2025/03/23 17:23:21 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed 2025/03/23 17:23:21 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] CUDA available: True MUSA available: False numpy_random_seed: 1239231278 GPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 4090 CUDA_HOME: /usr/local/cuda-12.4 NVCC: Cuda compilation tools, release 12.4, V12.4.99 GCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 PyTorch: 2.5.1+cu124 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201703 - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 12.4 - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90 - CuDNN 90.1 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.4, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.5.1, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, TorchVision: 0.20.1+cu124 OpenCV: 4.9.0 MMEngine: 0.10.7 Runtime environment: launcher: none randomness: {'seed': None, 'deterministic': False} cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None deterministic: False Distributed launcher: none Distributed training: False GPU number: 1 ------------------------------------------------------------ 2025/03/23 17:23:21 - mmengine - INFO - Config: accumulative_counts = 2 batch_size = 1 betas = ( 0.9, 0.999, ) custom_hooks = [ dict( tokenizer=dict( pretrained_model_name_or_path='/data/wangqun/models/InternVL2_5-2B', trust_remote_code=True, type='transformers.AutoTokenizer.from_pretrained'), type='xtuner.engine.hooks.DatasetInfoHook'), ] data_path = '/home/wangqun/data/layout_ocr_multi.json' dataloader_num_workers = 4 default_hooks = dict( checkpoint=dict( by_epoch=False, interval=1000, max_keep_ckpts=-1, save_optimizer=False, type='mmengine.hooks.CheckpointHook'), logger=dict( interval=10, log_metric_by_epoch=False, type='mmengine.hooks.LoggerHook'), param_scheduler=dict(type='mmengine.hooks.ParamSchedulerHook'), sampler_seed=dict(type='mmengine.hooks.DistSamplerSeedHook'), timer=dict(type='mmengine.hooks.IterTimerHook')) env_cfg = dict( cudnn_benchmark=False, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) image_folder = '/' launcher = 'none' llava_dataset = dict( data_paths='/home/wangqun/data/layout_ocr_multi.json', image_folders='/', max_length=8192, model_path='/data/wangqun/models/InternVL2_5-2B', template='xtuner.utils.PROMPT_TEMPLATE.internlm2_chat', type='xtuner.dataset.InternVL_V1_5_Dataset') load_from = None log_level = 'DEBUG' log_processor = dict(by_epoch=False) lr = 2e-05 max_epochs = 4 max_length = 8192 max_norm = 1 model = dict( freeze_llm=True, freeze_visual_encoder=True, llm_lora=dict( lora_alpha=256, lora_dropout=0.05, r=128, target_modules=None, task_type='CAUSAL_LM', type='peft.LoraConfig'), model_path='/data/wangqun/models/InternVL2_5-2B', quantization_llm=True, quantization_vit=False, type='xtuner.model.InternVL_V1_5') optim_type = 'torch.optim.AdamW' optim_wrapper = dict( optimizer=dict( betas=( 0.9, 0.999, ), lr=2e-05, type='torch.optim.AdamW', weight_decay=0.05), type='DeepSpeedOptimWrapper') param_scheduler = [ dict( begin=0, by_epoch=True, convert_to_iter_based=True, end=0.12, start_factor=1e-05, type='mmengine.optim.LinearLR'), dict( begin=0.12, by_epoch=True, convert_to_iter_based=True, end=4, eta_min=0.0, type='mmengine.optim.CosineAnnealingLR'), ] path = '/data/wangqun/models/InternVL2_5-2B' prompt_template = 'xtuner.utils.PROMPT_TEMPLATE.internlm2_chat' randomness = dict(deterministic=False, seed=None) resume = False runner_type = 'FlexibleRunner' save_steps = 1000 save_total_limit = -1 strategy = dict( config=dict( bf16=dict(enabled=True), fp16=dict(enabled=False, initial_scale_power=16), gradient_accumulation_steps='auto', gradient_clipping='auto', train_micro_batch_size_per_gpu='auto', zero_allow_untested_optimizer=True, zero_force_ds_cpu_optimizer=False, zero_optimization=dict(overlap_comm=True, stage=2)), exclude_frozen_parameters=True, gradient_accumulation_steps=2, gradient_clipping=1, sequence_parallel_size=1, train_micro_batch_size_per_gpu=1, type='xtuner.engine.DeepSpeedStrategy') tokenizer = dict( pretrained_model_name_or_path='/data/wangqun/models/InternVL2_5-2B', trust_remote_code=True, type='transformers.AutoTokenizer.from_pretrained') train_cfg = dict(max_epochs=4, type='xtuner.engine.runner.TrainLoop') train_dataloader = dict( batch_size=1, collate_fn=dict(type='xtuner.dataset.collate_fns.default_collate_fn'), dataset=dict( data_paths='/home/wangqun/data/layout_ocr_multi.json', image_folders='/', max_length=8192, model_path='/data/wangqun/models/InternVL2_5-2B', template='xtuner.utils.PROMPT_TEMPLATE.internlm2_chat', type='xtuner.dataset.InternVL_V1_5_Dataset'), num_workers=4, sampler=dict( length_property='modality_length', per_device_batch_size=2, type='xtuner.dataset.samplers.LengthGroupedSampler')) visualizer = dict( type='mmengine.visualization.Visualizer', vis_backends=[ dict(type='mmengine.visualization.TensorboardVisBackend'), ]) warmup_ratio = 0.03 weight_decay = 0.05 work_dir = '/home/wangqun/work_dirs/internvl_ft_run_14_filter' 2025/03/23 17:23:21 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend 2025/03/23 17:23:21 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer 2025/03/23 17:23:21 - mmengine - DEBUG - Attribute `_env_initialized` is not defined in or `._env_initialized is False, `_init_env` will be called and ._env_initialized will be set to True