| + deepspeed --master_port 18791 --module safe_rlhf.finetune --train_datasets inverse-json::/home/hansirui_1st/jiayi/resist/imdb_data/train/neg/2000/train.json --model_name_or_path /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000 --max_length 512 --trust_remote_code True --epochs 1 --per_device_train_batch_size 1 --per_device_eval_batch_size 4 --gradient_accumulation_steps 8 --gradient_checkpointing --learning_rate 1e-5 --lr_warmup_ratio 0 --weight_decay 0.0 --lr_scheduler_type constant --weight_decay 0.0 --seed 42 --output_dir /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000-Q2-2000 --log_type wandb --log_run_name imdb-tinyllama-3T-s3-Q1-1000-Q2-2000 --log_project Inverse_Alignment_IMDb --zero_stage 3 --offload none --bf16 True --tf32 True --save_16bit |
| nvcc warning : incompatible redefinition for option |
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| [rank6]:[W527 21:34:31.229177542 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 6] using GPU 6 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id. |
| [rank1]:[W527 21:34:31.240770999 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 1] using GPU 1 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id. |
| [rank3]:[W527 21:34:31.284071064 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 3] using GPU 3 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id. |
| [rank5]:[W527 21:34:31.284084092 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 5] using GPU 5 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id. |
| [rank4]:[W527 21:34:31.287711053 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 4] using GPU 4 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id. |
| [rank0]:[W527 21:34:31.291856714 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 0] using GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id. |
| [rank7]:[W527 21:34:31.303329923 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 7] using GPU 7 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id. |
| [rank2]:[W527 21:34:31.410154439 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 2] using GPU 2 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id. |
| loading configuration file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/config.json |
| loading configuration file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/config.json |
| loading configuration file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/config.json |
| loading configuration file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/config.json |
| loading configuration file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/config.json |
| loading configuration file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/config.json |
| loading configuration file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/config.json |
| loading configuration file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/config.json |
| Model config LlamaConfig { |
| "architectures": [ |
| "LlamaForCausalLM" |
| ], |
| "attention_bias": false, |
| "attention_dropout": 0.0, |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "head_dim": 64, |
| "hidden_act": "silu", |
| "hidden_size": 2048, |
| "initializer_range": 0.02, |
| "intermediate_size": 5632, |
| "max_position_embeddings": 2048, |
| "mlp_bias": false, |
| "model_type": "llama", |
| "num_attention_heads": 32, |
| "num_hidden_layers": 22, |
| "num_key_value_heads": 4, |
| "pad_token_id": 32000, |
| "pretraining_tp": 1, |
| "rms_norm_eps": 1e-05, |
| "rope_scaling": null, |
| "rope_theta": 10000.0, |
| "tie_word_embeddings": false, |
| "torch_dtype": "float32", |
| "transformers_version": "4.52.1", |
| "use_cache": true, |
| "vocab_size": 32001 |
| } |
|
|
| Model config LlamaConfig { |
| "architectures": [ |
| "LlamaForCausalLM" |
| ], |
| "attention_bias": false, |
| "attention_dropout": 0.0, |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "head_dim": 64, |
| "hidden_act": "silu", |
| "hidden_size": 2048, |
| "initializer_range": 0.02, |
| "intermediate_size": 5632, |
| "max_position_embeddings": 2048, |
| "mlp_bias": false, |
| "model_type": "llama", |
| "num_attention_heads": 32, |
| "num_hidden_layers": 22, |
| "num_key_value_heads": 4, |
| "pad_token_id": 32000, |
| "pretraining_tp": 1, |
| "rms_norm_eps": 1e-05, |
| "rope_scaling": null, |
| "rope_theta": 10000.0, |
| "tie_word_embeddings": false, |
| "torch_dtype": "float32", |
| "transformers_version": "4.52.1", |
| "use_cache": true, |
| "vocab_size": 32001 |
| } |
|
|
| Model config LlamaConfig { |
| "architectures": [ |
| "LlamaForCausalLM" |
| ], |
| "attention_bias": false, |
| "attention_dropout": 0.0, |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "head_dim": 64, |
| "hidden_act": "silu", |
| "hidden_size": 2048, |
| "initializer_range": 0.02, |
| "intermediate_size": 5632, |
| "max_position_embeddings": 2048, |
| "mlp_bias": false, |
| "model_type": "llama", |
| "num_attention_heads": 32, |
| "num_hidden_layers": 22, |
| "num_key_value_heads": 4, |
| "pad_token_id": 32000, |
| "pretraining_tp": 1, |
| "rms_norm_eps": 1e-05, |
| "rope_scaling": null, |
| "rope_theta": 10000.0, |
| "tie_word_embeddings": false, |
| "torch_dtype": "float32", |
| "transformers_version": "4.52.1", |
| "use_cache": true, |
| "vocab_size": 32001 |
| } |
|
|
| Model config LlamaConfig { |
| "architectures": [ |
| "LlamaForCausalLM" |
| ], |
| "attention_bias": false, |
| "attention_dropout": 0.0, |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "head_dim": 64, |
| "hidden_act": "silu", |
| "hidden_size": 2048, |
| "initializer_range": 0.02, |
| "intermediate_size": 5632, |
| "max_position_embeddings": 2048, |
| "mlp_bias": false, |
| "model_type": "llama", |
| "num_attention_heads": 32, |
| "num_hidden_layers": 22, |
| "num_key_value_heads": 4, |
| "pad_token_id": 32000, |
| "pretraining_tp": 1, |
| "rms_norm_eps": 1e-05, |
| "rope_scaling": null, |
| "rope_theta": 10000.0, |
| "tie_word_embeddings": false, |
| "torch_dtype": "float32", |
| "transformers_version": "4.52.1", |
| "use_cache": true, |
| "vocab_size": 32001 |
| } |
|
|
| Model config LlamaConfig { |
| "architectures": [ |
| "LlamaForCausalLM" |
| ], |
| "attention_bias": false, |
| "attention_dropout": 0.0, |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "head_dim": 64, |
| "hidden_act": "silu", |
| "hidden_size": 2048, |
| "initializer_range": 0.02, |
| "intermediate_size": 5632, |
| "max_position_embeddings": 2048, |
| "mlp_bias": false, |
| "model_type": "llama", |
| "num_attention_heads": 32, |
| "num_hidden_layers": 22, |
| "num_key_value_heads": 4, |
| "pad_token_id": 32000, |
| "pretraining_tp": 1, |
| "rms_norm_eps": 1e-05, |
| "rope_scaling": null, |
| "rope_theta": 10000.0, |
| "tie_word_embeddings": false, |
| "torch_dtype": "float32", |
| "transformers_version": "4.52.1", |
| "use_cache": true, |
| "vocab_size": 32001 |
| } |
|
|
| Model config LlamaConfig { |
| "architectures": [ |
| "LlamaForCausalLM" |
| ], |
| "attention_bias": false, |
| "attention_dropout": 0.0, |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "head_dim": 64, |
| "hidden_act": "silu", |
| "hidden_size": 2048, |
| "initializer_range": 0.02, |
| "intermediate_size": 5632, |
| "max_position_embeddings": 2048, |
| "mlp_bias": false, |
| "model_type": "llama", |
| "num_attention_heads": 32, |
| "num_hidden_layers": 22, |
| "num_key_value_heads": 4, |
| "pad_token_id": 32000, |
| "pretraining_tp": 1, |
| "rms_norm_eps": 1e-05, |
| "rope_scaling": null, |
| "rope_theta": 10000.0, |
| "tie_word_embeddings": false, |
| "torch_dtype": "float32", |
| "transformers_version": "4.52.1", |
| "use_cache": true, |
| "vocab_size": 32001 |
| } |
|
|
| Model config LlamaConfig { |
| "architectures": [ |
| "LlamaForCausalLM" |
| ], |
| "attention_bias": false, |
| "attention_dropout": 0.0, |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "head_dim": 64, |
| "hidden_act": "silu", |
| "hidden_size": 2048, |
| "initializer_range": 0.02, |
| "intermediate_size": 5632, |
| "max_position_embeddings": 2048, |
| "mlp_bias": false, |
| "model_type": "llama", |
| "num_attention_heads": 32, |
| "num_hidden_layers": 22, |
| "num_key_value_heads": 4, |
| "pad_token_id": 32000, |
| "pretraining_tp": 1, |
| "rms_norm_eps": 1e-05, |
| "rope_scaling": null, |
| "rope_theta": 10000.0, |
| "tie_word_embeddings": false, |
| "torch_dtype": "float32", |
| "transformers_version": "4.52.1", |
| "use_cache": true, |
| "vocab_size": 32001 |
| } |
|
|
| Model config LlamaConfig { |
| "architectures": [ |
| "LlamaForCausalLM" |
| ], |
| "attention_bias": false, |
| "attention_dropout": 0.0, |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "head_dim": 64, |
| "hidden_act": "silu", |
| "hidden_size": 2048, |
| "initializer_range": 0.02, |
| "intermediate_size": 5632, |
| "max_position_embeddings": 2048, |
| "mlp_bias": false, |
| "model_type": "llama", |
| "num_attention_heads": 32, |
| "num_hidden_layers": 22, |
| "num_key_value_heads": 4, |
| "pad_token_id": 32000, |
| "pretraining_tp": 1, |
| "rms_norm_eps": 1e-05, |
| "rope_scaling": null, |
| "rope_theta": 10000.0, |
| "tie_word_embeddings": false, |
| "torch_dtype": "float32", |
| "transformers_version": "4.52.1", |
| "use_cache": true, |
| "vocab_size": 32001 |
| } |
|
|
| loading weights file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/pytorch_model.bin |
| loading weights file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/pytorch_model.bin |
| loading weights file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/pytorch_model.bin |
| loading weights file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/pytorch_model.bin |
| loading weights file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/pytorch_model.bin |
| Will use torch_dtype=torch.float32 as defined in model |
| Will use torch_dtype=torch.float32 as defined in model |
| Will use torch_dtype=torch.float32 as defined in model |
| Will use torch_dtype=torch.float32 as defined in model |
| Instantiating LlamaForCausalLM model under default dtype torch.float32. |
| Instantiating LlamaForCausalLM model under default dtype torch.float32. |
| Will use torch_dtype=torch.float32 as defined in model |
| Instantiating LlamaForCausalLM model under default dtype torch.float32. |
| Instantiating LlamaForCausalLM model under default dtype torch.float32. |
| Instantiating LlamaForCausalLM model under default dtype torch.float32. |
| loading weights file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/pytorch_model.bin |
| Detected DeepSpeed ZeRO-3: activating zero.init() for this model |
| Detected DeepSpeed ZeRO-3: activating zero.init() for this model |
| Detected DeepSpeed ZeRO-3: activating zero.init() for this model |
| Detected DeepSpeed ZeRO-3: activating zero.init() for this model |
| Detected DeepSpeed ZeRO-3: activating zero.init() for this model |
| Will use torch_dtype=torch.float32 as defined in model |
| Instantiating LlamaForCausalLM model under default dtype torch.float32. |
| Detected DeepSpeed ZeRO-3: activating zero.init() for this model |
| loading weights file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/pytorch_model.bin |
| Will use torch_dtype=torch.float32 as defined in model |
| Instantiating LlamaForCausalLM model under default dtype torch.float32. |
| Detected DeepSpeed ZeRO-3: activating zero.init() for this model |
| loading weights file /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000/pytorch_model.bin |
| Will use torch_dtype=torch.float32 as defined in model |
| Instantiating LlamaForCausalLM model under default dtype torch.float32. |
| Detected DeepSpeed ZeRO-3: activating zero.init() for this model |
| Generate config GenerationConfig { |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "pad_token_id": 32000 |
| } |
|
|
| Generate config GenerationConfig { |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "pad_token_id": 32000 |
| } |
|
|
| Generate config GenerationConfig { |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "pad_token_id": 32000 |
| } |
|
|
| Generate config GenerationConfig { |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "pad_token_id": 32000 |
| } |
|
|
| Generate config GenerationConfig { |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "pad_token_id": 32000 |
| } |
|
|
| Generate config GenerationConfig { |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "pad_token_id": 32000 |
| } |
|
|
| Generate config GenerationConfig { |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "pad_token_id": 32000 |
| } |
|
|
| Generate config GenerationConfig { |
| "bos_token_id": 1, |
| "eos_token_id": 2, |
| "pad_token_id": 32000 |
| } |
|
|
| All model checkpoint weights were used when initializing LlamaForCausalLM. |
|
|
| All the weights of LlamaForCausalLM were initialized from the model checkpoint at /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000. |
| If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. |
| Generation config file not found, using a generation config created from the model config. |
| All model checkpoint weights were used when initializing LlamaForCausalLM. |
|
|
| All the weights of LlamaForCausalLM were initialized from the model checkpoint at /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000. |
| If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. |
| All model checkpoint weights were used when initializing LlamaForCausalLM. |
|
|
| All the weights of LlamaForCausalLM were initialized from the model checkpoint at /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000. |
| If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. |
| Generation config file not found, using a generation config created from the model config. |
| Generation config file not found, using a generation config created from the model config. |
| All model checkpoint weights were used when initializing LlamaForCausalLM. |
|
|
| All the weights of LlamaForCausalLM were initialized from the model checkpoint at /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000. |
| If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. |
| All model checkpoint weights were used when initializing LlamaForCausalLM. |
|
|
| All the weights of LlamaForCausalLM were initialized from the model checkpoint at /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000. |
| If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. |
| All model checkpoint weights were used when initializing LlamaForCausalLM. |
|
|
| All the weights of LlamaForCausalLM were initialized from the model checkpoint at /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000. |
| If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. |
| All model checkpoint weights were used when initializing LlamaForCausalLM. |
|
|
| All the weights of LlamaForCausalLM were initialized from the model checkpoint at /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000. |
| If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. |
| Generation config file not found, using a generation config created from the model config. |
| Generation config file not found, using a generation config created from the model config. |
| Generation config file not found, using a generation config created from the model config. |
| Generation config file not found, using a generation config created from the model config. |
| loading file tokenizer.model |
| loading file tokenizer.json |
| loading file added_tokens.json |
| loading file special_tokens_map.json |
| loading file tokenizer_config.json |
| loading file chat_template.jinja |
| loading file tokenizer.model |
| loading file tokenizer.json |
| loading file added_tokens.json |
| loading file special_tokens_map.json |
| loading file tokenizer_config.json |
| loading file chat_template.jinja |
| loading file tokenizer.model |
| loading file tokenizer.json |
| loading file added_tokens.json |
| loading file special_tokens_map.json |
| loading file tokenizer_config.json |
| loading file chat_template.jinja |
| loading file tokenizer.model |
| loading file tokenizer.json |
| loading file added_tokens.json |
| loading file special_tokens_map.json |
| loading file tokenizer_config.json |
| loading file chat_template.jinja |
| loading file tokenizer.model |
| loading file tokenizer.json |
| loading file added_tokens.json |
| loading file tokenizer.model |
| loading file special_tokens_map.json |
| loading file tokenizer_config.json |
| loading file tokenizer.json |
| loading file chat_template.jinja |
| loading file added_tokens.json |
| loading file special_tokens_map.json |
| loading file tokenizer_config.json |
| loading file chat_template.jinja |
| loading file tokenizer.model |
| loading file tokenizer.json |
| loading file added_tokens.json |
| loading file special_tokens_map.json |
| loading file tokenizer_config.json |
| loading file chat_template.jinja |
| All model checkpoint weights were used when initializing LlamaForCausalLM. |
|
|
| All the weights of LlamaForCausalLM were initialized from the model checkpoint at /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000. |
| If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. |
| Generation config file not found, using a generation config created from the model config. |
| loading file tokenizer.model |
| loading file tokenizer.json |
| loading file added_tokens.json |
| loading file special_tokens_map.json |
| loading file tokenizer_config.json |
| loading file chat_template.jinja |
| Using /home/hansirui_1st/.cache/torch_extensions/py311_cu124 as PyTorch extensions root... |
| Using /home/hansirui_1st/.cache/torch_extensions/py311_cu124 as PyTorch extensions root... |
| Using /home/hansirui_1st/.cache/torch_extensions/py311_cu124 as PyTorch extensions root... |
| Using /home/hansirui_1st/.cache/torch_extensions/py311_cu124 as PyTorch extensions root... |
| Using /home/hansirui_1st/.cache/torch_extensions/py311_cu124 as PyTorch extensions root... |
| Using /home/hansirui_1st/.cache/torch_extensions/py311_cu124 as PyTorch extensions root... |
| Using /home/hansirui_1st/.cache/torch_extensions/py311_cu124 as PyTorch extensions root... |
| Using /home/hansirui_1st/.cache/torch_extensions/py311_cu124 as PyTorch extensions root... |
| Detected CUDA files, patching ldflags |
| Emitting ninja build file /home/hansirui_1st/.cache/torch_extensions/py311_cu124/fused_adam/build.ninja... |
| /aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/torch/utils/cpp_extension.py:2059: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. |
| If this is not desired, please set os.environ[ |
| warnings.warn( |
| Building extension module fused_adam... |
| Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N) |
| Loading extension module fused_adam... |
| Loading extension module fused_adam... |
| Loading extension module fused_adam... |
| Loading extension module fused_adam... |
| Loading extension module fused_adam... |
| Loading extension module fused_adam... |
| Loading extension module fused_adam... |
| Loading extension module fused_adam... |
| `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`. |
| `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`. |
| `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`. |
| `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`. |
| `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`. |
| `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`. |
| `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`. |
| wandb: Currently logged in as: xtom to https://api.wandb.ai. Use `wandb login --relogin` to force relogin |
| wandb: Tracking run with wandb version 0.19.11 |
| wandb: Run data is saved locally in /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000-Q2-2000/wandb/run-20250527_213449-3h6xaqt7 |
| wandb: Run `wandb offline` to turn off syncing. |
| wandb: Syncing run imdb-tinyllama-3T-s3-Q1-1000-Q2-2000 |
| wandb: βοΈ View project at https://wandb.ai/xtom/Inverse_Alignment_IMDb |
| wandb: π View run at https://wandb.ai/xtom/Inverse_Alignment_IMDb/runs/3h6xaqt7 |
|
Training 1/1 epoch: 0%| | 0/250 [00:00<?, ?it/s]`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`. |
|
Training 1/1 epoch (loss 2.7354): 0%| | 0/250 [00:10<?, ?it/s]
Training 1/1 epoch (loss 2.7354): 0%| | 1/250 [00:10<42:50, 10.32s/it]
Training 1/1 epoch (loss 2.7754): 0%| | 1/250 [00:12<42:50, 10.32s/it]
Training 1/1 epoch (loss 2.7754): 1%| | 2/250 [00:12<22:59, 5.56s/it]
Training 1/1 epoch (loss 2.7038): 1%| | 2/250 [00:13<22:59, 5.56s/it]
Training 1/1 epoch (loss 2.7038): 1%| | 3/250 [00:13<14:28, 3.51s/it]
Training 1/1 epoch (loss 2.5552): 1%| | 3/250 [00:15<14:28, 3.51s/it]
Training 1/1 epoch (loss 2.5552): 2%|β | 4/250 [00:15<11:18, 2.76s/it]
Training 1/1 epoch (loss 2.6441): 2%|β | 4/250 [00:17<11:18, 2.76s/it]
Training 1/1 epoch (loss 2.6441): 2%|β | 5/250 [00:17<10:15, 2.51s/it]
Training 1/1 epoch (loss 3.0312): 2%|β | 5/250 [00:17<10:15, 2.51s/it]
Training 1/1 epoch (loss 3.0312): 2%|β | 6/250 [00:17<07:23, 1.82s/it]
Training 1/1 epoch (loss 2.7856): 2%|β | 6/250 [00:19<07:23, 1.82s/it]
Training 1/1 epoch (loss 2.7856): 3%|β | 7/250 [00:19<06:54, 1.70s/it]
Training 1/1 epoch (loss 2.6177): 3%|β | 7/250 [00:21<06:54, 1.70s/it]
Training 1/1 epoch (loss 2.6177): 3%|β | 8/250 [00:21<07:14, 1.80s/it]
Training 1/1 epoch (loss 2.7280): 3%|β | 8/250 [00:22<07:14, 1.80s/it]
Training 1/1 epoch (loss 2.7280): 4%|β | 9/250 [00:22<06:31, 1.62s/it]
Training 1/1 epoch (loss 2.6027): 4%|β | 9/250 [00:24<06:31, 1.62s/it]
Training 1/1 epoch (loss 2.6027): 4%|β | 10/250 [00:24<07:23, 1.85s/it]
Training 1/1 epoch (loss 2.4057): 4%|β | 10/250 [00:25<07:23, 1.85s/it]
Training 1/1 epoch (loss 2.4057): 4%|β | 11/250 [00:25<06:07, 1.54s/it]
Training 1/1 epoch (loss 2.5840): 4%|β | 11/250 [00:27<06:07, 1.54s/it]
Training 1/1 epoch (loss 2.5840): 5%|β | 12/250 [00:27<05:54, 1.49s/it]
Training 1/1 epoch (loss 2.9646): 5%|β | 12/250 [00:28<05:54, 1.49s/it]
Training 1/1 epoch (loss 2.9646): 5%|β | 13/250 [00:28<05:53, 1.49s/it]
Training 1/1 epoch (loss 2.6338): 5%|β | 13/250 [00:29<05:53, 1.49s/it]
Training 1/1 epoch (loss 2.6338): 6%|β | 14/250 [00:29<05:21, 1.36s/it]
Training 1/1 epoch (loss 2.4952): 6%|β | 14/250 [00:32<05:21, 1.36s/it]
Training 1/1 epoch (loss 2.4952): 6%|β | 15/250 [00:32<06:39, 1.70s/it]
Training 1/1 epoch (loss 2.5450): 6%|β | 15/250 [00:34<06:39, 1.70s/it]
Training 1/1 epoch (loss 2.5450): 6%|β | 16/250 [00:34<07:07, 1.83s/it]
Training 1/1 epoch (loss 2.7685): 6%|β | 16/250 [00:34<07:07, 1.83s/it]
Training 1/1 epoch (loss 2.7685): 7%|β | 17/250 [00:34<05:33, 1.43s/it]
Training 1/1 epoch (loss 2.5476): 7%|β | 17/250 [00:37<05:33, 1.43s/it]
Training 1/1 epoch (loss 2.5476): 7%|β | 18/250 [00:37<06:44, 1.74s/it]
Training 1/1 epoch (loss 2.5684): 7%|β | 18/250 [00:38<06:44, 1.74s/it]
Training 1/1 epoch (loss 2.5684): 8%|β | 19/250 [00:38<06:38, 1.72s/it]
Training 1/1 epoch (loss 2.7124): 8%|β | 19/250 [00:39<06:38, 1.72s/it]
Training 1/1 epoch (loss 2.7124): 8%|β | 20/250 [00:39<05:38, 1.47s/it]
Training 1/1 epoch (loss 2.7044): 8%|β | 20/250 [00:42<05:38, 1.47s/it]
Training 1/1 epoch (loss 2.7044): 8%|β | 21/250 [00:42<06:30, 1.71s/it]
Training 1/1 epoch (loss 2.8981): 8%|β | 21/250 [00:43<06:30, 1.71s/it]
Training 1/1 epoch (loss 2.8981): 9%|β | 22/250 [00:43<05:52, 1.55s/it]
Training 1/1 epoch (loss 2.7073): 9%|β | 22/250 [00:44<05:52, 1.55s/it]
Training 1/1 epoch (loss 2.7073): 9%|β | 23/250 [00:44<05:39, 1.49s/it]
Training 1/1 epoch (loss 2.8618): 9%|β | 23/250 [00:46<05:39, 1.49s/it]
Training 1/1 epoch (loss 2.8618): 10%|β | 24/250 [00:46<06:01, 1.60s/it]
Training 1/1 epoch (loss 2.6291): 10%|β | 24/250 [00:47<06:01, 1.60s/it]
Training 1/1 epoch (loss 2.6291): 10%|β | 25/250 [00:47<05:10, 1.38s/it]
Training 1/1 epoch (loss 2.6759): 10%|β | 25/250 [00:48<05:10, 1.38s/it]
Training 1/1 epoch (loss 2.6759): 10%|β | 26/250 [00:48<05:15, 1.41s/it]
Training 1/1 epoch (loss 2.4780): 10%|β | 26/250 [00:50<05:15, 1.41s/it]
Training 1/1 epoch (loss 2.4780): 11%|β | 27/250 [00:50<05:12, 1.40s/it]
Training 1/1 epoch (loss 2.6035): 11%|β | 27/250 [00:51<05:12, 1.40s/it]
Training 1/1 epoch (loss 2.6035): 11%|β | 28/250 [00:51<05:04, 1.37s/it]
Training 1/1 epoch (loss 2.6512): 11%|β | 28/250 [00:52<05:04, 1.37s/it]
Training 1/1 epoch (loss 2.6512): 12%|ββ | 29/250 [00:52<04:51, 1.32s/it]
Training 1/1 epoch (loss 2.8163): 12%|ββ | 29/250 [00:53<04:51, 1.32s/it]
Training 1/1 epoch (loss 2.8163): 12%|ββ | 30/250 [00:53<04:49, 1.31s/it]
Training 1/1 epoch (loss 2.6592): 12%|ββ | 30/250 [00:54<04:49, 1.31s/it]
Training 1/1 epoch (loss 2.6592): 12%|ββ | 31/250 [00:54<03:51, 1.06s/it]
Training 1/1 epoch (loss 2.6847): 12%|ββ | 31/250 [00:56<03:51, 1.06s/it]
Training 1/1 epoch (loss 2.6847): 13%|ββ | 32/250 [00:56<04:34, 1.26s/it]
Training 1/1 epoch (loss 2.8169): 13%|ββ | 32/250 [00:58<04:34, 1.26s/it]
Training 1/1 epoch (loss 2.8169): 13%|ββ | 33/250 [00:58<05:52, 1.63s/it]
Training 1/1 epoch (loss 2.5125): 13%|ββ | 33/250 [00:59<05:52, 1.63s/it]
Training 1/1 epoch (loss 2.5125): 14%|ββ | 34/250 [00:59<04:33, 1.26s/it]
Training 1/1 epoch (loss 2.4245): 14%|ββ | 34/250 [01:01<04:33, 1.26s/it]
Training 1/1 epoch (loss 2.4245): 14%|ββ | 35/250 [01:01<05:21, 1.49s/it]
Training 1/1 epoch (loss 2.8705): 14%|ββ | 35/250 [01:02<05:21, 1.49s/it]
Training 1/1 epoch (loss 2.8705): 14%|ββ | 36/250 [01:02<05:12, 1.46s/it]
Training 1/1 epoch (loss 2.5758): 14%|ββ | 36/250 [01:02<05:12, 1.46s/it]
Training 1/1 epoch (loss 2.5758): 15%|ββ | 37/250 [01:02<04:12, 1.19s/it]
Training 1/1 epoch (loss 2.6109): 15%|ββ | 37/250 [01:03<04:12, 1.19s/it]
Training 1/1 epoch (loss 2.6109): 15%|ββ | 38/250 [01:03<03:58, 1.12s/it]
Training 1/1 epoch (loss 2.6952): 15%|ββ | 38/250 [01:05<03:58, 1.12s/it]
Training 1/1 epoch (loss 2.6952): 16%|ββ | 39/250 [01:05<04:20, 1.24s/it]
Training 1/1 epoch (loss 2.3491): 16%|ββ | 39/250 [01:06<04:20, 1.24s/it]
Training 1/1 epoch (loss 2.3491): 16%|ββ | 40/250 [01:06<03:56, 1.13s/it]
Training 1/1 epoch (loss 2.6515): 16%|ββ | 40/250 [01:07<03:56, 1.13s/it]
Training 1/1 epoch (loss 2.6515): 16%|ββ | 41/250 [01:07<04:24, 1.27s/it]
Training 1/1 epoch (loss 2.6031): 16%|ββ | 41/250 [01:09<04:24, 1.27s/it]
Training 1/1 epoch (loss 2.6031): 17%|ββ | 42/250 [01:09<04:51, 1.40s/it]
Training 1/1 epoch (loss 2.4837): 17%|ββ | 42/250 [01:10<04:51, 1.40s/it]
Training 1/1 epoch (loss 2.4837): 17%|ββ | 43/250 [01:10<04:24, 1.28s/it]
Training 1/1 epoch (loss 2.5378): 17%|ββ | 43/250 [01:12<04:24, 1.28s/it]
Training 1/1 epoch (loss 2.5378): 18%|ββ | 44/250 [01:12<05:05, 1.48s/it]
Training 1/1 epoch (loss 2.6984): 18%|ββ | 44/250 [01:13<05:05, 1.48s/it]
Training 1/1 epoch (loss 2.6984): 18%|ββ | 45/250 [01:13<04:31, 1.32s/it]
Training 1/1 epoch (loss 2.7438): 18%|ββ | 45/250 [01:15<04:31, 1.32s/it]
Training 1/1 epoch (loss 2.7438): 18%|ββ | 46/250 [01:15<05:28, 1.61s/it]
Training 1/1 epoch (loss 2.7564): 18%|ββ | 46/250 [01:17<05:28, 1.61s/it]
Training 1/1 epoch (loss 2.7564): 19%|ββ | 47/250 [01:17<06:00, 1.77s/it]
Training 1/1 epoch (loss 2.7519): 19%|ββ | 47/250 [01:19<06:00, 1.77s/it]
Training 1/1 epoch (loss 2.7519): 19%|ββ | 48/250 [01:19<05:18, 1.58s/it]
Training 1/1 epoch (loss 2.5844): 19%|ββ | 48/250 [01:20<05:18, 1.58s/it]
Training 1/1 epoch (loss 2.5844): 20%|ββ | 49/250 [01:20<05:11, 1.55s/it]
Training 1/1 epoch (loss 2.6382): 20%|ββ | 49/250 [01:22<05:11, 1.55s/it]
Training 1/1 epoch (loss 2.6382): 20%|ββ | 50/250 [01:22<05:22, 1.61s/it]
Training 1/1 epoch (loss 2.7297): 20%|ββ | 50/250 [01:23<05:22, 1.61s/it]
Training 1/1 epoch (loss 2.7297): 20%|ββ | 51/250 [01:23<04:34, 1.38s/it]
Training 1/1 epoch (loss 2.6724): 20%|ββ | 51/250 [01:25<04:34, 1.38s/it]
Training 1/1 epoch (loss 2.6724): 21%|ββ | 52/250 [01:25<05:00, 1.52s/it]
Training 1/1 epoch (loss 2.5736): 21%|ββ | 52/250 [01:26<05:00, 1.52s/it]
Training 1/1 epoch (loss 2.5736): 21%|ββ | 53/250 [01:26<04:30, 1.37s/it]
Training 1/1 epoch (loss 2.7633): 21%|ββ | 53/250 [01:27<04:30, 1.37s/it]
Training 1/1 epoch (loss 2.7633): 22%|βββ | 54/250 [01:27<04:11, 1.28s/it]
Training 1/1 epoch (loss 2.4767): 22%|βββ | 54/250 [01:28<04:11, 1.28s/it]
Training 1/1 epoch (loss 2.4767): 22%|βββ | 55/250 [01:28<04:13, 1.30s/it]
Training 1/1 epoch (loss 2.4521): 22%|βββ | 55/250 [01:29<04:13, 1.30s/it]
Training 1/1 epoch (loss 2.4521): 22%|βββ | 56/250 [01:29<04:16, 1.32s/it]
Training 1/1 epoch (loss 2.5960): 22%|βββ | 56/250 [01:31<04:16, 1.32s/it]
Training 1/1 epoch (loss 2.5960): 23%|βββ | 57/250 [01:31<04:12, 1.31s/it]
Training 1/1 epoch (loss 2.8843): 23%|βββ | 57/250 [01:32<04:12, 1.31s/it]
Training 1/1 epoch (loss 2.8843): 23%|βββ | 58/250 [01:32<04:39, 1.45s/it]
Training 1/1 epoch (loss 2.8867): 23%|βββ | 58/250 [01:33<04:39, 1.45s/it]
Training 1/1 epoch (loss 2.8867): 24%|βββ | 59/250 [01:33<03:42, 1.17s/it]
Training 1/1 epoch (loss 2.5548): 24%|βββ | 59/250 [01:34<03:42, 1.17s/it]
Training 1/1 epoch (loss 2.5548): 24%|βββ | 60/250 [01:34<03:55, 1.24s/it]
Training 1/1 epoch (loss 2.5486): 24%|βββ | 60/250 [01:37<03:55, 1.24s/it]
Training 1/1 epoch (loss 2.5486): 24%|βββ | 61/250 [01:37<04:58, 1.58s/it]
Training 1/1 epoch (loss 2.7071): 24%|βββ | 61/250 [01:37<04:58, 1.58s/it]
Training 1/1 epoch (loss 2.7071): 25%|βββ | 62/250 [01:37<04:12, 1.34s/it]
Training 1/1 epoch (loss 2.7145): 25%|βββ | 62/250 [01:39<04:12, 1.34s/it]
Training 1/1 epoch (loss 2.7145): 25%|βββ | 63/250 [01:39<04:38, 1.49s/it]
Training 1/1 epoch (loss 2.5459): 25%|βββ | 63/250 [01:41<04:38, 1.49s/it]
Training 1/1 epoch (loss 2.5459): 26%|βββ | 64/250 [01:41<04:33, 1.47s/it]
Training 1/1 epoch (loss 2.6659): 26%|βββ | 64/250 [01:43<04:33, 1.47s/it]
Training 1/1 epoch (loss 2.6659): 26%|βββ | 65/250 [01:43<05:27, 1.77s/it]
Training 1/1 epoch (loss 2.6412): 26%|βββ | 65/250 [01:46<05:27, 1.77s/it]
Training 1/1 epoch (loss 2.6412): 26%|βββ | 66/250 [01:46<06:03, 1.97s/it]
Training 1/1 epoch (loss 2.6346): 26%|βββ | 66/250 [01:46<06:03, 1.97s/it]
Training 1/1 epoch (loss 2.6346): 27%|βββ | 67/250 [01:46<04:40, 1.53s/it]
Training 1/1 epoch (loss 2.4868): 27%|βββ | 67/250 [01:48<04:40, 1.53s/it]
Training 1/1 epoch (loss 2.4868): 27%|βββ | 68/250 [01:48<05:09, 1.70s/it]
Training 1/1 epoch (loss 2.4887): 27%|βββ | 68/250 [01:50<05:09, 1.70s/it]
Training 1/1 epoch (loss 2.4887): 28%|βββ | 69/250 [01:50<04:50, 1.60s/it]
Training 1/1 epoch (loss 2.8012): 28%|βββ | 69/250 [01:50<04:50, 1.60s/it]
Training 1/1 epoch (loss 2.8012): 28%|βββ | 70/250 [01:50<03:55, 1.31s/it]
Training 1/1 epoch (loss 2.5448): 28%|βββ | 70/250 [01:52<03:55, 1.31s/it]
Training 1/1 epoch (loss 2.5448): 28%|βββ | 71/250 [01:52<04:18, 1.45s/it]
Training 1/1 epoch (loss 2.6839): 28%|βββ | 71/250 [01:54<04:18, 1.45s/it]
Training 1/1 epoch (loss 2.6839): 29%|βββ | 72/250 [01:54<04:46, 1.61s/it]
Training 1/1 epoch (loss 2.4258): 29%|βββ | 72/250 [01:55<04:46, 1.61s/it]
Training 1/1 epoch (loss 2.4258): 29%|βββ | 73/250 [01:55<03:54, 1.32s/it]
Training 1/1 epoch (loss 2.5064): 29%|βββ | 73/250 [01:57<03:54, 1.32s/it]
Training 1/1 epoch (loss 2.5064): 30%|βββ | 74/250 [01:57<04:23, 1.50s/it]
Training 1/1 epoch (loss 2.7643): 30%|βββ | 74/250 [01:58<04:23, 1.50s/it]
Training 1/1 epoch (loss 2.7643): 30%|βββ | 75/250 [01:58<04:10, 1.43s/it]
Training 1/1 epoch (loss 2.4737): 30%|βββ | 75/250 [01:59<04:10, 1.43s/it]
Training 1/1 epoch (loss 2.4737): 30%|βββ | 76/250 [01:59<04:03, 1.40s/it]
Training 1/1 epoch (loss 2.4158): 30%|βββ | 76/250 [02:01<04:03, 1.40s/it]
Training 1/1 epoch (loss 2.4158): 31%|βββ | 77/250 [02:01<04:09, 1.44s/it]
Training 1/1 epoch (loss 2.5450): 31%|βββ | 77/250 [02:02<04:09, 1.44s/it]
Training 1/1 epoch (loss 2.5450): 31%|βββ | 78/250 [02:02<03:46, 1.32s/it]
Training 1/1 epoch (loss 2.6406): 31%|βββ | 78/250 [02:03<03:46, 1.32s/it]
Training 1/1 epoch (loss 2.6406): 32%|ββββ | 79/250 [02:03<03:20, 1.17s/it]
Training 1/1 epoch (loss 2.6024): 32%|ββββ | 79/250 [02:05<03:20, 1.17s/it]
Training 1/1 epoch (loss 2.6024): 32%|ββββ | 80/250 [02:05<04:10, 1.47s/it]
Training 1/1 epoch (loss 2.7406): 32%|ββββ | 80/250 [02:06<04:10, 1.47s/it]
Training 1/1 epoch (loss 2.7406): 32%|ββββ | 81/250 [02:06<03:37, 1.29s/it]
Training 1/1 epoch (loss 2.8017): 32%|ββββ | 81/250 [02:07<03:37, 1.29s/it]
Training 1/1 epoch (loss 2.8017): 33%|ββββ | 82/250 [02:07<03:44, 1.34s/it]
Training 1/1 epoch (loss 2.6850): 33%|ββββ | 82/250 [02:09<03:44, 1.34s/it]
Training 1/1 epoch (loss 2.6850): 33%|ββββ | 83/250 [02:09<04:13, 1.52s/it]
Training 1/1 epoch (loss 2.5334): 33%|ββββ | 83/250 [02:09<04:13, 1.52s/it]
Training 1/1 epoch (loss 2.5334): 34%|ββββ | 84/250 [02:09<03:18, 1.20s/it]
Training 1/1 epoch (loss 2.7648): 34%|ββββ | 84/250 [02:11<03:18, 1.20s/it]
Training 1/1 epoch (loss 2.7648): 34%|ββββ | 85/250 [02:11<03:25, 1.25s/it]
Training 1/1 epoch (loss 2.6266): 34%|ββββ | 85/250 [02:13<03:25, 1.25s/it]
Training 1/1 epoch (loss 2.6266): 34%|ββββ | 86/250 [02:13<04:23, 1.61s/it]
Training 1/1 epoch (loss 2.6452): 34%|ββββ | 86/250 [02:14<04:23, 1.61s/it]
Training 1/1 epoch (loss 2.6452): 35%|ββββ | 87/250 [02:14<03:41, 1.36s/it]
Training 1/1 epoch (loss 2.6008): 35%|ββββ | 87/250 [02:16<03:41, 1.36s/it]
Training 1/1 epoch (loss 2.6008): 35%|ββββ | 88/250 [02:16<04:24, 1.63s/it]
Training 1/1 epoch (loss 2.6767): 35%|ββββ | 88/250 [02:18<04:24, 1.63s/it]
Training 1/1 epoch (loss 2.6767): 36%|ββββ | 89/250 [02:18<04:17, 1.60s/it]
Training 1/1 epoch (loss 2.7794): 36%|ββββ | 89/250 [02:19<04:17, 1.60s/it]
Training 1/1 epoch (loss 2.7794): 36%|ββββ | 90/250 [02:19<03:39, 1.37s/it]
Training 1/1 epoch (loss 2.7104): 36%|ββββ | 90/250 [02:21<03:39, 1.37s/it]
Training 1/1 epoch (loss 2.7104): 36%|ββββ | 91/250 [02:21<04:31, 1.71s/it]
Training 1/1 epoch (loss 2.5006): 36%|ββββ | 91/250 [02:23<04:31, 1.71s/it]
Training 1/1 epoch (loss 2.5006): 37%|ββββ | 92/250 [02:23<04:26, 1.68s/it]
Training 1/1 epoch (loss 2.3629): 37%|ββββ | 92/250 [02:24<04:26, 1.68s/it]
Training 1/1 epoch (loss 2.3629): 37%|ββββ | 93/250 [02:24<04:18, 1.65s/it]
Training 1/1 epoch (loss 2.6975): 37%|ββββ | 93/250 [02:27<04:18, 1.65s/it]
Training 1/1 epoch (loss 2.6975): 38%|ββββ | 94/250 [02:27<04:56, 1.90s/it]
Training 1/1 epoch (loss 2.7309): 38%|ββββ | 94/250 [02:28<04:56, 1.90s/it]
Training 1/1 epoch (loss 2.7309): 38%|ββββ | 95/250 [02:28<04:04, 1.58s/it]
Training 1/1 epoch (loss 2.6027): 38%|ββββ | 95/250 [02:29<04:04, 1.58s/it]
Training 1/1 epoch (loss 2.6027): 38%|ββββ | 96/250 [02:29<04:00, 1.56s/it]
Training 1/1 epoch (loss 2.6533): 38%|ββββ | 96/250 [02:31<04:00, 1.56s/it]
Training 1/1 epoch (loss 2.6533): 39%|ββββ | 97/250 [02:31<03:54, 1.53s/it]
Training 1/1 epoch (loss 2.6402): 39%|ββββ | 97/250 [02:31<03:54, 1.53s/it]
Training 1/1 epoch (loss 2.6402): 39%|ββββ | 98/250 [02:31<03:07, 1.23s/it]
Training 1/1 epoch (loss 2.6539): 39%|ββββ | 98/250 [02:33<03:07, 1.23s/it]
Training 1/1 epoch (loss 2.6539): 40%|ββββ | 99/250 [02:33<03:36, 1.43s/it]
Training 1/1 epoch (loss 2.6743): 40%|ββββ | 99/250 [02:35<03:36, 1.43s/it]
Training 1/1 epoch (loss 2.6743): 40%|ββββ | 100/250 [02:35<03:41, 1.48s/it]
Training 1/1 epoch (loss 2.6086): 40%|ββββ | 100/250 [02:35<03:41, 1.48s/it]
Training 1/1 epoch (loss 2.6086): 40%|ββββ | 101/250 [02:35<02:53, 1.16s/it]
Training 1/1 epoch (loss 2.6617): 40%|ββββ | 101/250 [02:37<02:53, 1.16s/it]
Training 1/1 epoch (loss 2.6617): 41%|ββββ | 102/250 [02:37<03:20, 1.36s/it]
Training 1/1 epoch (loss 2.7352): 41%|ββββ | 102/250 [02:38<03:20, 1.36s/it]
Training 1/1 epoch (loss 2.7352): 41%|ββββ | 103/250 [02:38<03:30, 1.43s/it]
Training 1/1 epoch (loss 2.5883): 41%|ββββ | 103/250 [02:39<03:30, 1.43s/it]
Training 1/1 epoch (loss 2.5883): 42%|βββββ | 104/250 [02:39<02:58, 1.22s/it]
Training 1/1 epoch (loss 2.6672): 42%|βββββ | 104/250 [02:41<02:58, 1.22s/it]
Training 1/1 epoch (loss 2.6672): 42%|βββββ | 105/250 [02:41<03:08, 1.30s/it]
Training 1/1 epoch (loss 2.3932): 42%|βββββ | 105/250 [02:42<03:08, 1.30s/it]
Training 1/1 epoch (loss 2.3932): 42%|βββββ | 106/250 [02:42<03:22, 1.41s/it]
Training 1/1 epoch (loss 2.7639): 42%|βββββ | 106/250 [02:44<03:22, 1.41s/it]
Training 1/1 epoch (loss 2.7639): 43%|βββββ | 107/250 [02:44<03:12, 1.35s/it]
Training 1/1 epoch (loss 2.8398): 43%|βββββ | 107/250 [02:46<03:12, 1.35s/it]
Training 1/1 epoch (loss 2.8398): 43%|βββββ | 108/250 [02:46<03:57, 1.67s/it]
Training 1/1 epoch (loss 2.5219): 43%|βββββ | 108/250 [02:47<03:57, 1.67s/it]
Training 1/1 epoch (loss 2.5219): 44%|βββββ | 109/250 [02:47<03:27, 1.47s/it]
Training 1/1 epoch (loss 2.6259): 44%|βββββ | 109/250 [02:49<03:27, 1.47s/it]
Training 1/1 epoch (loss 2.6259): 44%|βββββ | 110/250 [02:49<03:27, 1.48s/it]
Training 1/1 epoch (loss 2.6142): 44%|βββββ | 110/250 [02:50<03:27, 1.48s/it]
Training 1/1 epoch (loss 2.6142): 44%|βββββ | 111/250 [02:50<03:22, 1.46s/it]
Training 1/1 epoch (loss 2.6940): 44%|βββββ | 111/250 [02:51<03:22, 1.46s/it]
Training 1/1 epoch (loss 2.6940): 45%|βββββ | 112/250 [02:51<03:06, 1.35s/it]
Training 1/1 epoch (loss 2.7182): 45%|βββββ | 112/250 [02:53<03:06, 1.35s/it]
Training 1/1 epoch (loss 2.7182): 45%|βββββ | 113/250 [02:53<03:32, 1.55s/it]
Training 1/1 epoch (loss 2.7502): 45%|βββββ | 113/250 [02:54<03:32, 1.55s/it]
Training 1/1 epoch (loss 2.7502): 46%|βββββ | 114/250 [02:54<03:22, 1.49s/it]
Training 1/1 epoch (loss 2.5138): 46%|βββββ | 114/250 [02:55<03:22, 1.49s/it]
Training 1/1 epoch (loss 2.5138): 46%|βββββ | 115/250 [02:55<02:51, 1.27s/it]
Training 1/1 epoch (loss 2.6587): 46%|βββββ | 115/250 [02:56<02:51, 1.27s/it]
Training 1/1 epoch (loss 2.6587): 46%|βββββ | 116/250 [02:56<02:37, 1.17s/it]
Training 1/1 epoch (loss 2.7467): 46%|βββββ | 116/250 [02:58<02:37, 1.17s/it]
Training 1/1 epoch (loss 2.7467): 47%|βββββ | 117/250 [02:58<02:59, 1.35s/it]
Training 1/1 epoch (loss 2.5780): 47%|βββββ | 117/250 [02:59<02:59, 1.35s/it]
Training 1/1 epoch (loss 2.5780): 47%|βββββ | 118/250 [02:59<02:46, 1.26s/it]
Training 1/1 epoch (loss 2.6243): 47%|βββββ | 118/250 [03:00<02:46, 1.26s/it]
Training 1/1 epoch (loss 2.6243): 48%|βββββ | 119/250 [03:00<02:44, 1.26s/it]
Training 1/1 epoch (loss 2.6538): 48%|βββββ | 119/250 [03:02<02:44, 1.26s/it]
Training 1/1 epoch (loss 2.6538): 48%|βββββ | 120/250 [03:02<02:52, 1.32s/it]
Training 1/1 epoch (loss 2.6851): 48%|βββββ | 120/250 [03:02<02:52, 1.32s/it]
Training 1/1 epoch (loss 2.6851): 48%|βββββ | 121/250 [03:02<02:23, 1.11s/it]
Training 1/1 epoch (loss 2.5232): 48%|βββββ | 121/250 [03:05<02:23, 1.11s/it]
Training 1/1 epoch (loss 2.5232): 49%|βββββ | 122/250 [03:05<03:16, 1.54s/it]
Training 1/1 epoch (loss 2.6482): 49%|βββββ | 122/250 [03:07<03:16, 1.54s/it]
Training 1/1 epoch (loss 2.6482): 49%|βββββ | 123/250 [03:07<03:30, 1.66s/it]
Training 1/1 epoch (loss 2.8015): 49%|βββββ | 123/250 [03:08<03:30, 1.66s/it]
Training 1/1 epoch (loss 2.8015): 50%|βββββ | 124/250 [03:08<02:57, 1.41s/it]
Training 1/1 epoch (loss 2.6198): 50%|βββββ | 124/250 [03:09<02:57, 1.41s/it]
Training 1/1 epoch (loss 2.6198): 50%|βββββ | 125/250 [03:09<03:04, 1.48s/it]
Training 1/1 epoch (loss 2.8458): 50%|βββββ | 125/250 [03:10<03:04, 1.48s/it]
Training 1/1 epoch (loss 2.8458): 50%|βββββ | 126/250 [03:10<02:52, 1.39s/it]
Training 1/1 epoch (loss 2.6909): 50%|βββββ | 126/250 [03:11<02:52, 1.39s/it]
Training 1/1 epoch (loss 2.6909): 51%|βββββ | 127/250 [03:11<02:20, 1.14s/it]
Training 1/1 epoch (loss 2.8170): 51%|βββββ | 127/250 [03:14<02:20, 1.14s/it]
Training 1/1 epoch (loss 2.8170): 51%|βββββ | 128/250 [03:14<03:14, 1.60s/it]
Training 1/1 epoch (loss 2.5635): 51%|βββββ | 128/250 [03:15<03:14, 1.60s/it]
Training 1/1 epoch (loss 2.5635): 52%|ββββββ | 129/250 [03:15<03:04, 1.52s/it]
Training 1/1 epoch (loss 2.5446): 52%|ββββββ | 129/250 [03:17<03:04, 1.52s/it]
Training 1/1 epoch (loss 2.5446): 52%|ββββββ | 130/250 [03:17<03:08, 1.57s/it]
Training 1/1 epoch (loss 2.6392): 52%|ββββββ | 130/250 [03:19<03:08, 1.57s/it]
Training 1/1 epoch (loss 2.6392): 52%|ββββββ | 131/250 [03:19<03:21, 1.69s/it]
Training 1/1 epoch (loss 2.4825): 52%|ββββββ | 131/250 [03:19<03:21, 1.69s/it]
Training 1/1 epoch (loss 2.4825): 53%|ββββββ | 132/250 [03:19<02:38, 1.34s/it]
Training 1/1 epoch (loss 2.6058): 53%|ββββββ | 132/250 [03:21<02:38, 1.34s/it]
Training 1/1 epoch (loss 2.6058): 53%|ββββββ | 133/250 [03:21<03:04, 1.58s/it]
Training 1/1 epoch (loss 2.7032): 53%|ββββββ | 133/250 [03:22<03:04, 1.58s/it]
Training 1/1 epoch (loss 2.7032): 54%|ββββββ | 134/250 [03:22<02:45, 1.43s/it]
Training 1/1 epoch (loss 2.7110): 54%|ββββββ | 134/250 [03:23<02:45, 1.43s/it]
Training 1/1 epoch (loss 2.7110): 54%|ββββββ | 135/250 [03:23<02:11, 1.15s/it]
Training 1/1 epoch (loss 2.9120): 54%|ββββββ | 135/250 [03:25<02:11, 1.15s/it]
Training 1/1 epoch (loss 2.9120): 54%|ββββββ | 136/250 [03:25<03:00, 1.58s/it]
Training 1/1 epoch (loss 2.6818): 54%|ββββββ | 136/250 [03:27<03:00, 1.58s/it]
Training 1/1 epoch (loss 2.6818): 55%|ββββββ | 137/250 [03:27<03:00, 1.60s/it]
Training 1/1 epoch (loss 2.6602): 55%|ββββββ | 137/250 [03:28<03:00, 1.60s/it]
Training 1/1 epoch (loss 2.6602): 55%|ββββββ | 138/250 [03:28<02:32, 1.36s/it]
Training 1/1 epoch (loss 2.5539): 55%|ββββββ | 138/250 [03:29<02:32, 1.36s/it]
Training 1/1 epoch (loss 2.5539): 56%|ββββββ | 139/250 [03:29<02:36, 1.41s/it]
Training 1/1 epoch (loss 2.9435): 56%|ββββββ | 139/250 [03:31<02:36, 1.41s/it]
Training 1/1 epoch (loss 2.9435): 56%|ββββββ | 140/250 [03:31<02:48, 1.53s/it]
Training 1/1 epoch (loss 2.4668): 56%|ββββββ | 140/250 [03:32<02:48, 1.53s/it]
Training 1/1 epoch (loss 2.4668): 56%|ββββββ | 141/250 [03:32<02:17, 1.26s/it]
Training 1/1 epoch (loss 2.6992): 56%|ββββββ | 141/250 [03:34<02:17, 1.26s/it]
Training 1/1 epoch (loss 2.6992): 57%|ββββββ | 142/250 [03:34<02:37, 1.46s/it]
Training 1/1 epoch (loss 2.6744): 57%|ββββββ | 142/250 [03:36<02:37, 1.46s/it]
Training 1/1 epoch (loss 2.6744): 57%|ββββββ | 143/250 [03:36<02:59, 1.68s/it]
Training 1/1 epoch (loss 2.6328): 57%|ββββββ | 143/250 [03:37<02:59, 1.68s/it]
Training 1/1 epoch (loss 2.6328): 58%|ββββββ | 144/250 [03:37<02:25, 1.38s/it]
Training 1/1 epoch (loss 2.5458): 58%|ββββββ | 144/250 [03:39<02:25, 1.38s/it]
Training 1/1 epoch (loss 2.5458): 58%|ββββββ | 145/250 [03:39<02:58, 1.70s/it]
Training 1/1 epoch (loss 2.6742): 58%|ββββββ | 145/250 [03:41<02:58, 1.70s/it]
Training 1/1 epoch (loss 2.6742): 58%|ββββββ | 146/250 [03:41<02:49, 1.63s/it]
Training 1/1 epoch (loss 2.7110): 58%|ββββββ | 146/250 [03:41<02:49, 1.63s/it]
Training 1/1 epoch (loss 2.7110): 59%|ββββββ | 147/250 [03:41<02:27, 1.43s/it]
Training 1/1 epoch (loss 2.7734): 59%|ββββββ | 147/250 [03:43<02:27, 1.43s/it]
Training 1/1 epoch (loss 2.7734): 59%|ββββββ | 148/250 [03:43<02:41, 1.59s/it]
Training 1/1 epoch (loss 2.7170): 59%|ββββββ | 148/250 [03:45<02:41, 1.59s/it]
Training 1/1 epoch (loss 2.7170): 60%|ββββββ | 149/250 [03:45<02:37, 1.56s/it]
Training 1/1 epoch (loss 2.7399): 60%|ββββββ | 149/250 [03:46<02:37, 1.56s/it]
Training 1/1 epoch (loss 2.7399): 60%|ββββββ | 150/250 [03:46<02:24, 1.45s/it]
Training 1/1 epoch (loss 2.4527): 60%|ββββββ | 150/250 [03:49<02:24, 1.45s/it]
Training 1/1 epoch (loss 2.4527): 60%|ββββββ | 151/250 [03:49<02:55, 1.77s/it]
Training 1/1 epoch (loss 2.7273): 60%|ββββββ | 151/250 [03:50<02:55, 1.77s/it]
Training 1/1 epoch (loss 2.7273): 61%|ββββββ | 152/250 [03:50<02:47, 1.70s/it]
Training 1/1 epoch (loss 2.5126): 61%|ββββββ | 152/250 [03:51<02:47, 1.70s/it]
Training 1/1 epoch (loss 2.5126): 61%|ββββββ | 153/250 [03:51<02:32, 1.57s/it]
Training 1/1 epoch (loss 2.6346): 61%|ββββββ | 153/250 [03:54<02:32, 1.57s/it]
Training 1/1 epoch (loss 2.6346): 62%|βββββββ | 154/250 [03:54<02:54, 1.81s/it]
Training 1/1 epoch (loss 2.7382): 62%|βββββββ | 154/250 [03:54<02:54, 1.81s/it]
Training 1/1 epoch (loss 2.7382): 62%|βββββββ | 155/250 [03:54<02:16, 1.44s/it]
Training 1/1 epoch (loss 2.7744): 62%|βββββββ | 155/250 [03:56<02:16, 1.44s/it]
Training 1/1 epoch (loss 2.7744): 62%|βββββββ | 156/250 [03:56<02:15, 1.44s/it]
Training 1/1 epoch (loss 2.5662): 62%|βββββββ | 156/250 [03:58<02:15, 1.44s/it]
Training 1/1 epoch (loss 2.5662): 63%|βββββββ | 157/250 [03:58<02:41, 1.74s/it]
Training 1/1 epoch (loss 2.5666): 63%|βββββββ | 157/250 [03:59<02:41, 1.74s/it]
Training 1/1 epoch (loss 2.5666): 63%|βββββββ | 158/250 [03:59<02:03, 1.35s/it]
Training 1/1 epoch (loss 2.5839): 63%|βββββββ | 158/250 [04:00<02:03, 1.35s/it]
Training 1/1 epoch (loss 2.5839): 64%|βββββββ | 159/250 [04:00<01:47, 1.18s/it]
Training 1/1 epoch (loss 2.4570): 64%|βββββββ | 159/250 [04:01<01:47, 1.18s/it]
Training 1/1 epoch (loss 2.4570): 64%|βββββββ | 160/250 [04:01<01:58, 1.32s/it]
Training 1/1 epoch (loss 2.6584): 64%|βββββββ | 160/250 [04:02<01:58, 1.32s/it]
Training 1/1 epoch (loss 2.6584): 64%|βββββββ | 161/250 [04:02<01:42, 1.15s/it]
Training 1/1 epoch (loss 2.5171): 64%|βββββββ | 161/250 [04:04<01:42, 1.15s/it]
Training 1/1 epoch (loss 2.5171): 65%|βββββββ | 162/250 [04:04<02:00, 1.37s/it]
Training 1/1 epoch (loss 2.5289): 65%|βββββββ | 162/250 [04:05<02:00, 1.37s/it]
Training 1/1 epoch (loss 2.5289): 65%|βββββββ | 163/250 [04:05<02:06, 1.45s/it]
Training 1/1 epoch (loss 2.5706): 65%|βββββββ | 163/250 [04:06<02:06, 1.45s/it]
Training 1/1 epoch (loss 2.5706): 66%|βββββββ | 164/250 [04:06<01:44, 1.22s/it]
Training 1/1 epoch (loss 2.4507): 66%|βββββββ | 164/250 [04:08<01:44, 1.22s/it]
Training 1/1 epoch (loss 2.4507): 66%|βββββββ | 165/250 [04:08<02:03, 1.45s/it]
Training 1/1 epoch (loss 2.3329): 66%|βββββββ | 165/250 [04:09<02:03, 1.45s/it]
Training 1/1 epoch (loss 2.3329): 66%|βββββββ | 166/250 [04:09<01:54, 1.36s/it]
Training 1/1 epoch (loss 2.7144): 66%|βββββββ | 166/250 [04:11<01:54, 1.36s/it]
Training 1/1 epoch (loss 2.7144): 67%|βββββββ | 167/250 [04:11<01:50, 1.33s/it]
Training 1/1 epoch (loss 2.8237): 67%|βββββββ | 167/250 [04:13<01:50, 1.33s/it]
Training 1/1 epoch (loss 2.8237): 67%|βββββββ | 168/250 [04:13<02:21, 1.73s/it]
Training 1/1 epoch (loss 2.6067): 67%|βββββββ | 168/250 [04:14<02:21, 1.73s/it]
Training 1/1 epoch (loss 2.6067): 68%|βββββββ | 169/250 [04:14<02:00, 1.49s/it]
Training 1/1 epoch (loss 2.7057): 68%|βββββββ | 169/250 [04:16<02:00, 1.49s/it]
Training 1/1 epoch (loss 2.7057): 68%|βββββββ | 170/250 [04:16<02:07, 1.60s/it]
Training 1/1 epoch (loss 2.6938): 68%|βββββββ | 170/250 [04:18<02:07, 1.60s/it]
Training 1/1 epoch (loss 2.6938): 68%|βββββββ | 171/250 [04:18<02:18, 1.76s/it]
Training 1/1 epoch (loss 2.4950): 68%|βββββββ | 171/250 [04:19<02:18, 1.76s/it]
Training 1/1 epoch (loss 2.4950): 69%|βββββββ | 172/250 [04:19<01:58, 1.52s/it]
Training 1/1 epoch (loss 2.5798): 69%|βββββββ | 172/250 [04:21<01:58, 1.52s/it]
Training 1/1 epoch (loss 2.5798): 69%|βββββββ | 173/250 [04:21<01:56, 1.51s/it]
Training 1/1 epoch (loss 2.7050): 69%|βββββββ | 173/250 [04:22<01:56, 1.51s/it]
Training 1/1 epoch (loss 2.7050): 70%|βββββββ | 174/250 [04:22<02:03, 1.63s/it]
Training 1/1 epoch (loss 2.7121): 70%|βββββββ | 174/250 [04:23<02:03, 1.63s/it]
Training 1/1 epoch (loss 2.7121): 70%|βββββββ | 175/250 [04:23<01:46, 1.42s/it]
Training 1/1 epoch (loss 2.6983): 70%|βββββββ | 175/250 [04:25<01:46, 1.42s/it]
Training 1/1 epoch (loss 2.6983): 70%|βββββββ | 176/250 [04:25<01:42, 1.38s/it]
Training 1/1 epoch (loss 2.7167): 70%|βββββββ | 176/250 [04:26<01:42, 1.38s/it]
Training 1/1 epoch (loss 2.7167): 71%|βββββββ | 177/250 [04:26<01:28, 1.22s/it]
Training 1/1 epoch (loss 2.8293): 71%|βββββββ | 177/250 [04:27<01:28, 1.22s/it]
Training 1/1 epoch (loss 2.8293): 71%|βββββββ | 178/250 [04:27<01:25, 1.19s/it]
Training 1/1 epoch (loss 2.7642): 71%|βββββββ | 178/250 [04:28<01:25, 1.19s/it]
Training 1/1 epoch (loss 2.7642): 72%|ββββββββ | 179/250 [04:28<01:38, 1.39s/it]
Training 1/1 epoch (loss 2.4809): 72%|ββββββββ | 179/250 [04:29<01:38, 1.39s/it]
Training 1/1 epoch (loss 2.4809): 72%|ββββββββ | 180/250 [04:29<01:25, 1.22s/it]
Training 1/1 epoch (loss 2.5976): 72%|ββββββββ | 180/250 [04:31<01:25, 1.22s/it]
Training 1/1 epoch (loss 2.5976): 72%|ββββββββ | 181/250 [04:31<01:30, 1.31s/it]
Training 1/1 epoch (loss 2.6281): 72%|ββββββββ | 181/250 [04:33<01:30, 1.31s/it]
Training 1/1 epoch (loss 2.6281): 73%|ββββββββ | 182/250 [04:33<01:51, 1.64s/it]
Training 1/1 epoch (loss 2.4068): 73%|ββββββββ | 182/250 [04:34<01:51, 1.64s/it]
Training 1/1 epoch (loss 2.4068): 73%|ββββββββ | 183/250 [04:34<01:35, 1.43s/it]
Training 1/1 epoch (loss 2.6279): 73%|ββββββββ | 183/250 [04:36<01:35, 1.43s/it]
Training 1/1 epoch (loss 2.6279): 74%|ββββββββ | 184/250 [04:36<01:44, 1.59s/it]
Training 1/1 epoch (loss 2.7392): 74%|ββββββββ | 184/250 [04:38<01:44, 1.59s/it]
Training 1/1 epoch (loss 2.7392): 74%|ββββββββ | 185/250 [04:38<01:44, 1.62s/it]
Training 1/1 epoch (loss 2.7170): 74%|ββββββββ | 185/250 [04:39<01:44, 1.62s/it]
Training 1/1 epoch (loss 2.7170): 74%|ββββββββ | 186/250 [04:39<01:30, 1.42s/it]
Training 1/1 epoch (loss 2.6814): 74%|ββββββββ | 186/250 [04:41<01:30, 1.42s/it]
Training 1/1 epoch (loss 2.6814): 75%|ββββββββ | 187/250 [04:41<01:45, 1.67s/it]
Training 1/1 epoch (loss 2.8024): 75%|ββββββββ | 187/250 [04:42<01:45, 1.67s/it]
Training 1/1 epoch (loss 2.8024): 75%|ββββββββ | 188/250 [04:42<01:25, 1.38s/it]
Training 1/1 epoch (loss 2.4917): 75%|ββββββββ | 188/250 [04:44<01:25, 1.38s/it]
Training 1/1 epoch (loss 2.4917): 76%|ββββββββ | 189/250 [04:44<01:38, 1.62s/it]
Training 1/1 epoch (loss 2.6299): 76%|ββββββββ | 189/250 [04:46<01:38, 1.62s/it]
Training 1/1 epoch (loss 2.6299): 76%|ββββββββ | 190/250 [04:46<01:38, 1.65s/it]
Training 1/1 epoch (loss 2.7439): 76%|ββββββββ | 190/250 [04:46<01:38, 1.65s/it]
Training 1/1 epoch (loss 2.7439): 76%|ββββββββ | 191/250 [04:46<01:18, 1.33s/it]
Training 1/1 epoch (loss 2.6783): 76%|ββββββββ | 191/250 [04:48<01:18, 1.33s/it]
Training 1/1 epoch (loss 2.6783): 77%|ββββββββ | 192/250 [04:48<01:20, 1.39s/it]
Training 1/1 epoch (loss 2.4965): 77%|ββββββββ | 192/250 [04:49<01:20, 1.39s/it]
Training 1/1 epoch (loss 2.4965): 77%|ββββββββ | 193/250 [04:49<01:24, 1.48s/it]
Training 1/1 epoch (loss 2.5795): 77%|ββββββββ | 193/250 [04:50<01:24, 1.48s/it]
Training 1/1 epoch (loss 2.5795): 78%|ββββββββ | 194/250 [04:50<01:08, 1.21s/it]
Training 1/1 epoch (loss 2.6359): 78%|ββββββββ | 194/250 [04:52<01:08, 1.21s/it]
Training 1/1 epoch (loss 2.6359): 78%|ββββββββ | 195/250 [04:52<01:17, 1.41s/it]
Training 1/1 epoch (loss 2.5105): 78%|ββββββββ | 195/250 [04:54<01:17, 1.41s/it]
Training 1/1 epoch (loss 2.5105): 78%|ββββββββ | 196/250 [04:54<01:21, 1.52s/it]
Training 1/1 epoch (loss 2.5730): 78%|ββββββββ | 196/250 [04:54<01:21, 1.52s/it]
Training 1/1 epoch (loss 2.5730): 79%|ββββββββ | 197/250 [04:54<01:06, 1.26s/it]
Training 1/1 epoch (loss 2.5753): 79%|ββββββββ | 197/250 [04:56<01:06, 1.26s/it]
Training 1/1 epoch (loss 2.5753): 79%|ββββββββ | 198/250 [04:56<01:14, 1.43s/it]
Training 1/1 epoch (loss 2.5981): 79%|ββββββββ | 198/250 [04:58<01:14, 1.43s/it]
Training 1/1 epoch (loss 2.5981): 80%|ββββββββ | 199/250 [04:58<01:27, 1.71s/it]
Training 1/1 epoch (loss 2.5742): 80%|ββββββββ | 199/250 [04:59<01:27, 1.71s/it]
Training 1/1 epoch (loss 2.5742): 80%|ββββββββ | 200/250 [04:59<01:08, 1.36s/it]
Training 1/1 epoch (loss 2.4975): 80%|ββββββββ | 200/250 [05:01<01:08, 1.36s/it]
Training 1/1 epoch (loss 2.4975): 80%|ββββββββ | 201/250 [05:01<01:11, 1.45s/it]
Training 1/1 epoch (loss 2.6734): 80%|ββββββββ | 201/250 [05:02<01:11, 1.45s/it]
Training 1/1 epoch (loss 2.6734): 81%|ββββββββ | 202/250 [05:02<01:11, 1.49s/it]
Training 1/1 epoch (loss 2.5933): 81%|ββββββββ | 202/250 [05:03<01:11, 1.49s/it]
Training 1/1 epoch (loss 2.5933): 81%|ββββββββ | 203/250 [05:03<00:57, 1.23s/it]
Training 1/1 epoch (loss 2.6839): 81%|ββββββββ | 203/250 [05:05<00:57, 1.23s/it]
Training 1/1 epoch (loss 2.6839): 82%|βββββββββ | 204/250 [05:05<01:05, 1.43s/it]
Training 1/1 epoch (loss 2.5952): 82%|βββββββββ | 204/250 [05:06<01:05, 1.43s/it]
Training 1/1 epoch (loss 2.5952): 82%|βββββββββ | 205/250 [05:06<01:05, 1.46s/it]
Training 1/1 epoch (loss 2.8456): 82%|βββββββββ | 205/250 [05:07<01:05, 1.46s/it]
Training 1/1 epoch (loss 2.8456): 82%|βββββββββ | 206/250 [05:07<00:55, 1.26s/it]
Training 1/1 epoch (loss 2.7115): 82%|βββββββββ | 206/250 [05:09<00:55, 1.26s/it]
Training 1/1 epoch (loss 2.7115): 83%|βββββββββ | 207/250 [05:09<00:59, 1.39s/it]
Training 1/1 epoch (loss 2.5469): 83%|βββββββββ | 207/250 [05:11<00:59, 1.39s/it]
Training 1/1 epoch (loss 2.5469): 83%|βββββββββ | 208/250 [05:11<01:03, 1.52s/it]
Training 1/1 epoch (loss 2.6974): 83%|βββββββββ | 208/250 [05:11<01:03, 1.52s/it]
Training 1/1 epoch (loss 2.6974): 84%|βββββββββ | 209/250 [05:11<00:50, 1.24s/it]
Training 1/1 epoch (loss 2.5528): 84%|βββββββββ | 209/250 [05:14<00:50, 1.24s/it]
Training 1/1 epoch (loss 2.5528): 84%|βββββββββ | 210/250 [05:14<01:04, 1.60s/it]
Training 1/1 epoch (loss 2.6600): 84%|βββββββββ | 210/250 [05:15<01:04, 1.60s/it]
Training 1/1 epoch (loss 2.6600): 84%|βββββββββ | 211/250 [05:15<01:03, 1.64s/it]
Training 1/1 epoch (loss 2.7031): 84%|βββββββββ | 211/250 [05:16<01:03, 1.64s/it]
Training 1/1 epoch (loss 2.7031): 85%|βββββββββ | 212/250 [05:16<00:55, 1.47s/it]
Training 1/1 epoch (loss 2.6016): 85%|βββββββββ | 212/250 [05:18<00:55, 1.47s/it]
Training 1/1 epoch (loss 2.6016): 85%|βββββββββ | 213/250 [05:18<00:55, 1.49s/it]
Training 1/1 epoch (loss 2.7702): 85%|βββββββββ | 213/250 [05:19<00:55, 1.49s/it]
Training 1/1 epoch (loss 2.7702): 86%|βββββββββ | 214/250 [05:19<00:52, 1.45s/it]
Training 1/1 epoch (loss 2.7034): 86%|βββββββββ | 214/250 [05:21<00:52, 1.45s/it]
Training 1/1 epoch (loss 2.7034): 86%|βββββββββ | 215/250 [05:21<00:51, 1.46s/it]
Training 1/1 epoch (loss 2.6423): 86%|βββββββββ | 215/250 [05:23<00:51, 1.46s/it]
Training 1/1 epoch (loss 2.6423): 86%|βββββββββ | 216/250 [05:23<00:56, 1.67s/it]
Training 1/1 epoch (loss 2.7618): 86%|βββββββββ | 216/250 [05:24<00:56, 1.67s/it]
Training 1/1 epoch (loss 2.7618): 87%|βββββββββ | 217/250 [05:24<00:50, 1.52s/it]
Training 1/1 epoch (loss 2.6611): 87%|βββββββββ | 217/250 [05:26<00:50, 1.52s/it]
Training 1/1 epoch (loss 2.6611): 87%|βββββββββ | 218/250 [05:26<00:48, 1.51s/it]
Training 1/1 epoch (loss 2.8014): 87%|βββββββββ | 218/250 [05:27<00:48, 1.51s/it]
Training 1/1 epoch (loss 2.8014): 88%|βββββββββ | 219/250 [05:27<00:46, 1.50s/it]
Training 1/1 epoch (loss 2.8035): 88%|βββββββββ | 219/250 [05:28<00:46, 1.50s/it]
Training 1/1 epoch (loss 2.8035): 88%|βββββββββ | 220/250 [05:28<00:38, 1.27s/it]
Training 1/1 epoch (loss 2.6305): 88%|βββββββββ | 220/250 [05:29<00:38, 1.27s/it]
Training 1/1 epoch (loss 2.6305): 88%|βββββββββ | 221/250 [05:29<00:36, 1.27s/it]
Training 1/1 epoch (loss 2.5913): 88%|βββββββββ | 221/250 [05:31<00:36, 1.27s/it]
Training 1/1 epoch (loss 2.5913): 89%|βββββββββ | 222/250 [05:31<00:39, 1.43s/it]
Training 1/1 epoch (loss 2.5601): 89%|βββββββββ | 222/250 [05:32<00:39, 1.43s/it]
Training 1/1 epoch (loss 2.5601): 89%|βββββββββ | 223/250 [05:32<00:33, 1.25s/it]
Training 1/1 epoch (loss 2.6432): 89%|βββββββββ | 223/250 [05:34<00:33, 1.25s/it]
Training 1/1 epoch (loss 2.6432): 90%|βββββββββ | 224/250 [05:34<00:40, 1.56s/it]
Training 1/1 epoch (loss 2.6181): 90%|βββββββββ | 224/250 [05:35<00:40, 1.56s/it]
Training 1/1 epoch (loss 2.6181): 90%|βββββββββ | 225/250 [05:35<00:36, 1.44s/it]
Training 1/1 epoch (loss 2.5492): 90%|βββββββββ | 225/250 [05:37<00:36, 1.44s/it]
Training 1/1 epoch (loss 2.5492): 90%|βββββββββ | 226/250 [05:37<00:36, 1.52s/it]
Training 1/1 epoch (loss 2.2332): 90%|βββββββββ | 226/250 [05:38<00:36, 1.52s/it]
Training 1/1 epoch (loss 2.2332): 91%|βββββββββ | 227/250 [05:38<00:34, 1.52s/it]
Training 1/1 epoch (loss 2.7473): 91%|βββββββββ | 227/250 [05:39<00:34, 1.52s/it]
Training 1/1 epoch (loss 2.7473): 91%|βββββββββ | 228/250 [05:39<00:28, 1.28s/it]
Training 1/1 epoch (loss 2.4589): 91%|βββββββββ | 228/250 [05:41<00:28, 1.28s/it]
Training 1/1 epoch (loss 2.4589): 92%|ββββββββββ| 229/250 [05:41<00:30, 1.44s/it]
Training 1/1 epoch (loss 2.8040): 92%|ββββββββββ| 229/250 [05:43<00:30, 1.44s/it]
Training 1/1 epoch (loss 2.8040): 92%|ββββββββββ| 230/250 [05:43<00:30, 1.51s/it]
Training 1/1 epoch (loss 2.7780): 92%|ββββββββββ| 230/250 [05:43<00:30, 1.51s/it]
Training 1/1 epoch (loss 2.7780): 92%|ββββββββββ| 231/250 [05:43<00:23, 1.26s/it]
Training 1/1 epoch (loss 2.5530): 92%|ββββββββββ| 231/250 [05:45<00:23, 1.26s/it]
Training 1/1 epoch (loss 2.5530): 93%|ββββββββββ| 232/250 [05:45<00:25, 1.42s/it]
Training 1/1 epoch (loss 2.5512): 93%|ββββββββββ| 232/250 [05:47<00:25, 1.42s/it]
Training 1/1 epoch (loss 2.5512): 93%|ββββββββββ| 233/250 [05:47<00:28, 1.65s/it]
Training 1/1 epoch (loss 2.6546): 93%|ββββββββββ| 233/250 [05:48<00:28, 1.65s/it]
Training 1/1 epoch (loss 2.6546): 94%|ββββββββββ| 234/250 [05:48<00:20, 1.29s/it]
Training 1/1 epoch (loss 2.4297): 94%|ββββββββββ| 234/250 [05:49<00:20, 1.29s/it]
Training 1/1 epoch (loss 2.4297): 94%|ββββββββββ| 235/250 [05:49<00:21, 1.42s/it]
Training 1/1 epoch (loss 2.5177): 94%|ββββββββββ| 235/250 [05:51<00:21, 1.42s/it]
Training 1/1 epoch (loss 2.5177): 94%|ββββββββββ| 236/250 [05:51<00:21, 1.57s/it]
Training 1/1 epoch (loss 2.5544): 94%|ββββββββββ| 236/250 [05:52<00:21, 1.57s/it]
Training 1/1 epoch (loss 2.5544): 95%|ββββββββββ| 237/250 [05:52<00:16, 1.23s/it]
Training 1/1 epoch (loss 2.5321): 95%|ββββββββββ| 237/250 [05:54<00:16, 1.23s/it]
Training 1/1 epoch (loss 2.5321): 95%|ββββββββββ| 238/250 [05:54<00:17, 1.46s/it]
Training 1/1 epoch (loss 2.5561): 95%|ββββββββββ| 238/250 [05:56<00:17, 1.46s/it]
Training 1/1 epoch (loss 2.5561): 96%|ββββββββββ| 239/250 [05:56<00:17, 1.60s/it]
Training 1/1 epoch (loss 2.4013): 96%|ββββββββββ| 239/250 [05:57<00:17, 1.60s/it]
Training 1/1 epoch (loss 2.4013): 96%|ββββββββββ| 240/250 [05:57<00:15, 1.51s/it]
Training 1/1 epoch (loss 2.6069): 96%|ββββββββββ| 240/250 [05:59<00:15, 1.51s/it]
Training 1/1 epoch (loss 2.6069): 96%|ββββββββββ| 241/250 [05:59<00:14, 1.64s/it]
Training 1/1 epoch (loss 2.5219): 96%|ββββββββββ| 241/250 [06:00<00:14, 1.64s/it]
Training 1/1 epoch (loss 2.5219): 97%|ββββββββββ| 242/250 [06:00<00:11, 1.42s/it]
Training 1/1 epoch (loss 2.6846): 97%|ββββββββββ| 242/250 [06:01<00:11, 1.42s/it]
Training 1/1 epoch (loss 2.6846): 97%|ββββββββββ| 243/250 [06:01<00:08, 1.26s/it]
Training 1/1 epoch (loss 2.6150): 97%|ββββββββββ| 243/250 [06:02<00:08, 1.26s/it]
Training 1/1 epoch (loss 2.6150): 98%|ββββββββββ| 244/250 [06:02<00:07, 1.27s/it]
Training 1/1 epoch (loss 2.6570): 98%|ββββββββββ| 244/250 [06:03<00:07, 1.27s/it]
Training 1/1 epoch (loss 2.6570): 98%|ββββββββββ| 245/250 [06:03<00:06, 1.26s/it]
Training 1/1 epoch (loss 2.6773): 98%|ββββββββββ| 245/250 [06:05<00:06, 1.26s/it]
Training 1/1 epoch (loss 2.6773): 98%|ββββββββββ| 246/250 [06:05<00:05, 1.46s/it]
Training 1/1 epoch (loss 2.5825): 98%|ββββββββββ| 246/250 [06:07<00:05, 1.46s/it]
Training 1/1 epoch (loss 2.5825): 99%|ββββββββββ| 247/250 [06:07<00:04, 1.57s/it]
Training 1/1 epoch (loss 2.7051): 99%|ββββββββββ| 247/250 [06:08<00:04, 1.57s/it]
Training 1/1 epoch (loss 2.7051): 99%|ββββββββββ| 248/250 [06:08<00:02, 1.30s/it]
Training 1/1 epoch (loss 2.5178): 99%|ββββββββββ| 248/250 [06:10<00:02, 1.30s/it]
Training 1/1 epoch (loss 2.5178): 100%|ββββββββββ| 249/250 [06:10<00:01, 1.59s/it]
Training 1/1 epoch (loss 2.6620): 100%|ββββββββββ| 249/250 [06:12<00:01, 1.59s/it]
Training 1/1 epoch (loss 2.6620): 100%|ββββββββββ| 250/250 [06:12<00:00, 1.61s/it]
Training 1/1 epoch (loss 2.6620): 100%|ββββββββββ| 250/250 [06:12<00:00, 1.49s/it] |
| tokenizer config file saved in /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000-Q2-2000/tokenizer_config.json |
| Special tokens file saved in /aifs4su/hansirui_1st/jiayi/setting3-imdb/tinyllama-3T/tinyllama-3T-s3-Q1-1000-Q2-2000/special_tokens_map.json |
| wandb: ERROR Problem finishing run |
| Exception ignored in atexit callback: <bound method rank_zero_only.<locals>.wrapper of <safe_rlhf.logger.Logger object at 0x1550e4444290>> |
| Traceback (most recent call last): |
| File "/home/hansirui_1st/jiayi/resist/setting3/safe_rlhf/utils.py", line 212, in wrapper |
| return func(*args, **kwargs) |
| ^^^^^^^^^^^^^^^^^^^^^ |
| File "/home/hansirui_1st/jiayi/resist/setting3/safe_rlhf/logger.py", line 183, in close |
| self.wandb.finish() |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 406, in wrapper |
| return func(self, *args, **kwargs) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 503, in wrapper |
| return func(self, *args, **kwargs) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 451, in wrapper |
| return func(self, *args, **kwargs) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 2309, in finish |
| return self._finish(exit_code) |
| ^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 406, in wrapper |
| return func(self, *args, **kwargs) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 2337, in _finish |
| self._atexit_cleanup(exit_code=exit_code) |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 2550, in _atexit_cleanup |
| self._on_finish() |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 2806, in _on_finish |
| wait_with_progress( |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/wandb/sdk/mailbox/wait_with_progress.py", line 24, in wait_with_progress |
| return wait_all_with_progress( |
| ^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/wandb/sdk/mailbox/wait_with_progress.py", line 87, in wait_all_with_progress |
| return asyncio_compat.run(progress_loop_with_timeout) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/site-packages/wandb/sdk/lib/asyncio_compat.py", line 27, in run |
| future = executor.submit(runner.run, fn) |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |
| File "/aifs4su/hansirui_1st/miniconda3/envs/jy-resist/lib/python3.11/concurrent/futures/thread.py", line 169, in submit |
| raise RuntimeError( |
| RuntimeError: cannot schedule new futures after interpreter shutdown |
|
|