508009843 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.508269755 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.541034732 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.495200423 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.495203783 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.541113413 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.571319756 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.535032228 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.549054849 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.517945396 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.453479048 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.496298525 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.509140231 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.509412652 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.525173178 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.496347186 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.572450899 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.517122226 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.517136236 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.494726361 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.542460842 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542463752 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.519145920 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.510518714 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.454564578 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.595246231 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.488845394 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.488844464 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.526586154 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.495885520 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.511414332 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.546577682 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.518441578 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.518444058 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.489107333 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.511712795 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544046652 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.522157693 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.522314574 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.555194850 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.479977818 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.596795961 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.490337409 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.490339409 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.574964316 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.528188940 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.480494013 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.548104150 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.513024166 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.490667008 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.548007121 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.528905463 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.571760675 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.545967235 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.598206331 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.539872039 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.524271596 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.524276506 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.572187825 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.529766227 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.576916091 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.482253309 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.482258259 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.557546669 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.525356988 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.525358618 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.464014159 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.599521700 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.539167595 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.550203560 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.530779251 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.573597264 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.459353670 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.524315863 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.544669373 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.524415894 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.548159409 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.522807388 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.526857873 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.465530970 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.551290498 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.526890733 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.600822719 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542538625 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.574706584 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.589902482 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.484310647 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.495673655 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.466326924 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.523905536 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.495787025 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.575210493 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.533401224 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.502193677 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.536594219 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.576711285 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.546678523 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.588834770 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.576452803 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.447772871 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.467797834 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.527257038 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.529275537 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.529280727 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.462510858 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542872135 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.612140538 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.497321116 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.577491554 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.577346312 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.529994275 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.527779359 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.527798699 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.530036995 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.548100607 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.518681138 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.603644008 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.551551282 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.448792873 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.497523057 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.543456849 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.526371015 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.468848084 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.518946650 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.545785735 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.607122131 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530439924 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.578454552 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.527315400 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.527378990 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.555595309 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.488027510 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.469785753 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.505474868 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.579527231 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.527406443 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.470007365 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.543344054 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.499426312 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.499427702 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.593981471 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.488368153 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.537103893 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.543758586 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.563828159 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.521766718 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.476925000 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.550189227 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.580549396 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.592466887 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.540620645 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.537924951 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564579586 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.564670136 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.592602358 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.451457073 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.532440947 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.466246501 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.541640110 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.541677230 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531398502 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.452123061 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.452145091 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.546751736 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.545573466 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.500917423 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.472042574 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.615740567 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.581074291 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.522202940 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.610109295 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.582020815 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.558315332 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.501240535 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.556211734 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.545008955 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.500663716 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530383845 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.533862175 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.501682517 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.522682756 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.549529099 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.552308218 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.556627737 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.610932992 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.530617968 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.546780012 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.545731195 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.501364280 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.559288955 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.559297595 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.524316116 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.525926453 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.583415242 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.547202944 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.526306236 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.579442938 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.540770011 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.567373148 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.492080456 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.525077368 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.531671496 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.579890472 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.527788647 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.542428918 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.525920772 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.614018808 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.535771027 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.562673368 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.562705948 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.571153078 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.571384750 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.620642137 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.543046471 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.599034247 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.527494302 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.550924754 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531943309 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530860967 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531495899 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.570837181 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.548297870 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.603255810 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.539235683 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531403702 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.572376093 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.510575335 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.606723300 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.606931451 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.556802551 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.535608793 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.581122621 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.516298686 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.516298716 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.559412251 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.559679533 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.468184334 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.494393167 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.582597989 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.482755317 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.581775453 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.581802404 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.609758439 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.549157600 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.559250149 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.612248426 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.557917364 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.557952195 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.560244060 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.560383041 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.562754453 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.575171532 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.538997729 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.598953188 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.627240237 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.518151765 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.517413119 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.561831150 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.573356840 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.539532623 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.563638638 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.564973794 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.565183916 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.574644369 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.574735780 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.490958240 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.491041891 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.560831476 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.519404199 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.570756227 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.601062049 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.597117792 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.597134932 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.575135672 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.563787855 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542121144 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.548951168 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.557465771 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.550806978 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.558448046 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.613075784 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.613077304 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.629415855 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.541555227 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.635147743 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.549101919 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542694486 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542695406 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542719156 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.577618430 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.577622050 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.577627030 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.577628370 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.558612318 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.520026675 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.521174212 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.521174322 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.615677962 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.615679802 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.541449717 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.541453897 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544244963 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544248483 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544252323 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544253203 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564095908 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564098468 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564105478 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.586221358 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.586227378 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.586260292 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.582042148 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.582043728 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.563222711 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.587167673 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.587198794 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.587336737 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.565371128 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.617142669 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564526595 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.631417501 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.588358265 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.569668763 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.568489713 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.569708693 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.578730057 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.495034138 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.495050388 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.604778760 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.578777637 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564791112 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544935873 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.566369445 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.545074694 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.566504166 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.567807950 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.591876600 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.503579194 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.588186615 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.594328423 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.637794644 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.506345673 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.596387984 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.596419844 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.594055651 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.550735794 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.550755995 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.601022639 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.596908273 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.512652924 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.512655474 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.512780204 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.554120617 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.554140727 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.558293656 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.558322477 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.602616397 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.520039451 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.560851931 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.520722096 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.560953282 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.528598047 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.589841787 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.591241079 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.594192424 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.598314959 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.598320939 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.599103855 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.603339401 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.609106750 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[Gloo] Rank 0 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 0] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 1 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 1] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 3 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 3] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 2 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 2] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 0] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=0
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 0] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 3] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=3
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 2] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=2
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 3] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 1] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=1
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 2] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 1] CP initialized via device mesh
[2025-12-29 11:35:26] wandb_utils.py:72 - W&B logs will be stored in: /p/scratch/envcomp/logs/ANTA_CLAUS
[Gloo] Rank 4 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 6 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 5 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 6] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 4] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 5] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 7 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 7] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 7] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=7
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 6] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=6
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 5] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=5
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 4] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=4
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 7] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 4] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 6] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 5] CP initialized via device mesh
[Gloo] Rank 10 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 10] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 8 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 9 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 8] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 9] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 11 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 11] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 9] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=1
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 8] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=0
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 10] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=2
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 8] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 9] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 10] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 11] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=3
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 11] CP initialized via device mesh
[Gloo] Rank 13 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 13] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 15 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 12 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 15] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 12] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 14 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 14] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 13] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=5
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 15] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=7
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 12] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=4
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 13] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 15] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 12] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 14] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=6
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 14] CP initialized via device mesh
[Gloo] Rank 16 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 18 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 16] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 18] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 19 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 19] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 17 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 17] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 19] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=3
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 16] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=0
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 18] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=2
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 18] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 19] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 16] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 17] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=1
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 17] CP initialized via device mesh
[Gloo] Rank 21 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 21] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 22 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 20 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 23 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 22] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 20] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 23] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 23] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=7
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 20] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=4
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 21] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=5
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 22] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=6
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 23] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 21] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 20] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 22] CP initialized via device mesh
[Gloo] Rank 24 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 24] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 27 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 27] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 26 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 25 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 26] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 25] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 27] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=3
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 27] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 26] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 25] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=1
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 26] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 25] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 24] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=0
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 24] CP initialized via device mesh
[Gloo] Rank 28 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 28] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 29 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 30 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 29] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 30] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 28] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=4
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 29] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=5
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 28] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 30] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=6
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 29] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 30] CP initialized via device mesh
[Gloo] Rank 31 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 31] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 31] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=7
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 31] CP initialized via device mesh
[Gloo] Rank 32 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 32] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 34 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 33 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 34] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 33] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 35 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 35] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 32] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=0
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 34] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 33] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=1
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 32] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 34] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 33] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 35] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=3
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 35] CP initialized via device mesh
[Gloo] Rank 37 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 36 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 37] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 39 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 38 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 36] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 39] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 38] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 38] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=6
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 37] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=5
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 36] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=4
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 39] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=7
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 37] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 38] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 36] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 39] CP initialized via device mesh
[Gloo] Rank 41 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 40 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 41] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 40] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 43 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 42 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 43] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 42] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 42] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 40] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=0
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 43] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=3
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 41] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=1
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 42] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 43] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 40] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 41] CP initialized via device mesh
[Gloo] Rank 44 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 47 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 45 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 44] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 45] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 47] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 46 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 46] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 46] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=6
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 44] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=4
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 45] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=5
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 47] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=7
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 46] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 44] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 45] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 47] CP initialized via device mesh
[Gloo] Rank 49 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 49] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 48 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 50 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 50] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 48] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 49] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=1
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 50] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 48] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=0
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 49] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 50] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 48] CP initialized via device mesh
[Gloo] Rank 51 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 51] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 51] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=3
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 51] CP initialized via device mesh
wandb: Tracking run with wandb version 0.23.1
wandb: W&B syncing is set to `offline` in this directory. Run `wandb online` or set WANDB_MODE=online to enable cloud syncing.
wandb: Run data is saved locally in /p/scratch/envcomp/logs/ANTA_CLAUS/wandb/offline-run-20251229_113526-fb070njd
[Gloo] Rank 53 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 55 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 52 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 53] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 54 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 55] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 52] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 54] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 52] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=4
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 54] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=6
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 52] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 54] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 55] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=7
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 53] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=5
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 55] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 53] CP initialized via device mesh
[Gloo] Rank 56 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 58 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 56] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 58] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 57 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 59 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 59] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 57] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 56] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 58] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 57] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 59] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=3
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 56] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 58] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 59] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 57] CP initialized via device mesh
[Gloo] Rank 61 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 61] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 63 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 62 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 63] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 62] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 60 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 60] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 63] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=7
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 60] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=4
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 61] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=5
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 62] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=6
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 60] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 63] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 61] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 62] CP initialized via device mesh
[Gloo] Rank 64 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 66 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 64] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 66] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 67 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 67] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 65 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 65] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 64] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 67] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=3
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 66] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 65] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=1
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 67] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 64] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 66] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 65] CP initialized via device mesh
[Gloo] Rank 68 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 68] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 70 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 69 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 71 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 70] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 69] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 71] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 70] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=6
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 69] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=5
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 71] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=7
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 70] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 69] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 68] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=4
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 71] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 68] CP initialized via device mesh
[Gloo] Rank 72 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 73 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 74 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 72] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 73] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 74] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 75 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 75] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 74] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 72] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 73] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=1
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 73] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 72] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 74] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 75] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=3
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 75] CP initialized via device mesh
[Gloo] Rank 76 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 76] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank [Gloo] Rank 79 is connected to 25578 peer ranks.  is connected to Expected number of connected peer ranks is : 255255 peer ranks. 
Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 79] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 78] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 77 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 77] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 79] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=7
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 78] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=6
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 76] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=4
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 79] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 78] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 76] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 77] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=5
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 77] CP initialized via device mesh
[Gloo] Rank 80 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 81 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 80] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 82 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 83 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 81] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 83] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 82] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 82] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 81] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=1
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 82] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 81] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 80] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 83] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=3
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 80] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 83] CP initialized via device mesh
[Gloo] Rank 84 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 85 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 86 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 84] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 87 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 85] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 86] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 87] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 85] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=5
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 87] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=7
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 84] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=4
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 86] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=6
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 85] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 87] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 84] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 86] CP initialized via device mesh
[Gloo] Rank 88 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 89 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 88] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 90 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 89] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 90] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 91 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 91] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 88] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 89] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 90] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 91] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=3
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 88] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 89] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 90] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 91] CP initialized via device mesh
[Gloo] Rank 92 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 92] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 94 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 95 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 93 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 95] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 94] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 93] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 94] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=6
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 93] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=5
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 92] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=4
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 95] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=7
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 94] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 93] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 95] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 92] CP initialized via device mesh
[Gloo] Rank 96 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 97 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 98 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 96] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 99 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 97] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 98] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 99] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 98] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 99] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 97] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 99] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 98] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 97] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 96] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 96] CP initialized via device mesh
[Gloo] Rank 100 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 101 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 103 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 102 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 100] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 101] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 103] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 102] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 103] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 100] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 102] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 101] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 103] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 100] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 102] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 101] CP initialized via device mesh
[Gloo] Rank 104 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 105 is connected to 255[Gloo] Rank  peer ranks. Expected number of connected peer ranks is : 106 is connected to 255255 peer ranks. Expected number of connected peer ranks is : 
255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 104] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 107 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 106] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 105] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 107] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 104] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 107] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 105] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 106] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 104] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 107] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 106] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 105] CP initialized via device mesh
[Gloo] Rank 108 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 109 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 110 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 108] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 111 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 109] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 110] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 111] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 111] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 110] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 108] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 109] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 108] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 111] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 110] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 109] CP initialized via device mesh
[Gloo] Rank 112 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 113 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 114 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 115 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 112] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 113] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 114] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 115] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 112] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 114] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 113] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 115] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 113] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 112] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 114] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 115] CP initialized via device mesh
[Gloo] Rank 116 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 118 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 119 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 116] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 118] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 119] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 117 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 117] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 119] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 118] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 116] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 119] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 118] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 116] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 117] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 117] CP initialized via device mesh
[Gloo] Rank 120 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 121 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 122 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 120] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 123 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 121] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 122] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 123] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 123] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 120] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 121] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 122] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 120] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 122] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 121] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 123] CP initialized via device mesh
[Gloo] Rank 124 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 125 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 126 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 127 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 124] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 125] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 126] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 127] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 125] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 126] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 124] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 125] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 126] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 124] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 127] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 127] CP initialized via device mesh
[Gloo] Rank 128 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 129 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 130 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 131 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 129] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 128] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 130] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 131] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 131] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 129] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 129] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 131] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 128] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 130] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 128] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 130] CP initialized via device mesh
[Gloo] Rank 132 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 133 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 134 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 135 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 132] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 133] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 134] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 135] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 133] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 134] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 132] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 135] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 133] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 134] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 132] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 135] CP initialized via device mesh
[Gloo] Rank 136 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 137 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 136] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 138 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 139 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 137] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 138] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 139] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 138] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 139] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 136] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 137] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 138] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 136] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 139] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 137] CP initialized via device mesh
[Gloo] Rank 140 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 141 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 142 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 140] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 141] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 142] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 143 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 143] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 141] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 143] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 142] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 140] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 143] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 142] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 140] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 141] CP initialized via device mesh
[Gloo] Rank 144 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 145 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 144] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 146 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 147 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 145] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 146] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 147] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 144] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 147] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 145] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 146] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 144] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 147] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 145] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 146] CP initialized via device mesh
[Gloo] Rank 148 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 149 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 150 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 148] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 150] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 149] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 151 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 151] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 148] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 150] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 149] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 148] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 150] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 149] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 151] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 151] CP initialized via device mesh
[Gloo] Rank 152 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 153 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 152] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 153] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 155 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 154 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 155] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 154] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 152] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 153] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 154] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 155] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 152] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 153] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 154] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 155] CP initialized via device mesh
[Gloo] Rank 156 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 157 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 156] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 159 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 157] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 159] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 158 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 158] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 159] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 156] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 157] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 158] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 157] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 159] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 156] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 158] CP initialized via device mesh
[Gloo] Rank 160 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 161 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 160] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 161] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 163 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 162 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 163] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 162] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 161] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 163] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 162] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 161] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 163] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 162] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 160] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 160] CP initialized via device mesh
[Gloo] Rank 164 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 165 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 167 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 166 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 164] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 165] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 167] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 166] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 164] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 167] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 166] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 165] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 164] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 167] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 166] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 165] CP initialized via device mesh
[Gloo] Rank 168 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 168] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 169 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 170 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 171 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 169] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 170] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 171] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 168] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 171] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 169] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 170] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 169] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 168] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 171] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 170] CP initialized via device mesh
[Gloo] Rank 172 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 173 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 174 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 175 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 173] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 172] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 174] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 175] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 173] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 174] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 172] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 175] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 173] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 174] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 172] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 175] CP initialized via device mesh
[Gloo] Rank 176 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 177 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 178 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 176] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 179 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 178] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 180 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 181 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 177] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 179] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 182 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 183 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 181] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 180] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 183] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 182] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 179] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 178] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 177] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 176] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 178] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 177] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 179] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 176] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 183] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 180] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 182] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 181] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 182] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 183] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 181] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 180] CP initialized via device mesh
[Gloo] Rank 184 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 185 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank [Gloo] Rank 187 is connected to 186255 is connected to  peer ranks. Expected number of connected peer ranks is : 255255 peer ranks. 
Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 184] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 185] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 186] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 187] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 185] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 184] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 186] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 187] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 186] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 185] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 184] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 187] CP initialized via device mesh
[Gloo] Rank 188 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 189 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 190 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 189] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 188] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 191 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 190] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 191] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 188] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 190] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 189] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 191] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 190] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 189] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 191] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 188] CP initialized via device mesh
[Gloo] Rank 192 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 193 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 194 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 192] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 195 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 193] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 194] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 195] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 192] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 194] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 193] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 195] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 192] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 193] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 194] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 195] CP initialized via device mesh
[Gloo] Rank 196 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 197 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 198 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 196] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 198] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 197] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 199 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 199] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 198] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 196] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 198] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 196] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 197] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 197] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 199] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 199] CP initialized via device mesh
[Gloo] Rank 204 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 205 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 206 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 207 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 205] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 204] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 206] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 207] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 207] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 205] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 206] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 207] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 205] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 206] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 204] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=4
[Gloo] Rank 208 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 204] CP initialized via device mesh
[Gloo] Rank 209 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 210 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 208] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 209] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 210] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 211 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 211] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 210] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 208] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 209] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 211] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 210] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 208] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 209] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 211] CP initialized via device mesh
[Gloo] Rank 212 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 213 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 215 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 214 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 212] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 213] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 215] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 214] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 215] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 212] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 213] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 215] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 212] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 214] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 213] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 214] CP initialized via device mesh
[Gloo] Rank [Gloo] Rank 201 is connected to 200255 peer ranks.  is connected to Expected number of connected peer ranks is : 255255
 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 202 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 203 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 201] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 200] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 202] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 203] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 200] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 201] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 202] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 203] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 201] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 200] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 202] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 203] CP initialized via device mesh
[Gloo] Rank 216 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 218 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 219 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 216] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 217 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 220 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 221 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 219] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 218] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 222 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 217] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 223 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 224 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 220] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 221] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 225 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 222] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 227 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 223] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 228 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 224] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 226 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 225] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 229 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 230 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 227] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 231 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 226] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 228] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 232 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 233 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 229] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 235 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 230] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 231] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 236 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 237 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 232] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 238 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 239 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 233] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 235] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 240 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 241 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 236] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 237] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 234 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 238] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 243 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 239] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 240] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 241] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 244 is connected to 255[Gloo] Rank  peer ranks. Expected number of connected peer ranks is : 245255 is connected to 255
 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 234] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 217] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 216] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 243] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 246 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 245] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 248 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 244] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 250 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 246] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 252 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 253 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 247 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 217] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 216] CP initialized via device mesh
[Gloo] Rank 255 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 249 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 251 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 218] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 247] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 252] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 248] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 250] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 255] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 253] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 242 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 223] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 222] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 221] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 220] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 251] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 249] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 218] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 224] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 227] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 226] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 225] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 242] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 221] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 222] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 223] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 220] CP initialized via device mesh
[Gloo] Rank 254 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 227] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 224] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 226] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 225] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 219] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 254] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 231] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 229] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 232] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 234] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 233] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 235] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 219] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 231] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 229] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 232] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 234] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 233] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 239] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 237] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 238] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 236] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 235] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 230] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 240] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 241] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 243] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 239] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 238] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 237] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 236] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 230] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 240] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 241] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 242] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 243] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 247] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 245] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 246] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 244] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 242] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 247] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 245] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 244] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 246] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 254] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 252] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 253] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 248] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 251] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 249] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 250] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 254] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 252] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 228] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 251] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 248] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 249] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 250] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 253] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 228] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 255] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 255] CP initialized via device mesh
wandb: Detected [openai] in use.
wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
wandb: For more information, check out the docs at: https://weave-docs.wandb.ai/
[2025-12-29 11:38:20] train_sft.py:218 - [Rank 57] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:20] train_sft.py:218 - [Rank 59] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:20] train_sft.py:218 - [Rank 68] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:20] train_sft.py:218 - [Rank 172] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:20] train_sft.py:218 - [Rank 115] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:20] train_sft.py:218 - [Rank 107] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:20] train_sft.py:218 - [Rank 137] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:20] train_sft.py:218 - [Rank 187] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 114] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 113] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 124] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 180] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 52] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 149] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 2] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 56] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 105] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 166] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 104] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 37] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 133] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 173] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 109] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 71] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 179] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 174] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 48] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 63] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 58] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 78] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 183] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 244] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 157] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 74] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 69] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 112] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 189] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 140] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 80] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 122] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 132] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 50] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 210] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 73] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 234] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
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Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 228] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 214] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 192] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 135] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 19] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 219] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 159] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 8] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 236] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 13] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 160] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 83] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 38] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 254] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 182] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 250] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 76] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 119] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 165] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 245] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 70] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 75] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 60] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 93] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 45] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 237] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 118] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 188] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 158] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 6] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 4] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 84] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 148] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 51] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 72] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 252] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 36] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 127] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 205] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 95] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 128] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 34] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 208] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 186] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 87] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 25] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 243] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 120] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 141] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 167] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 203] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 152] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 121] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 106] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 61] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 178] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 130] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 240] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 102] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 225] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 233] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 239] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 10] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 117] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 41] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 126] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 1] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 22] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 151] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 49] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 215] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 131] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 255] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 155] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

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Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 82] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 94] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 181] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 200] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 171] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 177] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 212] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 190] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 232] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 81] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 123] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 134] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 16] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 44] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 153] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 99] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 226] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 62] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 253] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 201] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 143] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 54] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 31] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

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Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 185] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 89] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 66] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 5] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 156] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 218] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 116] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 175] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 125] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 150] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 217] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 144] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 242] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 85] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 79] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 191] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 110] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

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Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 184] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 136] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 86] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 249] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 11] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
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Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 176] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 15] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 229] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 196] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 39] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 9] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 142] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 0] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 238] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 211] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 55] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 235] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 154] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 227] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 129] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 248] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 230] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 67] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 96] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 241] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 20] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 220] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 162] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
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Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 40] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 224] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 223] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 145] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 21] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

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Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 46] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 139] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 64] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

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Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 35] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 209] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 204] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 101] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 138] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 98] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 213] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 26] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 24] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 202] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 164] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 90] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 161] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 169] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 92] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 77] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 168] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 97] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 221] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 216] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 246] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 170] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
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Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 91] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 27] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 17] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 207] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 103] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 251] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 30] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 231] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 147] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 247] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 222] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 32] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 193] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 198] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 3] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 195] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 29] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 108] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 14] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 12] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 33] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 43] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 23] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 65] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 47] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 100] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 28] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 163] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:28] train_sft.py:218 - [Rank 111] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:28] train_sft.py:218 - [Rank 88] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:28] train_sft.py:218 - [Rank 42] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:28] train_sft.py:218 - [Rank 194] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:28] train_sft.py:218 - [Rank 206] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:28] train_sft.py:218 - [Rank 7] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:28] train_sft.py:218 - [Rank 199] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:28] train_sft.py:218 - [Rank 146] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:28] train_sft.py:218 - [Rank 18] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:28] train_sft.py:218 - [Rank 197] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:31,  7.78s/it]
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Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:13,  3.27s/it][2025-12-29 11:38:29] train_sft.py:218 - [Rank 53] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

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[2025-12-29 11:39:06] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

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[2025-12-29 11:39:10] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
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[2025-12-29 11:39:25] train_sft.py:322 - [Rank 24] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 25] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 19] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 16] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 17] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 18] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 26] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 27] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 4] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 5] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 79] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 176] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 6] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 76] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 173] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 175] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 7] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 10] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 122] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 142] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 96] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 178] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 97] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 193] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 106] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 104] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 143] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 47] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 99] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 195] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 194] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 8] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 46] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 37] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 105] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 35] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 33] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 34] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 111] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 103] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 102] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 109] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 32] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 83] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 36] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 120] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 110] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 80] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 128] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 108] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 107] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 101] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 179] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 11] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 130] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 100] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 82] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 63] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 45] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 162] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 163] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 129] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 121] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 136] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 90] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 14] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 192] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 60] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 78] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 12] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 77] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 13] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 139] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 38] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 39] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 123] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 141] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 88] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 161] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 70] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 2] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 1] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 89] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 131] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 49] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 50] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 98] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 44] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 235] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 72] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 81] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 91] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 61] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 234] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 233] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 68] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 172] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 62] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 75] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 71] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 3] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 137] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 196] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 48] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 189] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 177] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 226] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 51] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 132] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 134] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 225] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 191] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 174] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 152] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 84] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 188] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 133] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 190] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 87] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 140] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 15] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 227] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 69] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 199] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 153] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 197] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 224] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 232] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 160] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 9] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 86] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 115] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 113] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 155] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 112] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 198] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 85] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 138] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 74] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 114] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 148] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 154] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 73] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 158] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 149] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 22] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 23] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 21] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 151] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 20] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 150] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 211] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 127] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 208] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 209] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 157] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 126] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 236] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 251] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 147] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 145] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 164] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 237] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 146] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 156] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 210] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 169] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 144] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 167] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 186] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 165] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 240] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 168] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 218] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 231] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 171] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 216] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 170] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 31] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 229] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 228] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 187] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 248] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 217] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 29] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 221] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 243] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 230] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 184] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 249] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 219] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 242] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 185] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 241] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 200] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 201] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 159] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 124] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 222] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 55] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 52] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 28] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 92] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 95] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 250] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 180] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 182] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 66] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 64] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 181] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 183] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 166] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 245] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 244] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 117] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 238] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 119] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 239] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 94] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 125] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 67] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 59] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 202] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 203] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 57] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 247] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 246] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 40] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 65] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 41] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 220] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 93] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 118] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 116] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 53] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 58] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 42] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 43] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 54] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 135] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 205] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 204] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 255] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 254] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 206] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 215] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 213] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 207] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 252] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 214] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 253] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 223] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 212] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 30] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 56] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:30] train_sft.py:322 - [Rank 0] Model initialized with FSDP
[2025-12-29 11:39:30] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:30] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 2] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 3] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 0] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 2] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 3] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 179] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 178] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 176] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 217] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 218] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 1] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 150] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 72] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 73] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 65] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 0] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 177] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 155] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 154] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 153] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 152] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 34] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 140] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 133] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 132] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 134] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 103] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 102] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 100] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 31] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 214] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 18] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 19] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 16] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 27] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 25] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 178] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 179] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 176] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 240] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 155] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 154] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 111] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 108] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 55] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 54] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 135] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 228] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 229] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 231] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 166] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 47] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 44] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 188] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 191] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 189] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 202] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 200] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 203] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 74] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 186] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 187] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 184] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 11] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 8] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 9] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 10] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 215] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 1] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 234] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 232] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 18] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 24] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 25] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 27] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 177] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 242] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 153] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 63] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 149] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 55] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 54] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 134] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 132] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 133] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 103] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 102] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 79] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 78] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 165] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 164] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 157] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 156] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 117] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 118] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 116] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 119] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 45] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 248] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 72] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 73] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 49] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 51] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 48] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 113] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 115] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 212] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 214] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 82] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 83] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 81] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 17] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 19] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 24] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 121] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 120] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 123] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 41] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 43] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 40] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 131] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 130] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 138] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 136] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 139] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 137] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 97] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 99] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 96] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 98] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 89] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 88] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 90] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 91] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 241] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 152] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 35] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 160] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 63] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 61] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 148] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 150] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 126] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 127] SFTTrainer initialized successfully
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[2025-12-29 11:39:30] train_sft.py:102 - [Rank 110] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 174] SFTTrainer initialized successfully
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[2025-12-29 11:39:30] train_sft.py:702 - [Rank 135] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 231] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 228] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 229] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 12] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 100] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 199] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 196] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 252] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 255] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 20] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 22] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 95] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 93] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 86] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 245] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 246] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 244] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 29] SFTTrainer initialized successfully
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[2025-12-29 11:39:30] train_sft.py:102 - [Rank 222] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 220] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 183] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 180] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 166] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 5] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 47] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 44] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 205] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 207] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 236] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 188] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 191] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 189] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 39] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 38] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 36] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 74] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 106] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 226] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 58] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 186] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 192] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 195] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 194] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 168] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 170] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 211] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 209] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 11] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 8] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 9] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 66] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 144] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 145] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 147] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 68] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 69] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 215] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 216] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 233] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 82] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 16] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 26] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 122] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 121] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 120] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 43] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 41] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 40] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 131] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 130] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 138] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 136] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 99] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 97] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 89] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 88] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 240] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 243] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 34] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 160] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 61] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 149] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 148] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 126] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 141] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 111] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 172] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 175] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 53] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 230] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 12] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 101] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 199] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 252] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 23] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 20] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 21] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 92] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 84] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 76] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 78] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 79] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 246] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 245] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 28] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 30] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 31] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 223] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 183] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 164] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 165] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 157] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 118] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 4] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 6] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 45] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 206] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 204] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 238] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 236] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 190] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 38] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 39] Starting training: rollout_id 0 -> 5000
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[2025-12-29 11:39:30] train_sft.py:102 - [Rank 251] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 75] SFTTrainer initialized successfully
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[2025-12-29 11:39:30] train_sft.py:702 - [Rank 49] Starting training: rollout_id 0 -> 5000
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[2025-12-29 11:39:30] train_sft.py:702 - [Rank 51] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 225] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 58] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 184] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 187] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 195] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 171] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 168] Starting training: rollout_id 0 -> 5000
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[2025-12-29 11:39:30] train_sft.py:102 - [Rank 208] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 10] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 64] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 67] SFTTrainer initialized successfully
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[2025-12-29 11:39:30] train_sft.py:102 - [Rank 70] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 212] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 217] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 234] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 83] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 81] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 17] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 26] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 123] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 42] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 128] SFTTrainer initialized successfully
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[2025-12-29 11:39:30] train_sft.py:702 - [Rank 137] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 139] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 96] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 91] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 90] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 242] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 32] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 162] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 161] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 163] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 60] SFTTrainer initialized successfully
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[2025-12-29 11:39:30] train_sft.py:102 - [Rank 125] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 140] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 108] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 174] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 52] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 230] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 14] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 13] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 101] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 198] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 253] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 22] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 95] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 86] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 77] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 247] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 29] Starting training: rollout_id 0 -> 5000
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[2025-12-29 11:39:30] train_sft.py:702 - [Rank 222] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 220] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 181] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 180] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 167] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 158] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 156] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 117] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 5] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 46] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 205] Starting training: rollout_id 0 -> 5000
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[2025-12-29 11:39:30] train_sft.py:102 - [Rank 239] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 190] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 36] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 37] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 200] Starting training: rollout_id 0 -> 5000
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[2025-12-29 11:39:30] train_sft.py:702 - [Rank 75] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 105] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 50] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 224] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 226] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 57] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 185] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 192] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 170] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 113] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 209] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 211] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 65] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 145] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 144] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 68] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 213] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 218] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 235] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 232] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 80] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 122] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 42] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 128] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 129] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 98] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 241] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 35] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 162] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 161] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 163] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 60] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 151] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 127] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 124] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 143] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 109] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 110] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 172] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 53] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 15] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 196] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 254] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 255] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 23] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 21] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 93] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 84] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 76] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 244] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 28] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 30] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 223] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 182] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 167] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 159] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 116] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 119] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 7] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 46] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 206] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 204] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 237] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 37] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 203] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 251] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 250] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 107] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 106] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 50] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 225] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 56] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 59] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 185] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 194] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 169] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 115] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 208] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 210] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 66] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 147] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 71] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 213] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 216] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 233] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 80] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 243] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 33] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 62] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 125] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 142] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 175] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 14] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 13] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 197] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 253] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 94] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 85] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 77] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 247] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 181] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 158] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 4] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 6] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 238] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 201] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 249] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 104] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 227] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 224] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 57] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 193] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 171] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 114] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 210] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 64] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 146] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 69] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 219] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 235] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 32] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 62] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 141] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 173] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 15] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 198] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 254] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 92] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 87] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 182] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 159] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 7] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 239] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 201] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 250] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 249] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 105] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 227] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 59] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 56] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 193] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 169] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 112] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 67] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 70] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 219] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 33] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 142] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 173] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 197] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 94] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 85] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 237] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 107] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 114] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 71] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 87] Starting training: rollout_id 0 -> 5000

actor_val:   0%|          | 0/100 [00:00<?, ?it/s][2025-12-29 11:39:30] train_sft.py:376 - SFT rollout sample: tokens_len=3986, response_length=3898

actor_val:   1%|          | 1/100 [00:06<10:57,  6.64s/it][2025-12-29 11:39:37] train_sft.py:376 - SFT rollout sample: tokens_len=3980, response_length=3892

actor_val:   2%|▏         | 2/100 [00:08<06:30,  3.98s/it][2025-12-29 11:39:39] train_sft.py:376 - SFT rollout sample: tokens_len=3557, response_length=1416

actor_val:   3%|▎         | 3/100 [00:10<04:39,  2.88s/it][2025-12-29 11:39:40] train_sft.py:376 - SFT rollout sample: tokens_len=2923, response_length=783

actor_val:   4%|▍         | 4/100 [00:11<03:48,  2.38s/it][2025-12-29 11:39:42] train_sft.py:376 - SFT rollout sample: tokens_len=3730, response_length=3391

actor_val:   5%|▌         | 5/100 [00:13<03:19,  2.10s/it][2025-12-29 11:39:43] train_sft.py:376 - SFT rollout sample: tokens_len=2890, response_length=2410

actor_val:   6%|▌         | 6/100 [00:15<03:00,  1.92s/it][2025-12-29 11:39:45] train_sft.py:376 - SFT rollout sample: tokens_len=3402, response_length=1261

actor_val:   7%|▋         | 7/100 [00:16<02:48,  1.81s/it][2025-12-29 11:39:47] train_sft.py:376 - SFT rollout sample: tokens_len=3054, response_length=913

actor_val:   8%|▊         | 8/100 [00:18<02:39,  1.74s/it][2025-12-29 11:39:48] train_sft.py:376 - SFT rollout sample: tokens_len=3319, response_length=2933

actor_val:   9%|▉         | 9/100 [00:19<02:32,  1.68s/it][2025-12-29 11:39:50] train_sft.py:376 - SFT rollout sample: tokens_len=3884, response_length=1743

actor_val:  10%|█         | 10/100 [00:21<02:28,  1.65s/it][2025-12-29 11:39:51] train_sft.py:376 - SFT rollout sample: tokens_len=3707, response_length=1566

actor_val:  11%|█         | 11/100 [00:22<02:24,  1.63s/it][2025-12-29 11:39:53] train_sft.py:376 - SFT rollout sample: tokens_len=3607, response_length=3519

actor_val:  12%|█▏        | 12/100 [00:24<02:21,  1.61s/it][2025-12-29 11:39:54] train_sft.py:376 - SFT rollout sample: tokens_len=1014, response_length=926

actor_val:  13%|█▎        | 13/100 [00:26<02:18,  1.59s/it][2025-12-29 11:39:56] train_sft.py:376 - SFT rollout sample: tokens_len=3994, response_length=3596

actor_val:  14%|█▍        | 14/100 [00:27<02:16,  1.59s/it][2025-12-29 11:39:58] train_sft.py:376 - SFT rollout sample: tokens_len=4056, response_length=3657

actor_val:  15%|█▌        | 15/100 [00:29<02:14,  1.59s/it][2025-12-29 11:39:59] train_sft.py:376 - SFT rollout sample: tokens_len=3301, response_length=2903

actor_val:  16%|█▌        | 16/100 [00:30<02:12,  1.58s/it][2025-12-29 11:40:01] train_sft.py:376 - SFT rollout sample: tokens_len=2155, response_length=2067

actor_val:  17%|█▋        | 17/100 [00:32<02:10,  1.57s/it][2025-12-29 11:40:02] train_sft.py:376 - SFT rollout sample: tokens_len=3078, response_length=937

actor_val:  18%|█▊        | 18/100 [00:33<02:08,  1.57s/it][2025-12-29 11:40:04] train_sft.py:376 - SFT rollout sample: tokens_len=4054, response_length=1913

actor_val:  19%|█▉        | 19/100 [00:35<02:07,  1.57s/it][2025-12-29 11:40:05] train_sft.py:376 - SFT rollout sample: tokens_len=2916, response_length=2495

actor_val:  20%|██        | 20/100 [00:37<02:05,  1.57s/it][2025-12-29 11:40:07] train_sft.py:376 - SFT rollout sample: tokens_len=2528, response_length=387

actor_val:  21%|██        | 21/100 [00:38<02:04,  1.57s/it][2025-12-29 11:40:09] train_sft.py:376 - SFT rollout sample: tokens_len=3350, response_length=1209

actor_val:  22%|██▏       | 22/100 [00:40<02:02,  1.57s/it][2025-12-29 11:40:10] train_sft.py:376 - SFT rollout sample: tokens_len=3561, response_length=1420

actor_val:  23%|██▎       | 23/100 [00:41<02:00,  1.56s/it][2025-12-29 11:40:12] train_sft.py:376 - SFT rollout sample: tokens_len=3004, response_length=2430

actor_val:  24%|██▍       | 24/100 [00:43<01:58,  1.56s/it][2025-12-29 11:40:13] train_sft.py:376 - SFT rollout sample: tokens_len=3020, response_length=2695

actor_val:  25%|██▌       | 25/100 [00:44<01:57,  1.57s/it][2025-12-29 11:40:15] train_sft.py:376 - SFT rollout sample: tokens_len=2910, response_length=2403

actor_val:  26%|██▌       | 26/100 [00:46<01:56,  1.57s/it][2025-12-29 11:40:16] train_sft.py:376 - SFT rollout sample: tokens_len=3019, response_length=2673

actor_val:  27%|██▋       | 27/100 [00:48<01:55,  1.58s/it][2025-12-29 11:40:18] train_sft.py:376 - SFT rollout sample: tokens_len=593, response_length=252

actor_val:  28%|██▊       | 28/100 [00:49<01:53,  1.58s/it][2025-12-29 11:40:20] train_sft.py:376 - SFT rollout sample: tokens_len=4052, response_length=1911

actor_val:  29%|██▉       | 29/100 [00:51<01:51,  1.57s/it][2025-12-29 11:40:21] train_sft.py:376 - SFT rollout sample: tokens_len=3837, response_length=1696

actor_val:  30%|███       | 30/100 [00:52<01:50,  1.57s/it][2025-12-29 11:40:23] train_sft.py:376 - SFT rollout sample: tokens_len=2890, response_length=2410

actor_val:  31%|███       | 31/100 [00:54<01:48,  1.57s/it][2025-12-29 11:40:24] train_sft.py:376 - SFT rollout sample: tokens_len=4006, response_length=3918

actor_val:  32%|███▏      | 32/100 [00:55<01:46,  1.57s/it][2025-12-29 11:40:26] train_sft.py:376 - SFT rollout sample: tokens_len=3663, response_length=3325

actor_val:  33%|███▎      | 33/100 [00:57<01:45,  1.57s/it][2025-12-29 11:40:27] train_sft.py:376 - SFT rollout sample: tokens_len=3526, response_length=1385

actor_val:  34%|███▍      | 34/100 [00:59<01:43,  1.56s/it][2025-12-29 11:40:29] train_sft.py:376 - SFT rollout sample: tokens_len=4012, response_length=3633

actor_val:  35%|███▌      | 35/100 [01:00<01:42,  1.58s/it][2025-12-29 11:40:31] train_sft.py:376 - SFT rollout sample: tokens_len=3639, response_length=1627

actor_val:  36%|███▌      | 36/100 [01:02<01:41,  1.58s/it][2025-12-29 11:40:32] train_sft.py:376 - SFT rollout sample: tokens_len=2895, response_length=2519

actor_val:  37%|███▋      | 37/100 [01:03<01:39,  1.59s/it][2025-12-29 11:40:34] train_sft.py:376 - SFT rollout sample: tokens_len=2722, response_length=2634

actor_val:  38%|███▊      | 38/100 [01:05<01:38,  1.58s/it][2025-12-29 11:40:35] train_sft.py:376 - SFT rollout sample: tokens_len=3473, response_length=1332

actor_val:  39%|███▉      | 39/100 [01:07<01:36,  1.58s/it][2025-12-29 11:40:37] train_sft.py:376 - SFT rollout sample: tokens_len=2812, response_length=2724

actor_val:  40%|████      | 40/100 [01:08<01:33,  1.57s/it][2025-12-29 11:40:38] train_sft.py:376 - SFT rollout sample: tokens_len=3496, response_length=2476

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[2025-12-29 11:42:13] train_sft.py:658 - step 0: {'val/loss': 3.210625}
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actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
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actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
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[2025-12-29 13:04:51] checkpoint.py:287 - [FSDP] Saved checkpoint to /p/scratch/envcomp/mihir/huggingface/shared_data/13034357/checkpoints/iter_0001000

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[2025-12-29 14:30:27] checkpoint.py:287 - [FSDP] Saved checkpoint to /p/scratch/envcomp/mihir/huggingface/shared_data/13034357/checkpoints/iter_0002000

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[2025-12-29 15:55:43] checkpoint.py:287 - [FSDP] Saved checkpoint to /p/scratch/envcomp/mihir/huggingface/shared_data/13034357/checkpoints/iter_0003000

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actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:24] train_sft.py:596 - step 3619: {'train/loss': 0.00697646290063858, 'train/grad_norm': 0.3388826549053192, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:50:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
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actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:29] train_sft.py:596 - step 3620: {'train/loss': 0.01579800248146057, 'train/grad_norm': 0.46754077076911926, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:50:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
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actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:33] train_sft.py:596 - step 3621: {'train/loss': 0.01470531802624464, 'train/grad_norm': 0.6702247858047485, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:50:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
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actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:38] train_sft.py:596 - step 3622: {'train/loss': 0.008645465597510338, 'train/grad_norm': 0.4181038737297058, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:50:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
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actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:43] train_sft.py:596 - step 3623: {'train/loss': 0.014800034463405609, 'train/grad_norm': 0.24632389843463898, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 16:50:43] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
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actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:48] train_sft.py:596 - step 3624: {'train/loss': 0.012061119079589844, 'train/grad_norm': 0.4810565412044525, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:50:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:50:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:53] train_sft.py:596 - step 3625: {'train/loss': 0.009576350450515747, 'train/grad_norm': 0.23217646777629852, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:50:53] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
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actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:57] train_sft.py:596 - step 3626: {'train/loss': 0.007322888821363449, 'train/grad_norm': 0.254390686750412, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:50:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
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actor_train:   0%|          | 0/1 [00:00<?, ?it/s]