Model.generate() OOM on 1 of 2 GPUs?

I’m using megatron-11b model to generate sequences with prompts. It’s on a dgx with 8*A100 40GB cards.

Model is loaded onto CPU first then wrapped with accelerator.prepare(), resulting in two 12GB shards of the model on two GPUs.
dataset is loaded from local txt file. Very small, ~200 samples as a tester. Prompts are about 10~20 words long. DataLoader wrapped around dataset, called eval_loader is also passed into accelerator.prepare(). No other components, e.g. optimizer or any other datasets or loaders.

Loop over eval_loader and asking for model.generate() with size-1 batch. One of the two GPUs throws OOM and program terminates: say GPU0 and GPU1 are both around 12GB occupancy, and GPU1 memory surges to fully loaded and OOM.

code is as follow:

from glob import glob

import datasets
import torch
import transformers
from accelerate import Accelerator
from datasets import load_dataset
from megatron_11b import MegatronForCausalLM, MegatronTokenizer
# from megatron_11b.megatron_policy import MegatronPolicy
# from parallelformers import parallelize
from import DataLoader
from import tqdm

accelerator = Accelerator()

if accelerator.is_main_process:

data_files = {i: each for i, each in enumerate(glob('./prompt/inputs/*.txt'))}

dataset = load_dataset('text', data_files=data_files, split=2)
eval_loader = DataLoader(dataset)

tokenizer = MegatronTokenizer.from_pretrained("./megatron-11B")
model = MegatronForCausalLM.from_pretrained("./megatron-11B")

model, eval_loader = accelerator.prepare(model, eval_loader)

progress_bar = tqdm(range(len(eval_loader)), disable=not accelerator.is_main_process)

for each in eval_loader:
    encoded = tokenizer(each['text'], padding=True, return_tensors="pt")
    with torch.no_grad():
        result = model.generate(
            max_length=64, num_beams=5, no_repeat_ngram_size=2, repetition_penalty=1.2,

Does anyone have an idea why this happens? How to fix it?

I would suggest to use logging appropriately to see which example breaks the generation and why. The quick fix is probably to reduce num_beams to 4 or lower.

But how does it associate with only one of the two distributed GPUs?

Presumably the generation went of the rails/became to memory intensive for a specific example that got distributed to that gpu?

RuntimeError: CUDA out of memory. Tried to allocate 36.00 MiB (GPU 1; 39.59 GiB total capacity; 37.84 GiB already allocated; 22.19 MiB free; 37.84 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

even when batch size 1, num_beam=3