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[2023-12-19 17:47:31,804] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)
/root/miniconda3/envs/textgen/lib/python3.10/site-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.
  warnings.warn(
12/19/2023 17:47:36 - WARNING - llmtuner.model.parser - `ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.
/root/miniconda3/envs/textgen/lib/python3.10/site-packages/transformers/training_args.py:1751: FutureWarning: `--push_to_hub_token` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--hub_token` instead.
  warnings.warn(
12/19/2023 17:47:36 - INFO - llmtuner.model.parser - Process rank: 0, device: cuda:0, n_gpu: 2
  distributed training: True, compute dtype: torch.bfloat16
12/19/2023 17:47:36 - INFO - llmtuner.model.parser - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=2,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=True,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_persistent_workers=False,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=False,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=False,
do_predict=True,
do_train=False,
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={'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_config=None,
generation_max_length=None,
generation_num_beams=None,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_num_input_tokens_seen=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=5e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=./models/sft/phi-2-sft-alpaca_gpt4_en-1/Predict_20/runs/Dec19_17-47-36_autodl-container-f11a41911a-e496153c,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=500,
logging_strategy=steps,
lr_scheduler_kwargs={},
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_torch,
optim_args=None,
output_dir=./models/sft/phi-2-sft-alpaca_gpt4_en-1/Predict_20,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=1,
per_device_train_batch_size=8,
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=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=['tensorboard', 'wandb'],
resume_from_checkpoint=None,
run_name=./models/sft/phi-2-sft-alpaca_gpt4_en-1/Predict_20,
save_on_each_node=False,
save_only_model=False,
save_safetensors=True,
save_steps=500,
save_strategy=steps,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
sortish_sampler=False,
split_batches=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_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
)
12/19/2023 17:47:36 - INFO - llmtuner.data.loader - Loading dataset alpaca_gpt4_data_en.json...
[WARNING|logging.py:314] 2023-12-19 17:47:37,929 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.

Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]
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Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00,  2.56it/s]
12/19/2023 17:47:38 - INFO - llmtuner.model.adapter - Fine-tuning method: LoRA
12/19/2023 17:47:39 - INFO - llmtuner.model.adapter - Merged 1 adapter(s).
12/19/2023 17:47:39 - INFO - llmtuner.model.adapter - Loaded adapter(s): ./models/sft/phi-2-sft-alpaca_gpt4_en-1
12/19/2023 17:47:39 - INFO - llmtuner.model.loader - trainable params: 0 || all params: 2779683840 || trainable%: 0.0000
12/19/2023 17:47:39 - INFO - llmtuner.model.loader - This IS expected that the trainable params is 0 if you are using model for inference only.
12/19/2023 17:47:39 - INFO - llmtuner.data.template - Add pad token: <|endoftext|>
[WARNING|logging.py:314] 2023-12-19 17:47:40,715 >> You're using a CodeGenTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
/root/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '
input_ids:
[50256, 32, 8537, 1022, 257, 11040, 2836, 290, 281, 11666, 4430, 8796, 13, 383, 8796, 3607, 7613, 11, 6496, 11, 290, 23507, 7429, 284, 262, 2836, 338, 2683, 13, 198, 20490, 25, 13786, 1115, 9040, 329, 10589, 5448, 13, 198, 48902, 25]
inputs:
<|endoftext|>A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: Give three tips for staying healthy.
Assistant:

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100%|██████████| 10/10 [01:01<00:00,  7.51s/it]Building prefix dict from the default dictionary ...
Loading model from cache /tmp/jieba.cache
Loading model cost 0.578 seconds.
Prefix dict has been built successfully.

100%|██████████| 10/10 [01:02<00:00,  6.28s/it]
***** predict metrics *****
  predict_bleu-4             =    49.0534
  predict_rouge-1            =    54.9625
  predict_rouge-2            =    31.0959
  predict_rouge-l            =    39.8761
  predict_runtime            = 0:01:10.55
  predict_samples_per_second =      0.283
  predict_steps_per_second   =      0.142
12/19/2023 17:48:51 - INFO - llmtuner.train.sft.trainer - Saving prediction results to ./models/sft/phi-2-sft-alpaca_gpt4_en-1/Predict_20/generated_predictions.jsonl