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2024/08/25 18:15:54 - 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: 698415529
GPU 0,1: NVIDIA A100-SXM4-80GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.2, V12.2.140
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
PyTorch: 2.3.1+cu121
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201703
- Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v3.3.6 (Git Hash 86e6af5974177e513fd3fee58425e1063e7f1361)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX512
- CUDA Runtime 12.1
- 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 8.9.2
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, 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 -DUSE_FBGEMM -DUSE_QNNPACK -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-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -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, PERF_WITH_AVX512=1, TORCH_VERSION=2.3.1, 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: 0.18.1+cu121
OpenCV: 4.9.0
MMEngine: 0.10.3
Runtime environment:
cudnn_benchmark: False
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 698415529
deterministic: False
Distributed launcher: none
Distributed training: False
GPU number: 1
------------------------------------------------------------
2024/08/25 18:15:54 - mmengine - INFO - Config:
accumulative_counts = 4
batch_size = 4
betas = (
0.9,
0.999,
)
custom_hooks = [
dict(
tokenizer=dict(
pretrained_model_name_or_path='/root/models/InternVL2_2B',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.engine.hooks.DatasetInfoHook'),
]
data_path = '/root/data/screenshot_od/layout_ocr_multi.json'
data_root = '/root/data/extracted_images'
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 = '/root/data/extracted_imagesscreenshot_od/images'
launcher = 'none'
llava_dataset = dict(
data_paths='/root/data/screenshot_od/layout_ocr_multi.json',
image_folders='/root/data/extracted_imagesscreenshot_od/images',
max_length=8192,
model_path='/root/models/InternVL2_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='/root/models/InternVL2_2B',
quantization_llm=True,
quantization_vit=False,
type='xtuner.model.InternVL_V1_5')
optim_type = 'torch.optim.AdamW'
optim_wrapper = dict(
accumulative_counts=4,
clip_grad=dict(error_if_nonfinite=False, max_norm=1),
constructor='LearningRateDecayOptimWrapperConstructor',
dtype='float16',
loss_scale='dynamic',
optimizer=dict(
betas=(
0.9,
0.999,
),
lr=2e-05,
type='torch.optim.AdamW',
weight_decay=0.1),
paramwise_cfg=dict(layer_decay_rate=0.75),
type='mmengine.optim.AmpOptimWrapper')
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 = '/root/models/InternVL2_2B'
prompt_template = 'xtuner.utils.PROMPT_TEMPLATE.internlm2_chat'
randomness = dict(deterministic=False, seed=None)
resume = False
save_steps = 1000
save_total_limit = -1
tokenizer = dict(
pretrained_model_name_or_path='/root/models/InternVL2_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=4,
collate_fn=dict(type='xtuner.dataset.collate_fns.default_collate_fn'),
dataset=dict(
data_paths='/root/data/screenshot_od/layout_ocr_multi.json',
image_folders='/root/data/extracted_imagesscreenshot_od/images',
max_length=8192,
model_path='/root/models/InternVL2_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=16,
type='xtuner.dataset.samplers.LengthGroupedSampler'))
visualizer = dict(
type='mmengine.visualization.Visualizer',
vis_backends=[
dict(type='mmengine.visualization.TensorboardVisBackend'),
])
warmup_ratio = 0.03
weight_decay = 0.1
work_dir = '/root/wangqun/work_dirs/internvl_ft_run_11_filter'
2024/08/25 18:15:54 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
2024/08/25 18:15:54 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
2024/08/25 18:15:54 - mmengine - DEBUG - Attribute `_env_initialized` is not defined in <class 'mmengine.visualization.vis_backend.TensorboardVisBackend'> or `<class 'mmengine.visualization.vis_backend.TensorboardVisBackend'>._env_initialized is False, `_init_env` will be called and <class 'mmengine.visualization.vis_backend.TensorboardVisBackend'>._env_initialized will be set to True
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