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20240824_162237/vis_data/events.out.tfevents.1724487759.intern-studio-40019814.125195.0

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20240824_162554/vis_data/events.out.tfevents.1724487957.intern-studio-40019814.126964.0

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20240824_165409/20240824_165409.log

@@ -0,0 +1,563 @@
+2024/08/24 16:54:09 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/24 16:54:10 - 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: 488905104
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/24 16:54:10 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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'
+
+2024/08/24 16:54:10 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/24 16:54:10 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/24 16:54:10 - 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
+2024/08/24 16:54:12 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/24 16:54:12 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/24 16:54:12 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/24 16:54:12 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/24 16:54:12 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/24 16:54:12 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/24 16:54:12 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/24 16:54:12 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/24 16:54:13 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/24 16:54:13 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/24 16:54:13 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/24 16:54:13 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner
+2024/08/24 16:54:13 - mmengine - INFO - Starting to loading data and calc length
+2024/08/24 16:54:13 - mmengine - INFO - =======Starting to process /root/data/screenshot_od/layout_ocr_multi.json =======
+2024/08/24 16:54:19 - mmengine - INFO - =======total 4806 samples of /root/data/screenshot_od/layout_ocr_multi.json=======
+2024/08/24 16:54:19 - mmengine - INFO - end loading data and calc length
+2024/08/24 16:54:19 - mmengine - INFO - =======total 4806 samples=======
+2024/08/24 16:54:19 - mmengine - DEBUG - An `InternVL_V1_5_Dataset` instance is built from registry, and its implementation can be found in xtuner.dataset.internvl_dataset
+2024/08/24 16:54:19 - mmengine - INFO - LengthGroupedSampler is used.
+2024/08/24 16:54:19 - mmengine - INFO - LengthGroupedSampler construction is complete, and the selected attribute is modality_length
+2024/08/24 16:54:19 - mmengine - DEBUG - An `LengthGroupedSampler` instance is built from registry, and its implementation can be found in xtuner.dataset.samplers.length_grouped
+2024/08/24 16:54:19 - mmengine - WARNING - Dataset InternVL_V1_5_Dataset has no metainfo. ``dataset_meta`` in visualizer will be None.
+2024/08/24 16:54:20 - mmengine - DEBUG - An `TrainLoop` instance is built from registry, and its implementation can be found in xtuner.engine.runner.loops
+2024/08/24 16:54:20 - mmengine - INFO - Start to load InternVL_V1_5 model.
+2024/08/24 16:54:20 - mmengine - DEBUG - Get class `BaseDataPreprocessor` from "model" registry in "mmengine"
+2024/08/24 16:54:20 - mmengine - DEBUG - An `BaseDataPreprocessor` instance is built from registry, and its implementation can be found in mmengine.model.base_model.data_preprocessor
+2024/08/24 16:54:27 - mmengine - DEBUG - An `LoraConfig` instance is built from registry, and its implementation can be found in peft.tuners.lora.config
+2024/08/24 16:54:29 - mmengine - INFO - InternVL_V1_5(
+  (data_preprocessor): BaseDataPreprocessor()
+  (model): InternVLChatModel(
+    (vision_model): InternVisionModel(
+      (embeddings): InternVisionEmbeddings(
+        (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))
+      )
+      (encoder): InternVisionEncoder(
+        (layers): ModuleList(
+          (0-23): 24 x InternVisionEncoderLayer(
+            (attn): InternAttention(
+              (qkv): Linear(in_features=1024, out_features=3072, bias=True)
+              (attn_drop): Dropout(p=0.0, inplace=False)
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (proj): Linear(in_features=1024, out_features=1024, bias=True)
+            )
+            (mlp): InternMLP(
+              (act): GELUActivation()
+              (fc1): Linear(in_features=1024, out_features=4096, bias=True)
+              (fc2): Linear(in_features=4096, out_features=1024, bias=True)
+            )
+            (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (drop_path1): Identity()
+            (drop_path2): Identity()
+          )
+        )
+      )
+    )
+    (language_model): PeftModelForCausalLM(
+      (base_model): LoraModel(
+        (model): InternLM2ForCausalLM(
+          (model): InternLM2Model(
+            (tok_embeddings): Embedding(92553, 2048, padding_idx=2)
+            (layers): ModuleList(
+              (0-23): 24 x InternLM2DecoderLayer(
+                (attention): InternLM2Attention(
+                  (wqkv): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=4096, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=4096, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (wo): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (rotary_emb): InternLM2DynamicNTKScalingRotaryEmbedding()
+                )
+                (feed_forward): InternLM2MLP(
+                  (w1): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w3): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w2): lora.Linear(
+                    (base_layer): Linear4bit(in_features=8192, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=8192, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (act_fn): SiLU()
+                )
+                (attention_norm): InternLM2RMSNorm()
+                (ffn_norm): InternLM2RMSNorm()
+              )
+            )
+            (norm): InternLM2RMSNorm()
+          )
+          (output): lora.Linear(
+            (base_layer): Linear4bit(in_features=2048, out_features=92553, bias=False)
+            (lora_dropout): ModuleDict(
+              (default): Dropout(p=0.05, inplace=False)
+            )
+            (lora_A): ModuleDict(
+              (default): Linear(in_features=2048, out_features=128, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (default): Linear(in_features=128, out_features=92553, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+          )
+        )
+      )
+    )
+    (mlp1): Sequential(
+      (0): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): GELU(approximate='none')
+      (3): Linear(in_features=2048, out_features=2048, bias=True)
+    )
+  )
+)
+2024/08/24 16:54:29 - mmengine - INFO - InternVL_V1_5 construction is complete
+2024/08/24 16:54:29 - mmengine - DEBUG - An `InternVL_V1_5` instance is built from registry, and its implementation can be found in xtuner.model.internvl
+2024/08/24 16:54:29 - mmengine - DEBUG - Get class `DefaultOptimWrapperConstructor` from "optimizer wrapper constructor" registry in "mmengine"
+2024/08/24 16:54:29 - mmengine - DEBUG - An `DefaultOptimWrapperConstructor` instance is built from registry, and its implementation can be found in mmengine.optim.optimizer.default_constructor
+2024/08/24 16:54:29 - mmengine - DEBUG - An `AdamW` instance is built from registry, and its implementation can be found in torch.optim.adamw
+2024/08/24 16:54:29 - mmengine - DEBUG - Get class `DeepSpeedOptimWrapper` from "optim_wrapper" registry in "mmengine"
+2024/08/24 16:54:29 - mmengine - DEBUG - An `DeepSpeedOptimWrapper` instance is built from registry, and its implementation can be found in mmengine._strategy.deepspeed
+2024/08/24 16:54:30 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.LinearLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/24 16:54:30 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.CosineAnnealingLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/24 16:54:30 - mmengine - INFO - Num train samples 4806
+2024/08/24 16:54:30 - mmengine - INFO - train example:
+2024/08/24 16:54:31 - mmengine - INFO -  <s><|im_start|> system
+You are an AI assistant whose name is InternLM (书生·浦语).<|im_end|><|im_start|>user
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<IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> </img>  
+请从这张聊天截图中提取结构化信息<|im_end|><|im_start|> assistant
+{
+  "dialog_name": "<对方正在输入...",
+  "conversation": [
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不是",
+      "message_bbox": {
+        "min_x": 917,
+        "max_x": 989,
+        "min_y": 253,
+        "max_y": 289
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "在淘宝里",
+      "message_bbox": {
+        "min_x": 839,
+        "max_x": 987,
+        "min_y": 370,
+        "max_y": 404
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不能发微信",
+      "message_bbox": {
+        "min_x": 801,
+        "max_x": 989,
+        "min_y": 485,
+        "max_y": 521
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "两字",
+      "message_bbox": {
+        "min_x": 915,
+        "max_x": 988,
+        "min_y": 601,
+        "max_y": 637
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "微信",
+      "message_bbox": {
+        "min_x": 916,
+        "max_x": 990,
+        "min_y": 718,
+        "max_y": 753
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "①微信",
+      "message_bbox": {
+        "min_x": 845,
+        "max_x": 988,
+        "min_y": 833,
+        "max_y": 869
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    }
+  ]
+}<|im_end|>
+2024/08/24 16:54:31 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
+2024/08/24 16:54:31 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
+2024/08/24 16:54:31 - mmengine - INFO - Checkpoints will be saved to /root/wangqun/work_dirs/internvl_ft_run_11_filter.
+2024/08/24 16:55:45 - mmengine - INFO - Iter(train) [  10/4816]  lr: 1.2589e-06  eta: 9:56:50  time: 7.4512  data_time: 0.0203  memory: 59368  loss: 0.4242
+2024/08/24 16:56:44 - mmengine - INFO - Iter(train) [  20/4816]  lr: 2.6575e-06  eta: 8:53:54  time: 5.9076  data_time: 0.0227  memory: 30885  loss: 0.4527
+2024/08/24 16:57:41 - mmengine - INFO - Iter(train) [  30/4816]  lr: 4.0561e-06  eta: 8:26:32  time: 5.6923  data_time: 0.0235  memory: 30092  loss: 0.4705
+2024/08/24 16:58:37 - mmengine - INFO - Iter(train) [  40/4816]  lr: 5.4547e-06  eta: 8:09:22  time: 5.5408  data_time: 0.0214  memory: 28861  loss: 0.4857
+2024/08/24 16:59:31 - mmengine - INFO - Iter(train) [  50/4816]  lr: 6.8533e-06  eta: 7:56:36  time: 5.4090  data_time: 0.0227  memory: 29368  loss: 0.4412
+2024/08/24 17:00:25 - mmengine - INFO - Iter(train) [  60/4816]  lr: 8.2519e-06  eta: 7:47:59  time: 5.4226  data_time: 0.0221  memory: 27257  loss: 0.4419
+2024/08/24 17:01:17 - mmengine - INFO - Iter(train) [  70/4816]  lr: 9.6505e-06  eta: 7:38:36  time: 5.1610  data_time: 0.0249  memory: 27477  loss: 0.3942
+2024/08/24 17:02:08 - mmengine - INFO - Iter(train) [  80/4816]  lr: 1.1049e-05  eta: 7:31:41  time: 5.1956  data_time: 0.0229  memory: 27250  loss: 0.4353

20240824_165409/vis_data/events.out.tfevents.1724489652.intern-studio-40019814.137234.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c24b9692d6a90acddc134d72a9524367bdcf0d301dba89d26f34a822f86c38c9
+size 6864

20240824_170432/20240824_170432.log

@@ -0,0 +1,563 @@
+2024/08/24 17:04:33 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/24 17:04:33 - 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: 1374696625
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/24 17:04:33 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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'
+
+2024/08/24 17:04:33 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/24 17:04:33 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/24 17:04:33 - 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
+2024/08/24 17:04:35 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/24 17:04:35 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/24 17:04:35 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/24 17:04:35 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/24 17:04:35 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/24 17:04:35 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/24 17:04:35 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/24 17:04:35 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/24 17:04:35 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/24 17:04:35 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/24 17:04:35 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/24 17:04:35 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner
+2024/08/24 17:04:35 - mmengine - INFO - Starting to loading data and calc length
+2024/08/24 17:04:35 - mmengine - INFO - =======Starting to process /root/data/screenshot_od/layout_ocr_multi.json =======
+2024/08/24 17:04:42 - mmengine - INFO - =======total 4806 samples of /root/data/screenshot_od/layout_ocr_multi.json=======
+2024/08/24 17:04:42 - mmengine - INFO - end loading data and calc length
+2024/08/24 17:04:42 - mmengine - INFO - =======total 4806 samples=======
+2024/08/24 17:04:42 - mmengine - DEBUG - An `InternVL_V1_5_Dataset` instance is built from registry, and its implementation can be found in xtuner.dataset.internvl_dataset
+2024/08/24 17:04:42 - mmengine - INFO - LengthGroupedSampler is used.
+2024/08/24 17:04:42 - mmengine - INFO - LengthGroupedSampler construction is complete, and the selected attribute is modality_length
+2024/08/24 17:04:42 - mmengine - DEBUG - An `LengthGroupedSampler` instance is built from registry, and its implementation can be found in xtuner.dataset.samplers.length_grouped
+2024/08/24 17:04:42 - mmengine - WARNING - Dataset InternVL_V1_5_Dataset has no metainfo. ``dataset_meta`` in visualizer will be None.
+2024/08/24 17:04:42 - mmengine - DEBUG - An `TrainLoop` instance is built from registry, and its implementation can be found in xtuner.engine.runner.loops
+2024/08/24 17:04:42 - mmengine - INFO - Start to load InternVL_V1_5 model.
+2024/08/24 17:04:42 - mmengine - DEBUG - Get class `BaseDataPreprocessor` from "model" registry in "mmengine"
+2024/08/24 17:04:42 - mmengine - DEBUG - An `BaseDataPreprocessor` instance is built from registry, and its implementation can be found in mmengine.model.base_model.data_preprocessor
+2024/08/24 17:04:48 - mmengine - DEBUG - An `LoraConfig` instance is built from registry, and its implementation can be found in peft.tuners.lora.config
+2024/08/24 17:04:50 - mmengine - INFO - InternVL_V1_5(
+  (data_preprocessor): BaseDataPreprocessor()
+  (model): InternVLChatModel(
+    (vision_model): InternVisionModel(
+      (embeddings): InternVisionEmbeddings(
+        (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))
+      )
+      (encoder): InternVisionEncoder(
+        (layers): ModuleList(
+          (0-23): 24 x InternVisionEncoderLayer(
+            (attn): InternAttention(
+              (qkv): Linear(in_features=1024, out_features=3072, bias=True)
+              (attn_drop): Dropout(p=0.0, inplace=False)
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (proj): Linear(in_features=1024, out_features=1024, bias=True)
+            )
+            (mlp): InternMLP(
+              (act): GELUActivation()
+              (fc1): Linear(in_features=1024, out_features=4096, bias=True)
+              (fc2): Linear(in_features=4096, out_features=1024, bias=True)
+            )
+            (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (drop_path1): Identity()
+            (drop_path2): Identity()
+          )
+        )
+      )
+    )
+    (language_model): PeftModelForCausalLM(
+      (base_model): LoraModel(
+        (model): InternLM2ForCausalLM(
+          (model): InternLM2Model(
+            (tok_embeddings): Embedding(92553, 2048, padding_idx=2)
+            (layers): ModuleList(
+              (0-23): 24 x InternLM2DecoderLayer(
+                (attention): InternLM2Attention(
+                  (wqkv): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=4096, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=4096, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (wo): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (rotary_emb): InternLM2DynamicNTKScalingRotaryEmbedding()
+                )
+                (feed_forward): InternLM2MLP(
+                  (w1): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w3): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w2): lora.Linear(
+                    (base_layer): Linear4bit(in_features=8192, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=8192, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (act_fn): SiLU()
+                )
+                (attention_norm): InternLM2RMSNorm()
+                (ffn_norm): InternLM2RMSNorm()
+              )
+            )
+            (norm): InternLM2RMSNorm()
+          )
+          (output): lora.Linear(
+            (base_layer): Linear4bit(in_features=2048, out_features=92553, bias=False)
+            (lora_dropout): ModuleDict(
+              (default): Dropout(p=0.05, inplace=False)
+            )
+            (lora_A): ModuleDict(
+              (default): Linear(in_features=2048, out_features=128, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (default): Linear(in_features=128, out_features=92553, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+          )
+        )
+      )
+    )
+    (mlp1): Sequential(
+      (0): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): GELU(approximate='none')
+      (3): Linear(in_features=2048, out_features=2048, bias=True)
+    )
+  )
+)
+2024/08/24 17:04:50 - mmengine - INFO - InternVL_V1_5 construction is complete
+2024/08/24 17:04:50 - mmengine - DEBUG - An `InternVL_V1_5` instance is built from registry, and its implementation can be found in xtuner.model.internvl
+2024/08/24 17:04:50 - mmengine - DEBUG - Get class `DefaultOptimWrapperConstructor` from "optimizer wrapper constructor" registry in "mmengine"
+2024/08/24 17:04:50 - mmengine - DEBUG - An `DefaultOptimWrapperConstructor` instance is built from registry, and its implementation can be found in mmengine.optim.optimizer.default_constructor
+2024/08/24 17:04:50 - mmengine - DEBUG - An `AdamW` instance is built from registry, and its implementation can be found in torch.optim.adamw
+2024/08/24 17:04:50 - mmengine - DEBUG - Get class `DeepSpeedOptimWrapper` from "optim_wrapper" registry in "mmengine"
+2024/08/24 17:04:50 - mmengine - DEBUG - An `DeepSpeedOptimWrapper` instance is built from registry, and its implementation can be found in mmengine._strategy.deepspeed
+2024/08/24 17:04:51 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.LinearLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/24 17:04:51 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.CosineAnnealingLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/24 17:04:51 - mmengine - INFO - Num train samples 4806
+2024/08/24 17:04:51 - mmengine - INFO - train example:
+2024/08/24 17:04:52 - mmengine - INFO -  <s><|im_start|> system
+You are an AI assistant whose name is InternLM (书生·浦语).<|im_end|><|im_start|>user
+ <img> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> 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+请从这张聊天截图中提取结构化信息<|im_end|><|im_start|> assistant
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+  "conversation": [
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+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "①微信",
+      "message_bbox": {
+        "min_x": 845,
+        "max_x": 988,
+        "min_y": 833,
+        "max_y": 869
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    }
+  ]
+}<|im_end|>
+2024/08/24 17:04:52 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
+2024/08/24 17:04:52 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
+2024/08/24 17:04:52 - mmengine - INFO - Checkpoints will be saved to /root/wangqun/work_dirs/internvl_ft_run_11_filter.
+2024/08/24 17:06:08 - mmengine - INFO - Iter(train) [  10/4816]  lr: 1.2589e-06  eta: 10:07:00  time: 7.5781  data_time: 0.0219  memory: 59352  loss: 0.4248
+2024/08/24 17:07:03 - mmengine - INFO - Iter(train) [  20/4816]  lr: 2.6575e-06  eta: 8:45:48  time: 5.5782  data_time: 0.0247  memory: 31435  loss: 0.4667
+2024/08/24 17:07:58 - mmengine - INFO - Iter(train) [  30/4816]  lr: 4.0561e-06  eta: 8:14:39  time: 5.4476  data_time: 0.0235  memory: 30238  loss: 0.4820
+2024/08/24 17:08:52 - mmengine - INFO - Iter(train) [  40/4816]  lr: 5.4547e-06  eta: 7:58:43  time: 5.4527  data_time: 0.0218  memory: 29633  loss: 0.4697
+2024/08/24 17:09:45 - mmengine - INFO - Iter(train) [  50/4816]  lr: 6.8533e-06  eta: 7:46:31  time: 5.3090  data_time: 0.0234  memory: 29652  loss: 0.4483
+2024/08/24 17:10:38 - mmengine - INFO - Iter(train) [  60/4816]  lr: 8.2519e-06  eta: 7:37:48  time: 5.2878  data_time: 0.0217  memory: 27754  loss: 0.4420
+2024/08/24 17:11:29 - mmengine - INFO - Iter(train) [  70/4816]  lr: 9.6505e-06  eta: 7:29:15  time: 5.1046  data_time: 0.0225  memory: 27007  loss: 0.4059
+2024/08/24 17:12:20 - mmengine - INFO - Iter(train) [  80/4816]  lr: 1.1049e-05  eta: 7:22:10  time: 5.0564  data_time: 0.0219  memory: 26809  loss: 0.3818

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20240825_102307/20240825_102307.log

@@ -0,0 +1,557 @@
+2024/08/25 10:23:07 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/25 10:23:07 - 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: 212342581
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/25 10:23:07 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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 10:23:07 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/25 10:23:08 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/25 10:23:08 - 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
+2024/08/25 10:23:09 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/25 10:23:09 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/25 10:23:09 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/25 10:23:09 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/25 10:23:09 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/25 10:23:09 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/25 10:23:09 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/25 10:23:09 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/25 10:23:09 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/25 10:23:09 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/25 10:23:09 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/25 10:23:09 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner
+2024/08/25 10:23:09 - mmengine - INFO - Starting to loading data and calc length
+2024/08/25 10:23:09 - mmengine - INFO - =======Starting to process /root/data/screenshot_od/layout_ocr_multi.json =======
+2024/08/25 10:23:16 - mmengine - INFO - =======total 4806 samples of /root/data/screenshot_od/layout_ocr_multi.json=======
+2024/08/25 10:23:16 - mmengine - INFO - end loading data and calc length
+2024/08/25 10:23:16 - mmengine - INFO - =======total 4806 samples=======
+2024/08/25 10:23:16 - mmengine - DEBUG - An `InternVL_V1_5_Dataset` instance is built from registry, and its implementation can be found in xtuner.dataset.internvl_dataset
+2024/08/25 10:23:16 - mmengine - INFO - LengthGroupedSampler is used.
+2024/08/25 10:23:16 - mmengine - INFO - LengthGroupedSampler construction is complete, and the selected attribute is modality_length
+2024/08/25 10:23:16 - mmengine - DEBUG - An `LengthGroupedSampler` instance is built from registry, and its implementation can be found in xtuner.dataset.samplers.length_grouped
+2024/08/25 10:23:16 - mmengine - WARNING - Dataset InternVL_V1_5_Dataset has no metainfo. ``dataset_meta`` in visualizer will be None.
+2024/08/25 10:23:16 - mmengine - DEBUG - An `TrainLoop` instance is built from registry, and its implementation can be found in xtuner.engine.runner.loops
+2024/08/25 10:23:16 - mmengine - INFO - Start to load InternVL_V1_5 model.
+2024/08/25 10:23:16 - mmengine - DEBUG - Get class `BaseDataPreprocessor` from "model" registry in "mmengine"
+2024/08/25 10:23:16 - mmengine - DEBUG - An `BaseDataPreprocessor` instance is built from registry, and its implementation can be found in mmengine.model.base_model.data_preprocessor
+2024/08/25 10:23:23 - mmengine - DEBUG - An `LoraConfig` instance is built from registry, and its implementation can be found in peft.tuners.lora.config
+2024/08/25 10:23:24 - mmengine - INFO - InternVL_V1_5(
+  (data_preprocessor): BaseDataPreprocessor()
+  (model): InternVLChatModel(
+    (vision_model): InternVisionModel(
+      (embeddings): InternVisionEmbeddings(
+        (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))
+      )
+      (encoder): InternVisionEncoder(
+        (layers): ModuleList(
+          (0-23): 24 x InternVisionEncoderLayer(
+            (attn): InternAttention(
+              (qkv): Linear(in_features=1024, out_features=3072, bias=True)
+              (attn_drop): Dropout(p=0.0, inplace=False)
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (proj): Linear(in_features=1024, out_features=1024, bias=True)
+            )
+            (mlp): InternMLP(
+              (act): GELUActivation()
+              (fc1): Linear(in_features=1024, out_features=4096, bias=True)
+              (fc2): Linear(in_features=4096, out_features=1024, bias=True)
+            )
+            (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (drop_path1): Identity()
+            (drop_path2): Identity()
+          )
+        )
+      )
+    )
+    (language_model): PeftModelForCausalLM(
+      (base_model): LoraModel(
+        (model): InternLM2ForCausalLM(
+          (model): InternLM2Model(
+            (tok_embeddings): Embedding(92553, 2048, padding_idx=2)
+            (layers): ModuleList(
+              (0-23): 24 x InternLM2DecoderLayer(
+                (attention): InternLM2Attention(
+                  (wqkv): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=4096, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=4096, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (wo): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (rotary_emb): InternLM2DynamicNTKScalingRotaryEmbedding()
+                )
+                (feed_forward): InternLM2MLP(
+                  (w1): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w3): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w2): lora.Linear(
+                    (base_layer): Linear4bit(in_features=8192, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=8192, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (act_fn): SiLU()
+                )
+                (attention_norm): InternLM2RMSNorm()
+                (ffn_norm): InternLM2RMSNorm()
+              )
+            )
+            (norm): InternLM2RMSNorm()
+          )
+          (output): lora.Linear(
+            (base_layer): Linear4bit(in_features=2048, out_features=92553, bias=False)
+            (lora_dropout): ModuleDict(
+              (default): Dropout(p=0.05, inplace=False)
+            )
+            (lora_A): ModuleDict(
+              (default): Linear(in_features=2048, out_features=128, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (default): Linear(in_features=128, out_features=92553, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+          )
+        )
+      )
+    )
+    (mlp1): Sequential(
+      (0): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): GELU(approximate='none')
+      (3): Linear(in_features=2048, out_features=2048, bias=True)
+    )
+  )
+)
+2024/08/25 10:23:24 - mmengine - INFO - InternVL_V1_5 construction is complete
+2024/08/25 10:23:24 - mmengine - DEBUG - An `InternVL_V1_5` instance is built from registry, and its implementation can be found in xtuner.model.internvl
+2024/08/25 10:23:24 - mmengine - DEBUG - Get class `DefaultOptimWrapperConstructor` from "optimizer wrapper constructor" registry in "mmengine"
+2024/08/25 10:23:24 - mmengine - DEBUG - An `DefaultOptimWrapperConstructor` instance is built from registry, and its implementation can be found in mmengine.optim.optimizer.default_constructor
+2024/08/25 10:23:24 - mmengine - DEBUG - An `AdamW` instance is built from registry, and its implementation can be found in torch.optim.adamw
+2024/08/25 10:23:24 - mmengine - DEBUG - Get class `DeepSpeedOptimWrapper` from "optim_wrapper" registry in "mmengine"
+2024/08/25 10:23:24 - mmengine - DEBUG - An `DeepSpeedOptimWrapper` instance is built from registry, and its implementation can be found in mmengine._strategy.deepspeed
+2024/08/25 10:23:26 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.LinearLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 10:23:26 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.CosineAnnealingLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 10:23:26 - mmengine - INFO - Num train samples 4806
+2024/08/25 10:23:26 - mmengine - INFO - train example:
+2024/08/25 10:23:26 - mmengine - INFO -  <s><|im_start|> system
+You are an AI assistant whose name is InternLM (书生·浦语).<|im_end|><|im_start|>user
+ <img> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> 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<IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> </img>  
+请从这张聊天截图中提取结构化信息<|im_end|><|im_start|> assistant
+{
+  "dialog_name": "<对方正在输入...",
+  "conversation": [
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不是",
+      "message_bbox": {
+        "min_x": 917,
+        "max_x": 989,
+        "min_y": 253,
+        "max_y": 289
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "在淘宝里",
+      "message_bbox": {
+        "min_x": 839,
+        "max_x": 987,
+        "min_y": 370,
+        "max_y": 404
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不能发微信",
+      "message_bbox": {
+        "min_x": 801,
+        "max_x": 989,
+        "min_y": 485,
+        "max_y": 521
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "两字",
+      "message_bbox": {
+        "min_x": 915,
+        "max_x": 988,
+        "min_y": 601,
+        "max_y": 637
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "微信",
+      "message_bbox": {
+        "min_x": 916,
+        "max_x": 990,
+        "min_y": 718,
+        "max_y": 753
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "①微信",
+      "message_bbox": {
+        "min_x": 845,
+        "max_x": 988,
+        "min_y": 833,
+        "max_y": 869
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    }
+  ]
+}<|im_end|>
+2024/08/25 10:23:26 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
+2024/08/25 10:23:26 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
+2024/08/25 10:23:26 - mmengine - INFO - Checkpoints will be saved to /root/wangqun/work_dirs/internvl_ft_run_11_filter.
+2024/08/25 10:24:38 - mmengine - INFO - Iter(train) [  10/4816]  lr: 1.2589e-06  eta: 9:35:35  time: 7.1859  data_time: 0.0270  memory: 58220  loss: 0.4276
+2024/08/25 10:25:35 - mmengine - INFO - Iter(train) [  20/4816]  lr: 2.6575e-06  eta: 8:34:21  time: 5.6837  data_time: 0.0236  memory: 31385  loss: 0.4598

20240825_102307/vis_data/events.out.tfevents.1724552589.intern-studio-40019814.379332.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44478b8d120a199f0cba899fb687abdb507fa0b5c6120996f9281651ef7f80ce
+size 5322

20240825_102814/20240825_102814.log

@@ -0,0 +1,304 @@
+2024/08/25 10:28:14 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/25 10:28:15 - 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: 659640074
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/25 10:28:15 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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 10:28:15 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/25 10:28:15 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/25 10:28:15 - 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
+2024/08/25 10:28:16 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/25 10:28:16 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/25 10:28:16 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/25 10:28:16 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/25 10:28:16 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/25 10:28:16 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/25 10:28:16 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/25 10:28:16 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/25 10:28:17 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/25 10:28:17 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/25 10:28:17 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/25 10:28:17 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner
+2024/08/25 10:28:17 - mmengine - INFO - Starting to loading data and calc length
+2024/08/25 10:28:17 - mmengine - INFO - =======Starting to process /root/data/screenshot_od/layout_ocr_multi.json =======
+2024/08/25 10:28:24 - mmengine - INFO - =======total 4806 samples of /root/data/screenshot_od/layout_ocr_multi.json=======
+2024/08/25 10:28:24 - mmengine - INFO - end loading data and calc length
+2024/08/25 10:28:24 - mmengine - INFO - =======total 4806 samples=======
+2024/08/25 10:28:24 - mmengine - DEBUG - An `InternVL_V1_5_Dataset` instance is built from registry, and its implementation can be found in xtuner.dataset.internvl_dataset
+2024/08/25 10:28:24 - mmengine - INFO - LengthGroupedSampler is used.
+2024/08/25 10:28:24 - mmengine - INFO - LengthGroupedSampler construction is complete, and the selected attribute is modality_length
+2024/08/25 10:28:24 - mmengine - DEBUG - An `LengthGroupedSampler` instance is built from registry, and its implementation can be found in xtuner.dataset.samplers.length_grouped
+2024/08/25 10:28:24 - mmengine - WARNING - Dataset InternVL_V1_5_Dataset has no metainfo. ``dataset_meta`` in visualizer will be None.
+2024/08/25 10:28:24 - mmengine - DEBUG - An `TrainLoop` instance is built from registry, and its implementation can be found in xtuner.engine.runner.loops
+2024/08/25 10:28:24 - mmengine - INFO - Start to load InternVL_V1_5 model.
+2024/08/25 10:28:24 - mmengine - DEBUG - Get class `BaseDataPreprocessor` from "model" registry in "mmengine"
+2024/08/25 10:28:24 - mmengine - DEBUG - An `BaseDataPreprocessor` instance is built from registry, and its implementation can be found in mmengine.model.base_model.data_preprocessor

20240825_102814/vis_data/events.out.tfevents.1724552896.intern-studio-40019814.381761.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c00bfe66c2140a5bf6366bdcd164c6d0e57cff378cc359bc69231e4a04d1ee8
+size 4808

20240825_102957/20240825_102957.log

@@ -0,0 +1,555 @@
+2024/08/25 10:29:57 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/25 10:29:58 - 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: 1686087262
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/25 10:29:58 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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 10:29:58 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/25 10:29:58 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/25 10:29:58 - 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
+2024/08/25 10:29:59 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/25 10:29:59 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/25 10:29:59 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/25 10:29:59 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/25 10:29:59 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/25 10:29:59 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/25 10:29:59 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/25 10:29:59 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/25 10:29:59 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/25 10:29:59 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/25 10:29:59 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/25 10:29:59 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner
+2024/08/25 10:29:59 - mmengine - INFO - Starting to loading data and calc length
+2024/08/25 10:29:59 - mmengine - INFO - =======Starting to process /root/data/screenshot_od/layout_ocr_multi.json =======
+2024/08/25 10:30:06 - mmengine - INFO - =======total 4806 samples of /root/data/screenshot_od/layout_ocr_multi.json=======
+2024/08/25 10:30:06 - mmengine - INFO - end loading data and calc length
+2024/08/25 10:30:06 - mmengine - INFO - =======total 4806 samples=======
+2024/08/25 10:30:06 - mmengine - DEBUG - An `InternVL_V1_5_Dataset` instance is built from registry, and its implementation can be found in xtuner.dataset.internvl_dataset
+2024/08/25 10:30:06 - mmengine - INFO - LengthGroupedSampler is used.
+2024/08/25 10:30:06 - mmengine - INFO - LengthGroupedSampler construction is complete, and the selected attribute is modality_length
+2024/08/25 10:30:06 - mmengine - DEBUG - An `LengthGroupedSampler` instance is built from registry, and its implementation can be found in xtuner.dataset.samplers.length_grouped
+2024/08/25 10:30:06 - mmengine - WARNING - Dataset InternVL_V1_5_Dataset has no metainfo. ``dataset_meta`` in visualizer will be None.
+2024/08/25 10:30:06 - mmengine - DEBUG - An `TrainLoop` instance is built from registry, and its implementation can be found in xtuner.engine.runner.loops
+2024/08/25 10:30:06 - mmengine - INFO - Start to load InternVL_V1_5 model.
+2024/08/25 10:30:06 - mmengine - DEBUG - Get class `BaseDataPreprocessor` from "model" registry in "mmengine"
+2024/08/25 10:30:06 - mmengine - DEBUG - An `BaseDataPreprocessor` instance is built from registry, and its implementation can be found in mmengine.model.base_model.data_preprocessor
+2024/08/25 10:30:11 - mmengine - DEBUG - An `LoraConfig` instance is built from registry, and its implementation can be found in peft.tuners.lora.config
+2024/08/25 10:30:12 - mmengine - INFO - InternVL_V1_5(
+  (data_preprocessor): BaseDataPreprocessor()
+  (model): InternVLChatModel(
+    (vision_model): InternVisionModel(
+      (embeddings): InternVisionEmbeddings(
+        (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))
+      )
+      (encoder): InternVisionEncoder(
+        (layers): ModuleList(
+          (0-23): 24 x InternVisionEncoderLayer(
+            (attn): InternAttention(
+              (qkv): Linear(in_features=1024, out_features=3072, bias=True)
+              (attn_drop): Dropout(p=0.0, inplace=False)
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (proj): Linear(in_features=1024, out_features=1024, bias=True)
+            )
+            (mlp): InternMLP(
+              (act): GELUActivation()
+              (fc1): Linear(in_features=1024, out_features=4096, bias=True)
+              (fc2): Linear(in_features=4096, out_features=1024, bias=True)
+            )
+            (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (drop_path1): Identity()
+            (drop_path2): Identity()
+          )
+        )
+      )
+    )
+    (language_model): PeftModelForCausalLM(
+      (base_model): LoraModel(
+        (model): InternLM2ForCausalLM(
+          (model): InternLM2Model(
+            (tok_embeddings): Embedding(92553, 2048, padding_idx=2)
+            (layers): ModuleList(
+              (0-23): 24 x InternLM2DecoderLayer(
+                (attention): InternLM2Attention(
+                  (wqkv): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=4096, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=4096, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (wo): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (rotary_emb): InternLM2DynamicNTKScalingRotaryEmbedding()
+                )
+                (feed_forward): InternLM2MLP(
+                  (w1): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w3): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w2): lora.Linear(
+                    (base_layer): Linear4bit(in_features=8192, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=8192, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (act_fn): SiLU()
+                )
+                (attention_norm): InternLM2RMSNorm()
+                (ffn_norm): InternLM2RMSNorm()
+              )
+            )
+            (norm): InternLM2RMSNorm()
+          )
+          (output): lora.Linear(
+            (base_layer): Linear4bit(in_features=2048, out_features=92553, bias=False)
+            (lora_dropout): ModuleDict(
+              (default): Dropout(p=0.05, inplace=False)
+            )
+            (lora_A): ModuleDict(
+              (default): Linear(in_features=2048, out_features=128, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (default): Linear(in_features=128, out_features=92553, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+          )
+        )
+      )
+    )
+    (mlp1): Sequential(
+      (0): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): GELU(approximate='none')
+      (3): Linear(in_features=2048, out_features=2048, bias=True)
+    )
+  )
+)
+2024/08/25 10:30:12 - mmengine - INFO - InternVL_V1_5 construction is complete
+2024/08/25 10:30:12 - mmengine - DEBUG - An `InternVL_V1_5` instance is built from registry, and its implementation can be found in xtuner.model.internvl
+2024/08/25 10:30:12 - mmengine - DEBUG - Get class `DefaultOptimWrapperConstructor` from "optimizer wrapper constructor" registry in "mmengine"
+2024/08/25 10:30:12 - mmengine - DEBUG - An `DefaultOptimWrapperConstructor` instance is built from registry, and its implementation can be found in mmengine.optim.optimizer.default_constructor
+2024/08/25 10:30:12 - mmengine - DEBUG - An `AdamW` instance is built from registry, and its implementation can be found in torch.optim.adamw
+2024/08/25 10:30:12 - mmengine - DEBUG - Get class `DeepSpeedOptimWrapper` from "optim_wrapper" registry in "mmengine"
+2024/08/25 10:30:12 - mmengine - DEBUG - An `DeepSpeedOptimWrapper` instance is built from registry, and its implementation can be found in mmengine._strategy.deepspeed
+2024/08/25 10:30:14 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.LinearLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 10:30:14 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.CosineAnnealingLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 10:30:14 - mmengine - INFO - Num train samples 4806
+2024/08/25 10:30:14 - mmengine - INFO - train example:
+2024/08/25 10:30:14 - mmengine - INFO -  <s><|im_start|> system
+You are an AI assistant whose name is InternLM (书生·浦语).<|im_end|><|im_start|>user
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+请从这张聊天截图中提取结构化信息<|im_end|><|im_start|> assistant
+{
+  "dialog_name": "<对方正在输入...",
+  "conversation": [
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不是",
+      "message_bbox": {
+        "min_x": 917,
+        "max_x": 989,
+        "min_y": 253,
+        "max_y": 289
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "在淘宝里",
+      "message_bbox": {
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+        "max_x": 987,
+        "min_y": 370,
+        "max_y": 404
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不能发微信",
+      "message_bbox": {
+        "min_x": 801,
+        "max_x": 989,
+        "min_y": 485,
+        "max_y": 521
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "两字",
+      "message_bbox": {
+        "min_x": 915,
+        "max_x": 988,
+        "min_y": 601,
+        "max_y": 637
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "微信",
+      "message_bbox": {
+        "min_x": 916,
+        "max_x": 990,
+        "min_y": 718,
+        "max_y": 753
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "①微信",
+      "message_bbox": {
+        "min_x": 845,
+        "max_x": 988,
+        "min_y": 833,
+        "max_y": 869
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    }
+  ]
+}<|im_end|>
+2024/08/25 10:30:14 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
+2024/08/25 10:30:14 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
+2024/08/25 10:30:14 - mmengine - INFO - Checkpoints will be saved to /root/wangqun/work_dirs/internvl_ft_run_11_filter.

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20240825_174647/20240825_174647.log

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+2024/08/25 17:46:47 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/25 17:47:00 - 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: 1151570718
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/25 17:47:00 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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 17:47:00 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/25 17:47:00 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/25 17:47:00 - 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
+2024/08/25 17:47:03 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/25 17:47:03 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/25 17:47:03 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/25 17:47:03 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/25 17:47:03 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/25 17:47:03 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/25 17:47:03 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/25 17:47:03 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/25 17:47:04 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/25 17:47:04 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/25 17:47:04 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/25 17:47:04 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner

20240825_174647/vis_data/events.out.tfevents.1724579223.intern-studio-40019814.478798.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:168f197f24cf38e805ae5f8ec87abbfa4d2c4650003523f0b66ba7ed5ea6747c
+size 4808

20240825_175333/20240825_175333.log

@@ -0,0 +1,290 @@
+2024/08/25 17:53:33 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/25 17:53:36 - 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: 1188247065
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/25 17:53:36 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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 17:53:36 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/25 17:53:36 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/25 17:53:36 - 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
+2024/08/25 17:53:38 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/25 17:53:38 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/25 17:53:38 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/25 17:53:38 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/25 17:53:38 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/25 17:53:38 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/25 17:53:38 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/25 17:53:38 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/25 17:53:39 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/25 17:53:39 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/25 17:53:39 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/25 17:53:39 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner

20240825_175333/vis_data/events.out.tfevents.1724579618.intern-studio-40019814.481423.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:754d06f8575247d1d13737d197341dce7ffc7b1adf6eb2155edbeaa70b5f50ce
+size 4808

20240825_181551/20240825_181551.log

@@ -0,0 +1,180 @@
+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

20240825_181551/vis_data/events.out.tfevents.1724580955.intern-studio-40019814.488163.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef99230d43f8b110aeefd3d6d7db921c3de80eadae7c0c14c8b03a0f865a25e9
+size 4419

20240825_184143/20240825_184143.log

@@ -0,0 +1,555 @@
+2024/08/25 18:41:43 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/25 18:41:45 - 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: 1826898608
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/25 18:41:45 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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 18:41:45 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/25 18:41:45 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/25 18:41:45 - 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
+2024/08/25 18:41:47 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/25 18:41:47 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/25 18:41:47 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/25 18:41:47 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/25 18:41:47 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/25 18:41:47 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/25 18:41:47 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/25 18:41:47 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/25 18:41:48 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/25 18:41:48 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/25 18:41:48 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/25 18:41:48 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner
+2024/08/25 18:41:57 - mmengine - INFO - Starting to loading data and calc length
+2024/08/25 18:41:57 - mmengine - INFO - =======Starting to process /root/data/screenshot_od/layout_ocr_multi.json =======
+2024/08/25 18:42:15 - mmengine - INFO - =======total 4806 samples of /root/data/screenshot_od/layout_ocr_multi.json=======
+2024/08/25 18:42:15 - mmengine - INFO - end loading data and calc length
+2024/08/25 18:42:15 - mmengine - INFO - =======total 4806 samples=======
+2024/08/25 18:42:15 - mmengine - DEBUG - An `InternVL_V1_5_Dataset` instance is built from registry, and its implementation can be found in xtuner.dataset.internvl_dataset
+2024/08/25 18:42:15 - mmengine - INFO - LengthGroupedSampler is used.
+2024/08/25 18:42:15 - mmengine - INFO - LengthGroupedSampler construction is complete, and the selected attribute is modality_length
+2024/08/25 18:42:15 - mmengine - DEBUG - An `LengthGroupedSampler` instance is built from registry, and its implementation can be found in xtuner.dataset.samplers.length_grouped
+2024/08/25 18:42:15 - mmengine - WARNING - Dataset InternVL_V1_5_Dataset has no metainfo. ``dataset_meta`` in visualizer will be None.
+2024/08/25 18:42:15 - mmengine - DEBUG - An `TrainLoop` instance is built from registry, and its implementation can be found in xtuner.engine.runner.loops
+2024/08/25 18:42:15 - mmengine - INFO - Start to load InternVL_V1_5 model.
+2024/08/25 18:42:15 - mmengine - DEBUG - Get class `BaseDataPreprocessor` from "model" registry in "mmengine"
+2024/08/25 18:42:15 - mmengine - DEBUG - An `BaseDataPreprocessor` instance is built from registry, and its implementation can be found in mmengine.model.base_model.data_preprocessor
+2024/08/25 18:42:31 - mmengine - DEBUG - An `LoraConfig` instance is built from registry, and its implementation can be found in peft.tuners.lora.config
+2024/08/25 18:42:32 - mmengine - INFO - InternVL_V1_5(
+  (data_preprocessor): BaseDataPreprocessor()
+  (model): InternVLChatModel(
+    (vision_model): InternVisionModel(
+      (embeddings): InternVisionEmbeddings(
+        (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))
+      )
+      (encoder): InternVisionEncoder(
+        (layers): ModuleList(
+          (0-23): 24 x InternVisionEncoderLayer(
+            (attn): InternAttention(
+              (qkv): Linear(in_features=1024, out_features=3072, bias=True)
+              (attn_drop): Dropout(p=0.0, inplace=False)
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (proj): Linear(in_features=1024, out_features=1024, bias=True)
+            )
+            (mlp): InternMLP(
+              (act): GELUActivation()
+              (fc1): Linear(in_features=1024, out_features=4096, bias=True)
+              (fc2): Linear(in_features=4096, out_features=1024, bias=True)
+            )
+            (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (drop_path1): Identity()
+            (drop_path2): Identity()
+          )
+        )
+      )
+    )
+    (language_model): PeftModelForCausalLM(
+      (base_model): LoraModel(
+        (model): InternLM2ForCausalLM(
+          (model): InternLM2Model(
+            (tok_embeddings): Embedding(92553, 2048, padding_idx=2)
+            (layers): ModuleList(
+              (0-23): 24 x InternLM2DecoderLayer(
+                (attention): InternLM2Attention(
+                  (wqkv): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=4096, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=4096, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (wo): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (rotary_emb): InternLM2DynamicNTKScalingRotaryEmbedding()
+                )
+                (feed_forward): InternLM2MLP(
+                  (w1): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w3): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w2): lora.Linear(
+                    (base_layer): Linear4bit(in_features=8192, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=8192, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (act_fn): SiLU()
+                )
+                (attention_norm): InternLM2RMSNorm()
+                (ffn_norm): InternLM2RMSNorm()
+              )
+            )
+            (norm): InternLM2RMSNorm()
+          )
+          (output): lora.Linear(
+            (base_layer): Linear4bit(in_features=2048, out_features=92553, bias=False)
+            (lora_dropout): ModuleDict(
+              (default): Dropout(p=0.05, inplace=False)
+            )
+            (lora_A): ModuleDict(
+              (default): Linear(in_features=2048, out_features=128, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (default): Linear(in_features=128, out_features=92553, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+          )
+        )
+      )
+    )
+    (mlp1): Sequential(
+      (0): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): GELU(approximate='none')
+      (3): Linear(in_features=2048, out_features=2048, bias=True)
+    )
+  )
+)
+2024/08/25 18:42:32 - mmengine - INFO - InternVL_V1_5 construction is complete
+2024/08/25 18:42:32 - mmengine - DEBUG - An `InternVL_V1_5` instance is built from registry, and its implementation can be found in xtuner.model.internvl
+2024/08/25 18:42:32 - mmengine - DEBUG - Get class `DefaultOptimWrapperConstructor` from "optimizer wrapper constructor" registry in "mmengine"
+2024/08/25 18:42:32 - mmengine - DEBUG - An `DefaultOptimWrapperConstructor` instance is built from registry, and its implementation can be found in mmengine.optim.optimizer.default_constructor
+2024/08/25 18:42:32 - mmengine - DEBUG - An `AdamW` instance is built from registry, and its implementation can be found in torch.optim.adamw
+2024/08/25 18:42:32 - mmengine - DEBUG - Get class `DeepSpeedOptimWrapper` from "optim_wrapper" registry in "mmengine"
+2024/08/25 18:42:32 - mmengine - DEBUG - An `DeepSpeedOptimWrapper` instance is built from registry, and its implementation can be found in mmengine._strategy.deepspeed
+2024/08/25 18:42:35 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.LinearLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 18:42:35 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.CosineAnnealingLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 18:43:34 - mmengine - INFO - Num train samples 4806
+2024/08/25 18:43:34 - mmengine - INFO - train example:
+2024/08/25 18:43:35 - mmengine - INFO -  <s><|im_start|> system
+You are an AI assistant whose name is InternLM (书生·浦语).<|im_end|><|im_start|>user
+ <img> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> 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+请从这张聊天截图中提取结构化信息<|im_end|><|im_start|> assistant
+{
+  "dialog_name": "<对方正在输入...",
+  "conversation": [
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不是",
+      "message_bbox": {
+        "min_x": 917,
+        "max_x": 989,
+        "min_y": 253,
+        "max_y": 289
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "在淘宝里",
+      "message_bbox": {
+        "min_x": 839,
+        "max_x": 987,
+        "min_y": 370,
+        "max_y": 404
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不能发微信",
+      "message_bbox": {
+        "min_x": 801,
+        "max_x": 989,
+        "min_y": 485,
+        "max_y": 521
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "两字",
+      "message_bbox": {
+        "min_x": 915,
+        "max_x": 988,
+        "min_y": 601,
+        "max_y": 637
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "微信",
+      "message_bbox": {
+        "min_x": 916,
+        "max_x": 990,
+        "min_y": 718,
+        "max_y": 753
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "①微信",
+      "message_bbox": {
+        "min_x": 845,
+        "max_x": 988,
+        "min_y": 833,
+        "max_y": 869
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    }
+  ]
+}<|im_end|>
+2024/08/25 18:43:35 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
+2024/08/25 18:43:35 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
+2024/08/25 18:43:35 - mmengine - INFO - Checkpoints will be saved to /root/wangqun/work_dirs/internvl_ft_run_11_filter.

20240825_184143/vis_data/events.out.tfevents.1724582507.intern-studio-40019814.495346.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0d37808d6ce4de353c78aff216c33f385f261999a7cf97ee856b694683d522e
+size 4808

20240825_184152/20240825_184152.log

@@ -0,0 +1,555 @@
+2024/08/25 18:41:52 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/25 18:41:55 - 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: 2068084726
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/25 18:41:55 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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 18:41:55 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/25 18:41:55 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/25 18:41:55 - 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
+2024/08/25 18:41:56 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/25 18:41:56 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/25 18:41:56 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/25 18:41:56 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/25 18:41:56 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/25 18:41:56 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/25 18:41:56 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/25 18:41:56 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/25 18:41:56 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/25 18:41:56 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/25 18:41:56 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/25 18:41:57 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner
+2024/08/25 18:41:59 - mmengine - INFO - Starting to loading data and calc length
+2024/08/25 18:41:59 - mmengine - INFO - =======Starting to process /root/data/screenshot_od/layout_ocr_multi.json =======
+2024/08/25 18:42:17 - mmengine - INFO - =======total 4806 samples of /root/data/screenshot_od/layout_ocr_multi.json=======
+2024/08/25 18:42:17 - mmengine - INFO - end loading data and calc length
+2024/08/25 18:42:17 - mmengine - INFO - =======total 4806 samples=======
+2024/08/25 18:42:17 - mmengine - DEBUG - An `InternVL_V1_5_Dataset` instance is built from registry, and its implementation can be found in xtuner.dataset.internvl_dataset
+2024/08/25 18:42:17 - mmengine - INFO - LengthGroupedSampler is used.
+2024/08/25 18:42:17 - mmengine - INFO - LengthGroupedSampler construction is complete, and the selected attribute is modality_length
+2024/08/25 18:42:17 - mmengine - DEBUG - An `LengthGroupedSampler` instance is built from registry, and its implementation can be found in xtuner.dataset.samplers.length_grouped
+2024/08/25 18:42:17 - mmengine - WARNING - Dataset InternVL_V1_5_Dataset has no metainfo. ``dataset_meta`` in visualizer will be None.
+2024/08/25 18:42:17 - mmengine - DEBUG - An `TrainLoop` instance is built from registry, and its implementation can be found in xtuner.engine.runner.loops
+2024/08/25 18:42:17 - mmengine - INFO - Start to load InternVL_V1_5 model.
+2024/08/25 18:42:17 - mmengine - DEBUG - Get class `BaseDataPreprocessor` from "model" registry in "mmengine"
+2024/08/25 18:42:17 - mmengine - DEBUG - An `BaseDataPreprocessor` instance is built from registry, and its implementation can be found in mmengine.model.base_model.data_preprocessor
+2024/08/25 18:42:31 - mmengine - DEBUG - An `LoraConfig` instance is built from registry, and its implementation can be found in peft.tuners.lora.config
+2024/08/25 18:42:32 - mmengine - INFO - InternVL_V1_5(
+  (data_preprocessor): BaseDataPreprocessor()
+  (model): InternVLChatModel(
+    (vision_model): InternVisionModel(
+      (embeddings): InternVisionEmbeddings(
+        (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))
+      )
+      (encoder): InternVisionEncoder(
+        (layers): ModuleList(
+          (0-23): 24 x InternVisionEncoderLayer(
+            (attn): InternAttention(
+              (qkv): Linear(in_features=1024, out_features=3072, bias=True)
+              (attn_drop): Dropout(p=0.0, inplace=False)
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (proj): Linear(in_features=1024, out_features=1024, bias=True)
+            )
+            (mlp): InternMLP(
+              (act): GELUActivation()
+              (fc1): Linear(in_features=1024, out_features=4096, bias=True)
+              (fc2): Linear(in_features=4096, out_features=1024, bias=True)
+            )
+            (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (drop_path1): Identity()
+            (drop_path2): Identity()
+          )
+        )
+      )
+    )
+    (language_model): PeftModelForCausalLM(
+      (base_model): LoraModel(
+        (model): InternLM2ForCausalLM(
+          (model): InternLM2Model(
+            (tok_embeddings): Embedding(92553, 2048, padding_idx=2)
+            (layers): ModuleList(
+              (0-23): 24 x InternLM2DecoderLayer(
+                (attention): InternLM2Attention(
+                  (wqkv): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=4096, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=4096, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (wo): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (rotary_emb): InternLM2DynamicNTKScalingRotaryEmbedding()
+                )
+                (feed_forward): InternLM2MLP(
+                  (w1): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w3): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w2): lora.Linear(
+                    (base_layer): Linear4bit(in_features=8192, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=8192, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (act_fn): SiLU()
+                )
+                (attention_norm): InternLM2RMSNorm()
+                (ffn_norm): InternLM2RMSNorm()
+              )
+            )
+            (norm): InternLM2RMSNorm()
+          )
+          (output): lora.Linear(
+            (base_layer): Linear4bit(in_features=2048, out_features=92553, bias=False)
+            (lora_dropout): ModuleDict(
+              (default): Dropout(p=0.05, inplace=False)
+            )
+            (lora_A): ModuleDict(
+              (default): Linear(in_features=2048, out_features=128, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (default): Linear(in_features=128, out_features=92553, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+          )
+        )
+      )
+    )
+    (mlp1): Sequential(
+      (0): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): GELU(approximate='none')
+      (3): Linear(in_features=2048, out_features=2048, bias=True)
+    )
+  )
+)
+2024/08/25 18:42:32 - mmengine - INFO - InternVL_V1_5 construction is complete
+2024/08/25 18:42:32 - mmengine - DEBUG - An `InternVL_V1_5` instance is built from registry, and its implementation can be found in xtuner.model.internvl
+2024/08/25 18:42:32 - mmengine - DEBUG - Get class `DefaultOptimWrapperConstructor` from "optimizer wrapper constructor" registry in "mmengine"
+2024/08/25 18:42:32 - mmengine - DEBUG - An `DefaultOptimWrapperConstructor` instance is built from registry, and its implementation can be found in mmengine.optim.optimizer.default_constructor
+2024/08/25 18:42:32 - mmengine - DEBUG - An `AdamW` instance is built from registry, and its implementation can be found in torch.optim.adamw
+2024/08/25 18:42:32 - mmengine - DEBUG - Get class `DeepSpeedOptimWrapper` from "optim_wrapper" registry in "mmengine"
+2024/08/25 18:42:32 - mmengine - DEBUG - An `DeepSpeedOptimWrapper` instance is built from registry, and its implementation can be found in mmengine._strategy.deepspeed
+2024/08/25 18:42:35 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.LinearLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 18:42:35 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.CosineAnnealingLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 18:43:30 - mmengine - INFO - Num train samples 4806
+2024/08/25 18:43:30 - mmengine - INFO - train example:
+2024/08/25 18:43:32 - mmengine - INFO -  <s><|im_start|> system
+You are an AI assistant whose name is InternLM (书生·浦语).<|im_end|><|im_start|>user
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<IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> </img>  
+请从这张聊天截图中提取结构化信息<|im_end|><|im_start|> assistant
+{
+  "dialog_name": "<对方正在输入...",
+  "conversation": [
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不是",
+      "message_bbox": {
+        "min_x": 917,
+        "max_x": 989,
+        "min_y": 253,
+        "max_y": 289
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "在淘宝里",
+      "message_bbox": {
+        "min_x": 839,
+        "max_x": 987,
+        "min_y": 370,
+        "max_y": 404
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不能发微信",
+      "message_bbox": {
+        "min_x": 801,
+        "max_x": 989,
+        "min_y": 485,
+        "max_y": 521
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "两字",
+      "message_bbox": {
+        "min_x": 915,
+        "max_x": 988,
+        "min_y": 601,
+        "max_y": 637
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "微信",
+      "message_bbox": {
+        "min_x": 916,
+        "max_x": 990,
+        "min_y": 718,
+        "max_y": 753
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "①微信",
+      "message_bbox": {
+        "min_x": 845,
+        "max_x": 988,
+        "min_y": 833,
+        "max_y": 869
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    }
+  ]
+}<|im_end|>
+2024/08/25 18:43:32 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
+2024/08/25 18:43:32 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
+2024/08/25 18:43:32 - mmengine - INFO - Checkpoints will be saved to /root/wangqun/work_dirs/internvl_ft_run_11_filter.

20240825_184152/vis_data/events.out.tfevents.1724582516.intern-studio-40019814.495347.0

@@ -0,0 +1,3 @@
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+oid sha256:f2cb915110ef2a80b30c5c6e3f4a97f887e7c329f75c0534189cedd7e4ce4f36
+size 4808

20240825_185120/20240825_185120.log

@@ -0,0 +1,292 @@
+2024/08/25 18:51:20 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/25 18:51:22 - 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: 1041269373
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/25 18:51:22 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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 18:51:22 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/25 18:51:22 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/25 18:51:22 - 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
+2024/08/25 18:51:24 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/25 18:51:24 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/25 18:51:24 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/25 18:51:24 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/25 18:51:24 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/25 18:51:24 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/25 18:51:24 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/25 18:51:24 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/25 18:51:24 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/25 18:51:24 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/25 18:51:24 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/25 18:51:25 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner
+2024/08/25 18:52:13 - mmengine - INFO - Starting to loading data and calc length
+2024/08/25 18:52:13 - mmengine - INFO - =======Starting to process /root/data/screenshot_od/layout_ocr_multi.json =======

20240825_185120/vis_data/events.out.tfevents.1724583083.intern-studio-40019814.498702.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4920cc8f4a01d2eba3e160b1f39b2b610e99559b17060f87b7907603cbb1e48a
+size 4808

20240825_185204/20240825_185204.log

@@ -0,0 +1,292 @@
+2024/08/25 18:52:04 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/25 18:52:05 - 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: 1352614851
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/25 18:52:05 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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 18:52:05 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/25 18:52:05 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/25 18:52:05 - 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
+2024/08/25 18:52:06 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/25 18:52:06 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/25 18:52:06 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/25 18:52:06 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/25 18:52:06 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/25 18:52:06 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/25 18:52:06 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/25 18:52:06 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/25 18:52:07 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/25 18:52:07 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/25 18:52:07 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/25 18:52:07 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner
+2024/08/25 18:52:11 - mmengine - INFO - Starting to loading data and calc length
+2024/08/25 18:52:11 - mmengine - INFO - =======Starting to process /root/data/screenshot_od/layout_ocr_multi.json =======

20240825_185204/vis_data/events.out.tfevents.1724583126.intern-studio-40019814.498701.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:740134033a6c6da6d1dec2a60806f390da85f1e48a56098f10940768f6567182
+size 4808

20240825_185458/20240825_185458.log

@@ -0,0 +1,555 @@
+2024/08/25 18:54:59 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/25 18:55:00 - 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: 842707294
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/25 18:55:00 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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 18:55:00 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/25 18:55:00 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/25 18:55:00 - 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
+2024/08/25 18:55:02 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/25 18:55:02 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/25 18:55:02 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/25 18:55:02 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/25 18:55:02 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/25 18:55:02 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/25 18:55:02 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/25 18:55:02 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/25 18:55:03 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/25 18:55:03 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/25 18:55:03 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/25 18:55:03 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner
+2024/08/25 18:56:03 - mmengine - INFO - Starting to loading data and calc length
+2024/08/25 18:56:03 - mmengine - INFO - =======Starting to process /root/data/screenshot_od/layout_ocr_multi.json =======
+2024/08/25 18:56:21 - mmengine - INFO - =======total 4806 samples of /root/data/screenshot_od/layout_ocr_multi.json=======
+2024/08/25 18:56:21 - mmengine - INFO - end loading data and calc length
+2024/08/25 18:56:21 - mmengine - INFO - =======total 4806 samples=======
+2024/08/25 18:56:21 - mmengine - DEBUG - An `InternVL_V1_5_Dataset` instance is built from registry, and its implementation can be found in xtuner.dataset.internvl_dataset
+2024/08/25 18:56:21 - mmengine - INFO - LengthGroupedSampler is used.
+2024/08/25 18:56:21 - mmengine - INFO - LengthGroupedSampler construction is complete, and the selected attribute is modality_length
+2024/08/25 18:56:21 - mmengine - DEBUG - An `LengthGroupedSampler` instance is built from registry, and its implementation can be found in xtuner.dataset.samplers.length_grouped
+2024/08/25 18:56:21 - mmengine - WARNING - Dataset InternVL_V1_5_Dataset has no metainfo. ``dataset_meta`` in visualizer will be None.
+2024/08/25 18:56:21 - mmengine - DEBUG - An `TrainLoop` instance is built from registry, and its implementation can be found in xtuner.engine.runner.loops
+2024/08/25 18:56:21 - mmengine - INFO - Start to load InternVL_V1_5 model.
+2024/08/25 18:56:21 - mmengine - DEBUG - Get class `BaseDataPreprocessor` from "model" registry in "mmengine"
+2024/08/25 18:56:21 - mmengine - DEBUG - An `BaseDataPreprocessor` instance is built from registry, and its implementation can be found in mmengine.model.base_model.data_preprocessor
+2024/08/25 18:56:33 - mmengine - DEBUG - An `LoraConfig` instance is built from registry, and its implementation can be found in peft.tuners.lora.config
+2024/08/25 18:56:34 - mmengine - INFO - InternVL_V1_5(
+  (data_preprocessor): BaseDataPreprocessor()
+  (model): InternVLChatModel(
+    (vision_model): InternVisionModel(
+      (embeddings): InternVisionEmbeddings(
+        (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))
+      )
+      (encoder): InternVisionEncoder(
+        (layers): ModuleList(
+          (0-23): 24 x InternVisionEncoderLayer(
+            (attn): InternAttention(
+              (qkv): Linear(in_features=1024, out_features=3072, bias=True)
+              (attn_drop): Dropout(p=0.0, inplace=False)
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (proj): Linear(in_features=1024, out_features=1024, bias=True)
+            )
+            (mlp): InternMLP(
+              (act): GELUActivation()
+              (fc1): Linear(in_features=1024, out_features=4096, bias=True)
+              (fc2): Linear(in_features=4096, out_features=1024, bias=True)
+            )
+            (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (drop_path1): Identity()
+            (drop_path2): Identity()
+          )
+        )
+      )
+    )
+    (language_model): PeftModelForCausalLM(
+      (base_model): LoraModel(
+        (model): InternLM2ForCausalLM(
+          (model): InternLM2Model(
+            (tok_embeddings): Embedding(92553, 2048, padding_idx=2)
+            (layers): ModuleList(
+              (0-23): 24 x InternLM2DecoderLayer(
+                (attention): InternLM2Attention(
+                  (wqkv): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=4096, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=4096, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (wo): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (rotary_emb): InternLM2DynamicNTKScalingRotaryEmbedding()
+                )
+                (feed_forward): InternLM2MLP(
+                  (w1): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w3): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w2): lora.Linear(
+                    (base_layer): Linear4bit(in_features=8192, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=8192, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (act_fn): SiLU()
+                )
+                (attention_norm): InternLM2RMSNorm()
+                (ffn_norm): InternLM2RMSNorm()
+              )
+            )
+            (norm): InternLM2RMSNorm()
+          )
+          (output): lora.Linear(
+            (base_layer): Linear4bit(in_features=2048, out_features=92553, bias=False)
+            (lora_dropout): ModuleDict(
+              (default): Dropout(p=0.05, inplace=False)
+            )
+            (lora_A): ModuleDict(
+              (default): Linear(in_features=2048, out_features=128, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (default): Linear(in_features=128, out_features=92553, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+          )
+        )
+      )
+    )
+    (mlp1): Sequential(
+      (0): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): GELU(approximate='none')
+      (3): Linear(in_features=2048, out_features=2048, bias=True)
+    )
+  )
+)
+2024/08/25 18:56:34 - mmengine - INFO - InternVL_V1_5 construction is complete
+2024/08/25 18:56:34 - mmengine - DEBUG - An `InternVL_V1_5` instance is built from registry, and its implementation can be found in xtuner.model.internvl
+2024/08/25 18:56:34 - mmengine - DEBUG - Get class `DefaultOptimWrapperConstructor` from "optimizer wrapper constructor" registry in "mmengine"
+2024/08/25 18:56:34 - mmengine - DEBUG - An `DefaultOptimWrapperConstructor` instance is built from registry, and its implementation can be found in mmengine.optim.optimizer.default_constructor
+2024/08/25 18:56:34 - mmengine - DEBUG - An `AdamW` instance is built from registry, and its implementation can be found in torch.optim.adamw
+2024/08/25 18:56:34 - mmengine - DEBUG - Get class `DeepSpeedOptimWrapper` from "optim_wrapper" registry in "mmengine"
+2024/08/25 18:56:34 - mmengine - DEBUG - An `DeepSpeedOptimWrapper` instance is built from registry, and its implementation can be found in mmengine._strategy.deepspeed
+2024/08/25 18:56:45 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.LinearLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 18:56:45 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.CosineAnnealingLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 18:57:07 - mmengine - INFO - Num train samples 4806
+2024/08/25 18:57:07 - mmengine - INFO - train example:
+2024/08/25 18:57:08 - mmengine - INFO -  <s><|im_start|> system
+You are an AI assistant whose name is InternLM (书生·浦语).<|im_end|><|im_start|>user
+ <img> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> 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<IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> </img>  
+请从这张聊天截图中提取结构化信息<|im_end|><|im_start|> assistant
+{
+  "dialog_name": "<对方正在输入...",
+  "conversation": [
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不是",
+      "message_bbox": {
+        "min_x": 917,
+        "max_x": 989,
+        "min_y": 253,
+        "max_y": 289
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "在淘宝里",
+      "message_bbox": {
+        "min_x": 839,
+        "max_x": 987,
+        "min_y": 370,
+        "max_y": 404
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不能发微信",
+      "message_bbox": {
+        "min_x": 801,
+        "max_x": 989,
+        "min_y": 485,
+        "max_y": 521
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "两字",
+      "message_bbox": {
+        "min_x": 915,
+        "max_x": 988,
+        "min_y": 601,
+        "max_y": 637
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "微信",
+      "message_bbox": {
+        "min_x": 916,
+        "max_x": 990,
+        "min_y": 718,
+        "max_y": 753
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "①微信",
+      "message_bbox": {
+        "min_x": 845,
+        "max_x": 988,
+        "min_y": 833,
+        "max_y": 869
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    }
+  ]
+}<|im_end|>
+2024/08/25 18:57:08 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
+2024/08/25 18:57:08 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
+2024/08/25 18:57:08 - mmengine - INFO - Checkpoints will be saved to /root/wangqun/work_dirs/internvl_ft_run_11_filter.

20240825_185458/vis_data/events.out.tfevents.1724583302.intern-studio-40019814.500905.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55fc75b32e2399ffd80e7e40b839be97927e44d55b08f4dc7e5db00beb8791f6
+size 4808

20240825_185538/20240825_185538.log

@@ -0,0 +1,555 @@
+2024/08/25 18:55:38 - mmengine - DEBUG - An `DeepSpeedStrategy` instance is built from registry, and its implementation can be found in xtuner.engine._strategy.deepspeed
+2024/08/25 18:55:39 - 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: 202466520
+    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:
+    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
+------------------------------------------------------------
+
+2024/08/25 18:55:39 - 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(
+    optimizer=dict(
+        betas=(
+            0.9,
+            0.999,
+        ),
+        lr=2e-05,
+        type='torch.optim.AdamW',
+        weight_decay=0.1),
+    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 = '/root/models/InternVL2_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=4,
+    gradient_clipping=1,
+    sequence_parallel_size=1,
+    train_micro_batch_size_per_gpu=4,
+    type='xtuner.engine.DeepSpeedStrategy')
+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 18:55:39 - mmengine - DEBUG - An `TensorboardVisBackend` instance is built from registry, and its implementation can be found in mmengine.visualization.vis_backend
+2024/08/25 18:55:39 - mmengine - DEBUG - An `Visualizer` instance is built from registry, and its implementation can be found in mmengine.visualization.visualizer
+2024/08/25 18:55:39 - 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
+2024/08/25 18:55:40 - mmengine - DEBUG - Get class `RuntimeInfoHook` from "hook" registry in "mmengine"
+2024/08/25 18:55:40 - mmengine - DEBUG - An `RuntimeInfoHook` instance is built from registry, and its implementation can be found in mmengine.hooks.runtime_info_hook
+2024/08/25 18:55:40 - mmengine - DEBUG - An `IterTimerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.iter_timer_hook
+2024/08/25 18:55:40 - mmengine - DEBUG - An `DistSamplerSeedHook` instance is built from registry, and its implementation can be found in mmengine.hooks.sampler_seed_hook
+2024/08/25 18:55:40 - mmengine - DEBUG - An `LoggerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.logger_hook
+2024/08/25 18:55:40 - mmengine - DEBUG - An `ParamSchedulerHook` instance is built from registry, and its implementation can be found in mmengine.hooks.param_scheduler_hook
+2024/08/25 18:55:40 - mmengine - DEBUG - An `CheckpointHook` instance is built from registry, and its implementation can be found in mmengine.hooks.checkpoint_hook
+2024/08/25 18:55:40 - mmengine - WARNING - Failed to search registry with scope "mmengine" in the "builder" registry tree. As a workaround, the current "builder" registry in "xtuner" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmengine" is a correct scope, or whether the registry is initialized.
+2024/08/25 18:55:41 - mmengine - DEBUG - An `from_pretrained` instance is built from registry, and its implementation can be found in transformers.models.auto.tokenization_auto
+2024/08/25 18:55:41 - mmengine - DEBUG - An `DatasetInfoHook` instance is built from registry, and its implementation can be found in xtuner.engine.hooks.dataset_info_hook
+2024/08/25 18:55:41 - mmengine - INFO - Hooks will be executed in the following order:
+before_run:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+before_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DatasetInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_train_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(NORMAL      ) DistSamplerSeedHook                
+ -------------------- 
+before_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_train_iter:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_train_epoch:
+(NORMAL      ) IterTimerHook                      
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_val_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_val_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_val_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_val_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+(LOW         ) ParamSchedulerHook                 
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+after_val:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_train:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(VERY_LOW    ) CheckpointHook                     
+ -------------------- 
+before_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) DatasetInfoHook                    
+ -------------------- 
+before_test_epoch:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+before_test_iter:
+(NORMAL      ) IterTimerHook                      
+ -------------------- 
+after_test_iter:
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test_epoch:
+(VERY_HIGH   ) RuntimeInfoHook                    
+(NORMAL      ) IterTimerHook                      
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+after_test:
+(VERY_HIGH   ) RuntimeInfoHook                    
+ -------------------- 
+after_run:
+(BELOW_NORMAL) LoggerHook                         
+ -------------------- 
+2024/08/25 18:55:41 - mmengine - DEBUG - An `FlexibleRunner` instance is built from registry, its implementation can be found inmmengine.runner._flexible_runner
+2024/08/25 18:56:14 - mmengine - INFO - Starting to loading data and calc length
+2024/08/25 18:56:14 - mmengine - INFO - =======Starting to process /root/data/screenshot_od/layout_ocr_multi.json =======
+2024/08/25 18:56:33 - mmengine - INFO - =======total 4806 samples of /root/data/screenshot_od/layout_ocr_multi.json=======
+2024/08/25 18:56:33 - mmengine - INFO - end loading data and calc length
+2024/08/25 18:56:33 - mmengine - INFO - =======total 4806 samples=======
+2024/08/25 18:56:33 - mmengine - DEBUG - An `InternVL_V1_5_Dataset` instance is built from registry, and its implementation can be found in xtuner.dataset.internvl_dataset
+2024/08/25 18:56:33 - mmengine - INFO - LengthGroupedSampler is used.
+2024/08/25 18:56:33 - mmengine - INFO - LengthGroupedSampler construction is complete, and the selected attribute is modality_length
+2024/08/25 18:56:33 - mmengine - DEBUG - An `LengthGroupedSampler` instance is built from registry, and its implementation can be found in xtuner.dataset.samplers.length_grouped
+2024/08/25 18:56:33 - mmengine - WARNING - Dataset InternVL_V1_5_Dataset has no metainfo. ``dataset_meta`` in visualizer will be None.
+2024/08/25 18:56:33 - mmengine - DEBUG - An `TrainLoop` instance is built from registry, and its implementation can be found in xtuner.engine.runner.loops
+2024/08/25 18:56:33 - mmengine - INFO - Start to load InternVL_V1_5 model.
+2024/08/25 18:56:33 - mmengine - DEBUG - Get class `BaseDataPreprocessor` from "model" registry in "mmengine"
+2024/08/25 18:56:33 - mmengine - DEBUG - An `BaseDataPreprocessor` instance is built from registry, and its implementation can be found in mmengine.model.base_model.data_preprocessor
+2024/08/25 18:56:41 - mmengine - DEBUG - An `LoraConfig` instance is built from registry, and its implementation can be found in peft.tuners.lora.config
+2024/08/25 18:56:42 - mmengine - INFO - InternVL_V1_5(
+  (data_preprocessor): BaseDataPreprocessor()
+  (model): InternVLChatModel(
+    (vision_model): InternVisionModel(
+      (embeddings): InternVisionEmbeddings(
+        (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))
+      )
+      (encoder): InternVisionEncoder(
+        (layers): ModuleList(
+          (0-23): 24 x InternVisionEncoderLayer(
+            (attn): InternAttention(
+              (qkv): Linear(in_features=1024, out_features=3072, bias=True)
+              (attn_drop): Dropout(p=0.0, inplace=False)
+              (proj_drop): Dropout(p=0.0, inplace=False)
+              (proj): Linear(in_features=1024, out_features=1024, bias=True)
+            )
+            (mlp): InternMLP(
+              (act): GELUActivation()
+              (fc1): Linear(in_features=1024, out_features=4096, bias=True)
+              (fc2): Linear(in_features=4096, out_features=1024, bias=True)
+            )
+            (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
+            (drop_path1): Identity()
+            (drop_path2): Identity()
+          )
+        )
+      )
+    )
+    (language_model): PeftModelForCausalLM(
+      (base_model): LoraModel(
+        (model): InternLM2ForCausalLM(
+          (model): InternLM2Model(
+            (tok_embeddings): Embedding(92553, 2048, padding_idx=2)
+            (layers): ModuleList(
+              (0-23): 24 x InternLM2DecoderLayer(
+                (attention): InternLM2Attention(
+                  (wqkv): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=4096, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=4096, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (wo): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (rotary_emb): InternLM2DynamicNTKScalingRotaryEmbedding()
+                )
+                (feed_forward): InternLM2MLP(
+                  (w1): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w3): lora.Linear(
+                    (base_layer): Linear4bit(in_features=2048, out_features=8192, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=2048, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=8192, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (w2): lora.Linear(
+                    (base_layer): Linear4bit(in_features=8192, out_features=2048, bias=False)
+                    (lora_dropout): ModuleDict(
+                      (default): Dropout(p=0.05, inplace=False)
+                    )
+                    (lora_A): ModuleDict(
+                      (default): Linear(in_features=8192, out_features=128, bias=False)
+                    )
+                    (lora_B): ModuleDict(
+                      (default): Linear(in_features=128, out_features=2048, bias=False)
+                    )
+                    (lora_embedding_A): ParameterDict()
+                    (lora_embedding_B): ParameterDict()
+                  )
+                  (act_fn): SiLU()
+                )
+                (attention_norm): InternLM2RMSNorm()
+                (ffn_norm): InternLM2RMSNorm()
+              )
+            )
+            (norm): InternLM2RMSNorm()
+          )
+          (output): lora.Linear(
+            (base_layer): Linear4bit(in_features=2048, out_features=92553, bias=False)
+            (lora_dropout): ModuleDict(
+              (default): Dropout(p=0.05, inplace=False)
+            )
+            (lora_A): ModuleDict(
+              (default): Linear(in_features=2048, out_features=128, bias=False)
+            )
+            (lora_B): ModuleDict(
+              (default): Linear(in_features=128, out_features=92553, bias=False)
+            )
+            (lora_embedding_A): ParameterDict()
+            (lora_embedding_B): ParameterDict()
+          )
+        )
+      )
+    )
+    (mlp1): Sequential(
+      (0): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)
+      (1): Linear(in_features=4096, out_features=2048, bias=True)
+      (2): GELU(approximate='none')
+      (3): Linear(in_features=2048, out_features=2048, bias=True)
+    )
+  )
+)
+2024/08/25 18:56:42 - mmengine - INFO - InternVL_V1_5 construction is complete
+2024/08/25 18:56:42 - mmengine - DEBUG - An `InternVL_V1_5` instance is built from registry, and its implementation can be found in xtuner.model.internvl
+2024/08/25 18:56:42 - mmengine - DEBUG - Get class `DefaultOptimWrapperConstructor` from "optimizer wrapper constructor" registry in "mmengine"
+2024/08/25 18:56:42 - mmengine - DEBUG - An `DefaultOptimWrapperConstructor` instance is built from registry, and its implementation can be found in mmengine.optim.optimizer.default_constructor
+2024/08/25 18:56:42 - mmengine - DEBUG - An `AdamW` instance is built from registry, and its implementation can be found in torch.optim.adamw
+2024/08/25 18:56:42 - mmengine - DEBUG - Get class `DeepSpeedOptimWrapper` from "optim_wrapper" registry in "mmengine"
+2024/08/25 18:56:42 - mmengine - DEBUG - An `DeepSpeedOptimWrapper` instance is built from registry, and its implementation can be found in mmengine._strategy.deepspeed
+2024/08/25 18:56:45 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.LinearLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 18:56:45 - mmengine - DEBUG - The `end` of <class 'mmengine.optim.scheduler.lr_scheduler.CosineAnnealingLR'> is not set. Use the max epochs/iters of train loop as default.
+2024/08/25 18:56:54 - mmengine - INFO - Num train samples 4806
+2024/08/25 18:56:54 - mmengine - INFO - train example:
+2024/08/25 18:56:55 - mmengine - INFO -  <s><|im_start|> system
+You are an AI assistant whose name is InternLM (书生·浦语).<|im_end|><|im_start|>user
+ <img> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> <IMG_CONTEXT> 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+请从这张聊天截图中提取结构化信息<|im_end|><|im_start|> assistant
+{
+  "dialog_name": "<对方正在输入...",
+  "conversation": [
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不是",
+      "message_bbox": {
+        "min_x": 917,
+        "max_x": 989,
+        "min_y": 253,
+        "max_y": 289
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "在淘宝里",
+      "message_bbox": {
+        "min_x": 839,
+        "max_x": 987,
+        "min_y": 370,
+        "max_y": 404
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "不能发微信",
+      "message_bbox": {
+        "min_x": 801,
+        "max_x": 989,
+        "min_y": 485,
+        "max_y": 521
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "两字",
+      "message_bbox": {
+        "min_x": 915,
+        "max_x": 988,
+        "min_y": 601,
+        "max_y": 637
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "微信",
+      "message_bbox": {
+        "min_x": 916,
+        "max_x": 990,
+        "min_y": 718,
+        "max_y": 753
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    },
+    {
+      "timestamp": "",
+      "speaker": "<对方正在输入...",
+      "content": "①微信",
+      "message_bbox": {
+        "min_x": 845,
+        "max_x": 988,
+        "min_y": 833,
+        "max_y": 869
+      },
+      "image": "",
+      "transfer": [],
+      "file": []
+    }
+  ]
+}<|im_end|>
+2024/08/25 18:56:55 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
+2024/08/25 18:56:55 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
+2024/08/25 18:56:55 - mmengine - INFO - Checkpoints will be saved to /root/wangqun/work_dirs/internvl_ft_run_11_filter.

20240825_185538/vis_data/events.out.tfevents.1724583340.intern-studio-40019814.500906.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d21475eb0e1f16967fa97393ff2def0f97723b72f754e536269dc360817e727
+size 4808

internvl_v2_internlm2_2b_qlora_finetune_copy.py

@@ -1,5 +1,5 @@
-accumulative_counts = 2
-batch_size = 1
+accumulative_counts = 4
+batch_size = 4
 betas = (
     0.9,
     0.999,
@@ -7,13 +7,13 @@ betas = (
 custom_hooks = [
     dict(
         tokenizer=dict(
-            pretrained_model_name_or_path='/root/models/InternVL2_2B',
+            pretrained_model_name_or_path='/data/wangqun/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/'
+data_path = '/home/wangqun/data/screenshot_od/layout_ocr_multi.json'
+data_root = '/home/wangqun/data/extracted_images'
 dataloader_num_workers = 4
 default_hooks = dict(
     checkpoint=dict(
@@ -33,13 +33,13 @@ 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/llava_images'
+image_folder = '/home/wangqun/data/extracted_imagesscreenshot_od/images'
 launcher = 'none'
 llava_dataset = dict(
-    data_paths='/root/data/screenshot_od/layout_ocr_multi.json',
-    image_folders='/root/data/llava_images',
+    data_paths='/home/wangqun/data/screenshot_od/layout_ocr_multi.json',
+    image_folders='/home/wangqun/data/extracted_imagesscreenshot_od/images',
     max_length=8192,
-    model_path='/root/models/InternVL2_2B',
+    model_path='/data/wangqun/models/internvl2-2B',
     template='xtuner.utils.PROMPT_TEMPLATE.internlm2_chat',
     type='xtuner.dataset.InternVL_V1_5_Dataset')
 load_from = None
@@ -59,7 +59,7 @@ model = dict(
         target_modules=None,
         task_type='CAUSAL_LM',
         type='peft.LoraConfig'),
-    model_path='/root/models/InternVL2_2B',
+    model_path='/data/wangqun/models/internvl2-2B',
     quantization_llm=True,
     quantization_vit=False,
     type='xtuner.model.InternVL_V1_5')
@@ -72,7 +72,7 @@ optim_wrapper = dict(
         ),
         lr=2e-05,
         type='torch.optim.AdamW',
-        weight_decay=0.05),
+        weight_decay=0.1),
     type='DeepSpeedOptimWrapper')
 param_scheduler = [
     dict(
@@ -90,7 +90,7 @@ param_scheduler = [
         eta_min=0.0,
         type='mmengine.optim.CosineAnnealingLR'),
 ]
-path = '/root/models/InternVL2_2B'
+path = '/data/wangqun/models/internvl2-2B'
 prompt_template = 'xtuner.utils.PROMPT_TEMPLATE.internlm2_chat'
 randomness = dict(deterministic=False, seed=None)
 resume = False
@@ -108,30 +108,30 @@ strategy = dict(
         zero_force_ds_cpu_optimizer=False,
         zero_optimization=dict(overlap_comm=True, stage=2)),
     exclude_frozen_parameters=True,
-    gradient_accumulation_steps=2,
+    gradient_accumulation_steps=4,
     gradient_clipping=1,
     sequence_parallel_size=1,
-    train_micro_batch_size_per_gpu=1,
+    train_micro_batch_size_per_gpu=4,
     type='xtuner.engine.DeepSpeedStrategy')
 tokenizer = dict(
-    pretrained_model_name_or_path='/root/models/InternVL2_2B',
+    pretrained_model_name_or_path='/data/wangqun/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=1,
+    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/llava_images',
+        data_paths='/home/wangqun/data/screenshot_od/layout_ocr_multi.json',
+        image_folders='/home/wangqun/data/extracted_imagesscreenshot_od/images',
         max_length=8192,
-        model_path='/root/models/InternVL2_2B',
+        model_path='/data/wangqun/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=2,
+        per_device_batch_size=16,
         type='xtuner.dataset.samplers.LengthGroupedSampler'))
 visualizer = dict(
     type='mmengine.visualization.Visualizer',
@@ -139,5 +139,5 @@ visualizer = dict(
         dict(type='mmengine.visualization.TensorboardVisBackend'),
     ])
 warmup_ratio = 0.03
-weight_decay = 0.05
-work_dir = '/root/wangqun/work_dirs/internvl_ft_run_8_filter'
+weight_decay = 0.1
+work_dir = '/home/wangqun/work_dirs/internvl_ft_run_11_filter'