CUDA OUT OF MEMORY on MULTI GPU
HiI want to Finetue mistral-7b using this code:from transformers import BitsAndBytesConfig
import torch

# specify how to quantize the model
quantization_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=torch.bfloat16,
)
device_map = {"": torch.cuda.current_device()} if torch.cuda.is_available() else None

model_kwargs = dict(
    attn_implementation="flash_attention_2", # set this to True if your GPU supports it (Flash Attention drastically speeds up model computations)
    torch_dtype="auto",
    use_cache=False, # set to False as we're going to use gradient checkpointing
    device_map=device_map,
    quantization_config=quantization_config,

from trl import SFTTrainer
from peft import LoraConfig
from transformers import TrainingArguments
max_seq_length = 3072
# path where the Trainer will save its checkpoints and logs
output_dir = 'data/zephyr-7b-sft-lora'

# based on config
training_args = TrainingArguments(
    fp16=True, # specify bf16=True instead when training on GPUs that support bf16
    do_eval=True,
    evaluation_strategy="epoch",
    gradient_accumulation_steps=64,
    gradient_checkpointing=True,
    gradient_checkpointing_kwargs={"use_reentrant": False},
    learning_rate=2.0e-05,
    log_level="info",
    logging_steps=5,
    logging_strategy="steps",
    lr_scheduler_type="cosine",
    max_steps=-1,
    num_train_epochs=1,
    output_dir=output_dir,
    overwrite_output_dir=True,
    per_device_eval_batch_size=1, # originally set to 8
    per_device_train_batch_size=1, # originally set to 8
    # push_to_hub=True,
    # hub_model_id="zephyr-7b-sft-lora",
    # hub_strategy="every_save",
    # report_to="tensorboard",
    save_strategy="no",
    save_total_limit=None,
    seed=42,
)

# based on config
peft_config = LoraConfig(
        r=64,
        lora_alpha=16,
        lora_dropout=0.1,
        bias="none",
        task_type="CAUSAL_LM",
        target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
)

trainer = SFTTrainer(
        model=model_id,
        model_init_kwargs=model_kwargs,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        dataset_text_field="text",
        tokenizer=tokenizer,
        packing=True,
        peft_config=peft_config,
        max_seq_length=max_seq_length,
    )
train_result = trainer.train()my problem is when I use a single GPU instance, it works well but when I use multi-GPU(4 GPUs) I face CUDA out of memory. As I watched the GPU log I saw that suddenly one of the GPU’s memory got full at the beginning of training while the other three did not!cuda799×747 69 KBI was wondering if anyone encountered this issue and if is there a way to fix this.Thanks