coniferlabs

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flan-ul2-alpaca-lora

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Hi there,

I'm also trying to finetune flan-ul2 (google/flan-ul2) using LoRA and I have few questions if you don't mind:

I'm trying to do this on a p3dn.24xlarge instance (8 GPUs with 32 GB gpu memory each). I follow this blog post (https://www.philschmid.de/fine-tune-flan-t5-peft) which is written for finetuning t5-xxl with lora. When I use more than one GPU, I'm getting this error:

RuntimeError: module must have its parameters and buffers on device cuda:0 (device_ids[0]) but found one of them on device: cuda:2

Therefore, I try to finetune flan-ul2 using only one of the 8 GPUs but it doesn't help me either because this time I got:
RuntimeError: No executable batch size found, reached zero.

which doesn't make sense at all because I didn't change anything related to the data processing.

So, my questions are:

1. Were you using both of the A100 GPUs for fine-tuning?
2. Did you encounter any of these errors when you're fine-tuning? If so, could you please share with me how did you fix them?

In case you're wondering what I've tried to do to fine-tune flan-ul2 using LoRA, I didn't change too many things on the blog post. All I did was to change the model name actually:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import DataCollatorForSeq2Seq

#model_id="google/flan-t5-xxl"
model_id="google/flan-ul2"
tokenizer = AutoTokenizer.from_pretrained(model_id)

#model_id = "philschmid/flan-t5-xxl-sharded-fp16"
model_id = "google/flan-ul2"
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, device_map="auto", load_in_8bit=True)

Then I run the trainer as shown below:

from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments

# Define training args
training_args = Seq2SeqTrainingArguments(
    output_dir=output_dir,
    auto_find_batch_size=True,
    learning_rate=1e-3, # higher learning rate
    num_train_epochs=5,
    logging_dir=f"{output_dir}/logs",
    logging_strategy="steps",
    logging_steps=500,
    save_strategy="no",
    report_to="tensorboard",
)

# Create Trainer instance
trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=tokenized_dataset["train"],
)