How to correctly use wandb hyperparameter tuning with Huggingface?

W&B Help
,
1

Hi everyone :wave:, I am using wandb with Huggingface in a AWS Sagemaker notebook and I am refering to the tutorial here: Define sweep configuration for hyperparameter tuning. and Hyperparameter Search using Trainer API.

My codes works well without hyperparameter search, but all runs failed after I enable hyperparameter search.

This is the error message from one of the failed runs:

Run 0ilv70r3 errored: ValueError("boxes1 must be in [x0, y0, x1, y1] (corner) format, but got tensor([[nan, nan, nan, nan],\n [nan, nan, nan, nan],\n [nan, nan, nan, nan],\n ...,\n [nan, nan, nan, nan],\n [nan, nan, nan, nan],\n [nan, nan, nan, nan]], device='cuda:0', dtype=torch.float16)") wandb: ERROR Run 0ilv70r3 errored: ValueError("boxes1 must be in [x0, y0, x1, y1] (corner) format, but got tensor([[nan, nan, nan, nan],\n [nan, nan, nan, nan],\n [nan, nan, nan, nan],\n ...,\n [nan, nan, nan, nan],\n [nan, nan, nan, nan],\n [nan, nan, nan, nan]], device='cuda:0', dtype=torch.float16)")

My model is an object detection model. It seems that the outputs do not fit. I wonder how can I solve this issue.

Here are some useful snippets of my code:

def wandb_hp_space(trial):
    return {
        "method": "bayes",
        "metric": {"name": "loss", "goal": "minimize"},
        "parameters": {
            "learning_rate": {"distribution": "log_uniform", "min": 1e-6, "max": 1e-4},
            "per_device_train_batch_size": {"values": [8, 16]},
        },
    }

    training_args = TrainingArguments(
        output_dir=args.output_dir,
        overwrite_output_dir=True,
        per_device_train_batch_size=args.per_device_train_batch_size,
        weight_decay=args.weight_decay,
        warmup_steps=args.warmup_steps,
        save_total_limit=args.save_total_limit,
        learning_rate=args.learning_rate,
        fp16=True,
        save_strategy="epoch",
        logging_strategy='epoch',
        remove_unused_columns=False,
        push_to_hub=True,
        hub_model_id=args.hub_model_id,
        hub_token=args.hub_token,
        hub_strategy="every_save",
        report_to="wandb",
    )

    def model_init(trial):
        return AutoModelForObjectDetection.from_pretrained(
            args.pretrained_model,
            id2label=CLASS_ID_TO_NAME,
            label2id=CLASS_NAME_TO_ID,
            ignore_mismatched_sizes=True,
        )

    trainer = Trainer(
        model=None,
        model_init=model_init,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=test_dataset,
        data_collator=collate_fn,
        tokenizer=image_processor,
    )

    trainer.hyperparameter_search(
        hp_space=wandb_hp_space,
        n_trials=5,
        direction="minimize",
        backend="wandb",
    )

I would greatly appreciate any guidance or advice on how to resolve this issue. Thank you very much in advance for your help! :pray: :pray:

2

Hi @oschan77, could you print all of the args that your script is receiving in your model_init? From what I can tell, the search space config looks good but I wanted to check that all the arg values are valid since this should be the only difference between a hyperparam search and a standard run.

Thank you,
Nate

3

Hi @nathank ! Thank you for your help! This is all the args that the script receive:

  parser.add_argument("--per_device_train_batch_size", type=int, default=4)
  parser.add_argument("--warmup_steps", type=int, default=100)
  parser.add_argument("--save_total_limit", type=int, default=2)
  parser.add_argument("--pretrained_model", type=str, default="facebook/detr-resnet-50")
  parser.add_argument("--learning_rate", type=float, default=1e-5)
  parser.add_argument("--weight_decay", type=float, default=1e-4)
  parser.add_argument("--image_resize_ratio", type=float, default=0.25)
  parser.add_argument("--hub_model_id", type=str, default=None)
  parser.add_argument("--hub_token", type=str, default=None)
  parser.add_argument("--wandb_token", type=str, default=None)
  parser.add_argument("--wandb_project_name", type=str, default="detr-algae-v0")
  parser.add_argument("--wandb_run_name", type=str, default=None)
  parser.add_argument("--output_dir", type=str, default=os.environ["SM_MODEL_DIR"])
  parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])
  parser.add_argument("--training_dir", type=str, default=os.environ["SM_CHANNEL_TRAIN"])
  parser.add_argument("--test_dir", type=str, default=os.environ["SM_CHANNEL_TEST"])
4

Hi @nathank , any ideas? I am still facing the same issue. Thanks!

5

Hi @oschan77, can you print out the shape and values of your boxes1 during your training to confirm these are Nan’s?

Also, it’s possible that some of the parameters being suggested by the sweep are not within bounds that work for your model. Could you share your sweep config?

6

Hi @oschan77, I wanted to follow up and see if this is still an issue?

7

This topic was automatically closed 60 days after the last reply. New replies are no longer allowed.