(base) root@ThinkStation-K-C2:/home/pypro/chatglm6b/ChatGLM-6B/ptuning# bash train.sh
2023-06-03 10:13:35.134319: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-06-03 10:13:35.207116: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
06/03/2023 10:13:36 - WARNING - main - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: False
06/03/2023 10:13:36 - INFO - main - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=False,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'fsdp_min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_max_length=None,
generation_num_beams=None,
gradient_accumulation_steps=16,
gradient_checkpointing=False,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=,
ignore_data_skip=False,
include_inputs_for_metrics=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=0.02,
length_column_name=length,
load_best_model_at_end=False,
local_rank=-1,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=cus/out/checkpoint20/runs/Jun03_10-13-36_ThinkStation-K-C2,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=10,
logging_strategy=steps,
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=100,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_hf,
optim_args=None,
output_dir=cus/out/checkpoint20,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=1,
per_device_train_batch_size=1,
predict_with_generate=True,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=,
ray_scope=last,
remove_unused_columns=True,
report_to=['tensorboard', 'wandb'],
resume_from_checkpoint=None,
run_name=cus/out/checkpoint20,
save_on_each_node=False,
save_steps=1000,
save_strategy=steps,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
sortish_sampler=False,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
xpu_backend=None,
)
Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-0713b568e641e634/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 4801.72it/s]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 877.74it/s]
Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-0713b568e641e634/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 405.27it/s]
[INFO|configuration_utils.py:666] 2023-06-03 10:13:37,854 >> loading configuration file /home/pypro/chatglm6b/ChatGLM-6B/THUDM/chatglm-6b/config.json
[WARNING|configuration_auto.py:905] 2023-06-03 10:13:37,854 >> Explicitly passing a revision is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision.
[INFO|configuration_utils.py:666] 2023-06-03 10:13:37,891 >> loading configuration file /home/pypro/chatglm6b/ChatGLM-6B/THUDM/chatglm-6b/config.json
[INFO|configuration_utils.py:720] 2023-06-03 10:13:37,892 >> Model config ChatGLMConfig {
"_name_or_path": "/home/pypro/chatglm6b/ChatGLM-6B/THUDM/chatglm-6b",
"architectures": [
"ChatGLMModel"
],
"auto_map": {
"AutoConfig": "configuration_chatglm.ChatGLMConfig",
"AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
"AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration"
},
"bos_token_id": 130004,
"eos_token_id": 130005,
"gmask_token_id": 130001,
"hidden_size": 4096,
"inner_hidden_size": 16384,
"layernorm_epsilon": 1e-05,
"mask_token_id": 130000,
"max_sequence_length": 2048,
"model_type": "chatglm",
"num_attention_heads": 32,
"num_layers": 28,
"pad_token_id": 3,
"position_encoding_2d": true,
"pre_seq_len": null,
"prefix_projection": false,
"quantization_bit": 0,
"torch_dtype": "float16",
"transformers_version": "4.27.1",
"use_cache": true,
"vocab_size": 130528
}
[WARNING|tokenization_auto.py:652] 2023-06-03 10:13:37,892 >> Explicitly passing a revision is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.
[INFO|tokenization_utils_base.py:1800] 2023-06-03 10:13:37,915 >> loading file ice_text.model
[INFO|tokenization_utils_base.py:1800] 2023-06-03 10:13:37,915 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:1800] 2023-06-03 10:13:37,915 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:1800] 2023-06-03 10:13:37,915 >> loading file tokenizer_config.json
[WARNING|auto_factory.py:456] 2023-06-03 10:13:38,022 >> Explicitly passing a revision is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision.
[INFO|modeling_utils.py:2400] 2023-06-03 10:13:38,061 >> loading weights file /home/pypro/chatglm6b/ChatGLM-6B/THUDM/chatglm-6b/pytorch_model.bin.index.json
[INFO|configuration_utils.py:575] 2023-06-03 10:13:38,061 >> Generate config GenerationConfig {
"_from_model_config": true,
"bos_token_id": 130004,
"eos_token_id": 130005,
"pad_token_id": 3,
"transformers_version": "4.27.1"
}
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:06<00:00, 1.16it/s]
[INFO|modeling_utils.py:3032] 2023-06-03 10:13:45,136 >> All model checkpoint weights were used when initializing ChatGLMForConditionalGeneration.
Is there an existing issue for this?
Current Behavior
train.sh 跑完了,没有看到报错,但是输出目录没有模型文件生成,试了好几次,都这样。
Expected Behavior
No response
Steps To Reproduce
(base) root@ThinkStation-K-C2:/home/pypro/chatglm6b/ChatGLM-6B/ptuning# bash train.sh 2023-06-03 10:13:35.134319: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX_VNNI FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-06-03 10:13:35.207116: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable,
ignore_data_skip=False,
include_inputs_for_metrics=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=0.02,
length_column_name=length,
load_best_model_at_end=False,
local_rank=-1,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=cus/out/checkpoint20/runs/Jun03_10-13-36_ThinkStation-K-C2,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=10,
logging_strategy=steps,
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=100,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_hf,
optim_args=None,
output_dir=cus/out/checkpoint20,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=1,
per_device_train_batch_size=1,
predict_with_generate=True,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=,
ray_scope=last,
remove_unused_columns=True,
report_to=['tensorboard', 'wandb'],
resume_from_checkpoint=None,
run_name=cus/out/checkpoint20,
save_on_each_node=False,
save_steps=1000,
save_strategy=steps,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
sortish_sampler=False,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
xpu_backend=None,
)
Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-0713b568e641e634/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 4801.72it/s]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 877.74it/s]
Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-0713b568e641e634/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 405.27it/s]
[INFO|configuration_utils.py:666] 2023-06-03 10:13:37,854 >> loading configuration file /home/pypro/chatglm6b/ChatGLM-6B/THUDM/chatglm-6b/config.json
[WARNING|configuration_auto.py:905] 2023-06-03 10:13:37,854 >> Explicitly passing a
TF_ENABLE_ONEDNN_OPTS=0
. 06/03/2023 10:13:36 - WARNING - main - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: False 06/03/2023 10:13:36 - INFO - main - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=False, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddp_bucket_cap_mb=None, ddp_find_unused_parameters=None, ddp_timeout=1800, debug=[], deepspeed=None, disable_tqdm=False, do_eval=False, do_predict=False, do_train=True, eval_accumulation_steps=None, eval_delay=0, eval_steps=None, evaluation_strategy=no, fp16=False, fp16_backend=auto, fp16_full_eval=False, fp16_opt_level=O1, fsdp=[], fsdp_config={'fsdp_min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, fsdp_min_num_params=0, fsdp_transformer_layer_cls_to_wrap=None, full_determinism=False, generation_max_length=None, generation_num_beams=None, gradient_accumulation_steps=16, gradient_checkpointing=False, greater_is_better=None, group_by_length=False, half_precision_backend=auto, hub_model_id=None, hub_private_repo=False, hub_strategy=every_save, hub_token=revision
is encouraged when loading a configuration with custom code to ensure no malicious code has been contributed in a newer revision. [INFO|configuration_utils.py:666] 2023-06-03 10:13:37,891 >> loading configuration file /home/pypro/chatglm6b/ChatGLM-6B/THUDM/chatglm-6b/config.json [INFO|configuration_utils.py:720] 2023-06-03 10:13:37,892 >> Model config ChatGLMConfig { "_name_or_path": "/home/pypro/chatglm6b/ChatGLM-6B/THUDM/chatglm-6b", "architectures": [ "ChatGLMModel" ], "auto_map": { "AutoConfig": "configuration_chatglm.ChatGLMConfig", "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration", "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration" }, "bos_token_id": 130004, "eos_token_id": 130005, "gmask_token_id": 130001, "hidden_size": 4096, "inner_hidden_size": 16384, "layernorm_epsilon": 1e-05, "mask_token_id": 130000, "max_sequence_length": 2048, "model_type": "chatglm", "num_attention_heads": 32, "num_layers": 28, "pad_token_id": 3, "position_encoding_2d": true, "pre_seq_len": null, "prefix_projection": false, "quantization_bit": 0, "torch_dtype": "float16", "transformers_version": "4.27.1", "use_cache": true, "vocab_size": 130528 }[WARNING|tokenization_auto.py:652] 2023-06-03 10:13:37,892 >> Explicitly passing a
revision
is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision. [INFO|tokenization_utils_base.py:1800] 2023-06-03 10:13:37,915 >> loading file ice_text.model [INFO|tokenization_utils_base.py:1800] 2023-06-03 10:13:37,915 >> loading file added_tokens.json [INFO|tokenization_utils_base.py:1800] 2023-06-03 10:13:37,915 >> loading file special_tokens_map.json [INFO|tokenization_utils_base.py:1800] 2023-06-03 10:13:37,915 >> loading file tokenizer_config.json [WARNING|auto_factory.py:456] 2023-06-03 10:13:38,022 >> Explicitly passing arevision
is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision. [INFO|modeling_utils.py:2400] 2023-06-03 10:13:38,061 >> loading weights file /home/pypro/chatglm6b/ChatGLM-6B/THUDM/chatglm-6b/pytorch_model.bin.index.json [INFO|configuration_utils.py:575] 2023-06-03 10:13:38,061 >> Generate config GenerationConfig { "_from_model_config": true, "bos_token_id": 130004, "eos_token_id": 130005, "pad_token_id": 3, "transformers_version": "4.27.1" }Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:06<00:00, 1.16it/s] [INFO|modeling_utils.py:3032] 2023-06-03 10:13:45,136 >> All model checkpoint weights were used when initializing ChatGLMForConditionalGeneration.
[WARNING|modeling_utils.py:3034] 2023-06-03 10:13:45,136 >> Some weights of ChatGLMForConditionalGeneration were not initialized from the model checkpoint at /home/pypro/chatglm6b/ChatGLM-6B/THUDM/chatglm-6b and are newly initialized: ['transformer.prefix_encoder.embedding.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. [INFO|modeling_utils.py:2690] 2023-06-03 10:13:45,159 >> Generation config file not found, using a generation config created from the model config. Quantized to 4 bit input_ids [53, 6945, 5, 8, 42, 4, 64286, 12, 65309, 64013, 63826, 86664, 64523, 69773, 4, 67342, 12, 130001, 130004, 5, 72176, 64095, 64685, 6, 86664, 71073, 66201, 63920, 63826, 85759, 6, 63856, 66461, 67464, 66201, 81363, 76247, 89580, 6, 71592, 58, 65925, 69367, 63825, 94584, 125779, 78495, 9, 18, 9, 9, 63830, 108750, 63842, 6, 64823, 63828, 66116, 67464, 66201, 6, 67494, 66116, 65320, 63826, 67464, 6, 69548, 109995, 6, 5, 86664, 64829, 63903, 64710, 67407, 6, 63829, 103615, 65250, 63825, 101004, 6, 64601, 68231, 58, 65309, 64013, 67407, 125779, 64057, 58, 63847, 63883, 65925, 71057, 95082, 117928, 63824, 63847, 63883, 68579, 71057, 95082, 64710, 64699, 67407, 6, 63847, 63883, 65925, 71057, 95082, 70040, 67407, 125779, 64601, 71073, 87853, 93893, 63826, 84772, 6, 66461, 67464, 74029, 82882, 72439, 64799, 66759, 130005, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 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inputs [Round 0] 问:孙文和孙中山那个伟大 答: 他们是同一个人,孙中山是一位革命家和政治家,他在中国民主革命的道路上起到了重要作用,被誉为“民族解放的先驱”, 他在1911年辛亥革命后,领导了中国的民主革命,实现了中国的统一和民主,建立了中华民国, 孙中山提出三民主义,是中国现代政治的理论基础, 他提出了“孙文主义”,即“以中国民族的利益为中心的民族主义、以中国民众的利益为中心的民权主义,以中国民族的利益为中心的民生主义”, 他是一位杰出的思想家和教育家,在中国民主教育的发展中做出了重要贡献 labelids [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 130004, 5, 72176, 64095, 64685, 6, 86664, 71073, 66201, 63920, 63826, 85759, 6, 63856, 66461, 67464, 66201, 81363, 76247, 89580, 6, 71592, 58, 65925, 69367, 63825, 94584, 125779, 78495, 9, 18, 9, 9, 63830, 108750, 63842, 6, 64823, 63828, 66116, 67464, 66201, 6, 67494, 66116, 65320, 63826, 67464, 6, 69548, 109995, 6, 5, 86664, 64829, 63903, 64710, 67407, 6, 63829, 103615, 65250, 63825, 101004, 6, 64601, 68231, 58, 65309, 64013, 67407, 125779, 64057, 58, 63847, 63883, 65925, 71057, 95082, 117928, 63824, 63847, 63883, 68579, 71057, 95082, 64710, 64699, 67407, 6, 63847, 63883, 65925, 71057, 95082, 70040, 67407, 125779, 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-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100] labels <image-100> 他们是同一个人,孙中山是一位革命家和政治家,他在中国民主革命的道路上起到了重要作用,被誉为“民族解放的先驱”, 他在1911年辛亥革命后,领导了中国的民主革命,实现了中国的统一和民主,建立了中华民国, 孙中山提出三民主义,是中国现代政治的理论基础, 他提出了“孙文主义”,即“以中国民族的利益为中心的民族主义、以中国民众的利益为中心的民权主义,以中国民族的利益为中心的民生主义”, 他是一位杰出的思想家和教育家,在中国民主教育的发展中做出了重要贡献